AI is revolutionizing the world. Face recognition is one such spectrum of it. Almost most of us use face recognition systems. They are everywhere. One can find them in devices like our mobile or platforms like Facebook or applications like Photo gallery apps or advanced security cameras.
In this blog, we are going to have our hands dirty with facial recognition in python and learn how can we train a model to learn faces from images! Before we start with the implementation, let us dive down a little into basics of face recognition theory
What is Face Recognition?
The issue is answered by a face identification scheme: does an image’s face match the image’s face? A face recognition scheme requires a face picture and predicts if the face corresponds to other pictures in the database supplied. Face-recognition schemes have been developed to compare and forecast possible face match irrespective of speech, face hair, and age.
Facial recognition is the process of identifying or verifying the identity of a person using their face. It captures, analyzes and compares patterns based on the person’s facial details.
The face detection process detects and points out human faces in images.
The face capture process transforms camera feed (a face) into a set of mathematical representation based on the person’s facial features.
The face match process verifies if two faces are of the same person.
Today it’s considered to be the most natural of all biometric measurements.
What are the Steps in Face Recognition?
Step 1: Detecting the Faces
Face detection is the first phase in our pipeline. We must put the images in a picture before trying to divide them. Methods such as HOG can be used to define the images in a specified picture. Histograph of Oriented Gradients The distribution (histogram) of gradient instructions is used as characteristics in the HOG function descriptor. Gradients (X and Y derivatives) are helpful in an image because the size of the gradient is wide around edges and angles, and we know that edges and corners are more informed about the shape of an object than flat regions. HOG is more like a manner to detect a picture of the picture, by identifying the corners by the comparison of the various sections of the picture
Step 2: Face Landmark Estimation
Moreover, we have to cope with issues such as faces in various directions. Such images look completely different from a computer and the similarity between them on their own can not be found. We can use an algorithm known as face-point assessment to do this. Vahid Kazemi and Josephine Sullivan have created an strategy in 2014. The fundamental concept is that we will have 68 particular points on every face (called sights). Once we understand where there are distinct face characteristics, we can scale the picture for a single person, spin it and shear it.
Step 3: Face Encoding
We need a way to obtain a few fundamental readings from each face at this point. Then we could evaluate the unfamiliar face in the same manner and discover the most close-known face. This can be done with profound teaching (CNNs). Incorporation of characteristics from prior measures must be created. We can once recognize this embedding for an unidentified face.
Step 4: Classifying Unknown Faces into Known Ones
In fact, this is a simpler phase. All we have to do is discover the individual who has the nearest measurement to our sample picture in our database of recognized individuals. We can do this using an algorithm for fundamental teaching machines. All we have to do is train a classifier to measure from a fresh sample picture and show which recognized individual is nearest to each other. It requires milliseconds to run this classifier. The classificator outcome is the person’s name!
Transfer Learning for Face Recognition
Transfer training is a computer training process in which a model created for a job is used again as the basis for a second job model. It is an approach popular in the field of in-depth learning, where prequalified models are used to start computer vision and natural language treatment work, given the huge computer and time resources required to develop neural network models on these problems. We use transfer learning in our blog as well. For face detection and recognition, we use pre-built designs. Training a face recognition model is a very costly job. You need a bunch of information and computing energy to train profound facial recognition teaching models.
For our assignment, we will currently use python’s facial recognition library. The book uses the profound teaching model educated by a threefold loss function. The Siamese network we call. “Siamese” implies linked or attached. Perhaps you heard of Siamese twins? Siamese networks may be formed by convolutionary structures and dense or layers of LSTM. We will use the Convolutionary Siamese Network since we will cope with pictures to identify the faces. You can understand the architecture by this image :
This is the fundamental algorithm:
we take two photographs (Figures 1 and 2). The last layer of the CNN generates a permanent shape matrix (picture embedding), the last part of which is the CNN. We get two embeddings as two pictures are feed. (h2 and h1). (h1).
The absolute range is calculated between the vectors.
Then a sigmoid function passes through measurements and the resemblance value is generated.
The scores are nearer to 1 if the pictures are comparable or nearer to 0.
Getting the libraries
The first step is to load all the libraries. We will be using the face_recognition library for detection and recognition in this case. This library provides out of the box methods to perform various tasks involved during a facial recognition process.
## Importing the libraries
from imutils import paths
import matplotlib.pyplot asplt
import numpy asnp
Generating the Encodings for the Knows Users
In this section, we are trying to convert images of the known users into a mathematical representation. This mathematical representation is a high dimensional vector. We can call this high dimensional vector as an embedding. Each image has it’s own 1 embedding. These embeddings are important to describe an image in a high dimensional space.
The code below tries to identify a face in a given image. Once the model detects the face, it extracts out facial features and passes them to another model which converts these features into a mathematical representation known as embeddings. In the end, we collate all the images and their corresponding embedding in a list.
This is a set of true values for us. All the users present in this list are the ones which we want to recognize correctly. Any user out of this set should be called out as an “unknown” by the model!
# find the indexes of all matched faces then initialize a
# dictionary to count the total number of times each face
# was matched
# loop over the matched indexes and maintain a count for
# each recognized face face
# determine the recognized face with the largest number of
# votes (note: in the event of an unlikely tie Python will
# select first entry in the dictionary)
Getting the Predictions
The previous utility function takes one image as input. Below code, basically iterates over multiple test images present in a folder. It passes it to the predict function and collects the predicted name. All the results are stored in a data frame!
# Sensitivity, hit rate, recall, or true positive rate
# Specificity or true negative rate
# Precision or positive predictive value
# Negative predictive value
# Fall out or false positive rate
# False negative rate
# False discovery rate
# Overall accuracy
Security is now one of the areas that most use face recognition. Facial recognition is a very efficient instrument which enforcers can use the technology to identify criminals and software businesses to assist consumers to access the technology. It is possible to further develop this technology to be used in other ways, like ATMs, private records or other delicate equipment. This may outdated other safety steps, including passwords and buttons.
In the subways and in the other rail networks, innovators also seek to introduce facial identification. You want to use this technology to pay for your transport charge, using faces as credit cards. The facial recognition takes your picture, runs it through a scheme and charges the account you have earlier developed instead of getting to go to a stand and purchase a ticket. This can rationalize the method and dramatically optimize traffic flow. Here’s the future.
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I have just completed my survey of data (from articles, blogs, white papers, university websites, curated tech websites, and research papers all available online) about predictive analytics.
And I have a reason to believe that we are standing on the brink of a revolution that will transform everything we know about data science and predictive analytics.
But before we go there, you need to know: why the hype about predictive analytics? What is predictive analytics?
Let’s cover that first.
Importance of Predictive Analytics
By PhotoMix Ltd
According to Wikipedia:
Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. The enhancement of predictive web analytics calculates statistical probabilities of future events online. Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining.
Predictive analytics is why every business wants data scientists. Analytics is not just about answering questions, it is also about finding the right questions to answer. The applications for this field are many, nearly every human endeavor can be listed in the excerpt from Wikipedia that follows listing the applications of predictive analytics:
Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, mobility, healthcare, child protection, pharmaceuticals, capacity planning, social networking, and a multitude of numerous other fields ranging from the military to online shopping websites, Internet of Things (IoT), and advertising.
In a very real sense, predictive analytics means applying data science models to given scenarios that forecast or generate a score of the likelihood of an event occurring. The data generated today is so voluminous that experts estimate that less than 1% is actually used for analysis, optimization, and prediction. In the case of Big Data, that estimate falls to 0.01% or less.
Common Example Use-Cases of Predictive Analytics
Components of Predictive Analytics
A skilled data scientist can utilize the prediction scores to optimize and improve the profit margin of a business or a company by a massive amount. For example:
If you buy a book for children on the Amazon website, the website identifies that you have an interest in that author and that genre and shows you more books similar to the one you just browsed or purchased.
YouTube also has a very similar algorithm behind its video suggestions when you view a particular video. The site identifies (or rather, the analytics algorithms running on the site identifies) more videos that you would enjoy watching based upon what you are watching now. In ML, this is called a recommender system.
Netflix is another famous example where recommender systems play a massive role in the suggestions for ‘shows you may like’ section, and the recommendations are well-known for their accuracy in most cases
Google AdWords (text ads at the top of every Google Search) that are displayed is another example of a machine learning algorithm whose usage can be classified under predictive analytics.
Departmental stores often optimize products so that common groups are easy to find. For example, the fresh fruits and vegetables would be close to the health foods supplements and diet control foods that weight-watchers commonly use. Coffee/tea/milk and biscuits/rusks make another possible grouping. You might think this is trivial, but department stores have recorded up to 20% increase in sales when such optimal grouping and placement was performed – again, through a form of analytics.
Bank loans and home loans are often approved with the credit scores of a customer. How is that calculated? An expert system of rules, classification, and extrapolation of existing patterns – you guessed it – using predictive analytics.
Allocating budgets in a company to maximize the total profit in the upcoming year is predictive analytics. This is simple at a startup, but imagine the situation in a company like Google, with thousands of departments and employees, all clamoring for funding. Predictive Analytics is the way to go in this case as well.
IoT (Internet of Things) smart devices are one of the most promising applications of predictive analytics. It will not be too long before the sensor data from aircraft parts use predictive analytics to tell its operators that it has a high likelihood of failure. Ditto for cars, refrigerators, military equipment, military infrastructure and aircraft, anything that uses IoT (which is nearly every embedded processing device available in the 21st century).
Fraud detection, malware detection, hacker intrusion detection, cryptocurrency hacking, and cryptocurrency theft are all ideal use cases for predictive analytics. In this case, the ML system detects anomalous behavior on an interface used by the hackers and cybercriminals to identify when a theft or a fraud is taking place, has taken place, or will take place in the future. Obviously, this is a dream come true for law enforcement agencies.
So now you know what predictive analytics is and what it can do. Now let’s come to the revolutionary new technology.
End-to-End Predictive Analytics Product – for non-tech users!
In a remarkable first, a research team at MIT, USA have created a new science called social physics, or sociophysics. Now, much about this field is deliberately kept highly confidential, because of its massive disruptive power as far as data science is concerned, especially predictive analytics. The only requirement of this science is that the system being modeled has to be a human-interaction based environment. To keep the discussion simple, we shall explain the entire system in points.
All systems in which human beings are involved follow scientific laws.
These laws have been identified, verified experimentally and derived scientifically.
Bylaws we mean equations, such as (just an example) Newton’s second law: F = m.a (Force equals mass times acceleration)
These equations establish laws of invariance – that are the same regardless of which human-interaction system is being modeled.
Hence the term social physics – like Maxwell’s laws of electromagnetism or Newton’s theory of gravitation, these laws are a new discovery that are universal as long as the agents interacting in the system are humans.
The invariance and universality of these laws have two important consequences:
The need for large amounts of data disappears – Because of the laws, many of the predictive capacities of the model can be obtained with a minimal amount of data. Hence small companies now have the power to use analytics that was mostly used by the FAMGA(Facebook, Amazon, Microsoft, Google, Apple) set of companies since they were the only ones with the money to maintain Big Data warehouses and data lakes.
There is no need for data cleaning. Since the model being used is canonical, it is independent of data problems like outliers, missing data, nonsense data, unavailable data, and data corruption. This is due to the orthogonality of the model ( a Knowledge Sphere) being constructed and the data available.
Performance is superior to deep learning, Google TensorFlow, Python, R, Julia, PyTorch, and scikit-learn. Consistently, the model has outscored the latter models in Kaggle competitions, without any data pre-processing or data preparation and cleansing!
Data being orthogonal to interpretation and manipulation means that encrypted data can be used as-is. There is no need to decrypt encrypted data to perform a data science task or experiment. This is significant because the independence of the model functioning even for encrypted data opens the door to blockchain technology and blockchain data to be used in standard data science tasks. Furthermore, this allows hashing techniques to be used to hide confidential data and perform the data mining task without any knowledge of what the data indicates.
Are You Serious?
That’s a valid question given these claims! And that is why I recommend everyone who has the slightest or smallest interest in data science to visit and completely read and explore the following links:
Now when I say completely read, I mean completely read. Visit every section and read every bit of text that is available on the three sites above. You will soon understand why this is such a revolutionary idea.
These links above are articles about the social physics book and about the science of sociophysics in general.
For more details, please visit the following articles on Medium. These further document Endor.coin, a cryptocurrency built around the idea of sharing data with the public and getting paid for using the system and usage of your data. Preferably, read all, if busy, at least read Article No, 1.
Upon every data set, the first action performed by the Endor Analytics Platform is clustering, also popularly known as automatic classification. Endor constructs what is known as a Knowledge Sphere, a canonical representation of the data set which can be constructed even with 10% of the data volume needed for the same project when deep learning was used.
Creation of the Knowledge Sphere takes 1-4 hours for a billion records dataset (which is pretty standard these days).
Now an explanation of the mathematics behind social physics is beyond our scope, but I will include the change in the data science process when the Endor platform was compared to a deep learning system built to solve the same problem the traditional way (with a 6-figure salary expert data scientist).
From Appendix A: Social Physics Explained, Section 3.1, pages 28-34 (some material not included):
Prediction Demonstration using the Endor System:
Data: The data that was used in this example originated from a retail financial investment platform and contained the entire investment transactions of members of an investment community. The data was anonymized and made public for research purposes at MIT (the data can be shared upon request).
Summary of the dataset: – 7 days of data – 3,719,023 rows – 178,266 unique users
Automatic Clusters Extraction: Upon first analysis of the data the Endor system detects and extracts “behavioral clusters” – groups of users whose data dynamics violates the mathematical invariances of the Social Physics. These clusters are based on all the columns of the data, but is limited only to the last 7 days – as this is the data that was provided to the system as input.
Behavioural Clusters Summary
Number of clusters:268,218 Clusters sizes: 62 (Mean), 15 (Median), 52508 (Max), 5 (Min) Clusters per user:164 (Mean), 118 (Median), 703 (Max), 2 (Min) Users in clusters: 102,770 out of the 178,266 users Records per user: 6 (Median), 33 (Mean): applies only to users in clusters
Prediction Queries The following prediction queries were defined: 1. New users to become “whales”: users who joined in the last 2 weeks that will generate at least $500 in commission in the next 90 days 2. Reducing activity : users who were active in the last week that will reduce activity by 50% in the next 30 days (but will not churn, and will still continue trading) 3. Churn in “whales”: currently active “whales” (as defined by their activity during the last 90 days), who were active in the past week, to become inactive for the next 30 days 4. Will trade in Apple share for the first time: users who had never invested in Apple share, and would buy it for the first time in the coming 30 days
Knowledge Sphere Manifestation of Queries It is again important to note that the definition of the search queries is completely orthogonal to the extraction of behavioral clusters and the generation of the Knowledge Sphere, which was done independently of the queries definition.
Therefore, it is interesting to analyze the manifestation of the queries in the clusters detected by the system: Do the clusters contain information that is relevant to the definition of the queries, despite the fact that:
1. The clusters were extracted in a fully automatic way, using no semantic information about the data, and –
2. The queries were defined after the clusters were extracted, and did not affect this process.
This analysis is done by measuring the number of clusters that contain a very high concentration of “samples”; In other words, by looking for clusters that contain “many more examples than statistically expected”.
A high number of such clusters (provided that it is significantly higher than the amount received when randomly sampling the same population) proves the ability of this process to extract valuable relevant semantic insights in a fully automatic way.
Comparison to Google TensorFlow
In this section a comparison between prediction process of the Endor system and Google’s TensorFlow is presented. It is important to note that TensorFlow, like any other Deep Learning library, faces some difficulties when dealing with data similar to the one under discussion:
1. An extremely uneven distribution of the number of records per user requires some canonization of the data, which in turn requires:
2. Some manual work, done by an individual who has at least some understanding of data science.
3. Some understanding of the semantics of the data, that requires an investment of time, as well as access to the owner or provider of the data
4. A single-class classification, using an extremely uneven distribution of positive vs. negative samples, tends to lead to the overfitting of the results and require some non-trivial maneuvering.
This again necessitates the involvement of an expert in Deep Learning (unlike the Endor system which can be used by Business, Product or Marketing experts, with no perquisites in Machine Learning or Data Science).
An expert in Deep Learning spent 2 weeks crafting a solution that would be based on TensorFlow and has sufficient expertise to be able to handle the data. The solution that was created used the following auxiliary techniques:
1.Trimming the data sequence to 200 records per customer, and padding the streams for users who have less than 200 records with neutral records.
2.Creating 200 training sets, each having 1,000 customers (50% known positive labels, 50% unknown) and then using these training sets to train the model.
3.Using sequence classification (RNN with 128 LSTMs) with 2 output neurons (positive, negative), with the overall result being the difference between the scores of the two.
Observations (all statistics available in the white paper – and it’s stunning)
1.Endor outperforms Tensor Flow in 3 out of 4 queries, and results in the same accuracy in the 4th . 2.The superiority of Endor is increasingly evident as the task becomes “more difficult” – focusing on the top-100 rather than the top-500.
3.There is a clear distinction between “less dynamic queries” (becoming a whale, churn, reduce activity” – for which static signals should likely be easier to detect) than the “Who will trade in Apple for the first time” query, which are (a) more dynamic, and (b) have a very low baseline, such that for the latter, Endor is 10x times more accurate!
4.As previously mentioned – the Tensor Flow results illustrated here employ 2 weeks of manual improvements done by a Deep Learning expert, whereas the Endor results are 100% automatic and the entire prediction process in Endor took 4 hours.
Clearly, the path going forward for predictive analytics and data science is Endor, Endor, and Endor again!
Predictions for the Future
Personally, one thing has me sold – the robustness of the Endor system to handle noise and missing data. Earlier, this was the biggest bane of the data scientist in most companies (when data engineers are not available). 90% of the time of a professional data scientist would go into data cleaning and data preprocessing since our ML models were acutely sensitive to noise. This is the first solution that has eliminated this ‘grunt’ level work from data science completely.
The second prediction: the Endor system works upon principles of human interaction dynamics. My intuition tells me that data collected at random has its own dynamical systems that appear clearly to experts in complexity theory. I am completely certain that just as this tool developed a prediction tool with human society dynamical laws, data collected in general has its own laws of invariance. And the first person to identify these laws and build another Endor-style platform on them will be at the top of the data science pyramid – the alpha unicorn.
Final prediction – democratizing data science means that now data scientists are not required to have six-figure salaries. The success of the Endor platform means that anyone can perform advanced data science without resorting to TensorFlow, Python, R, Anaconda, etc. This platform will completely disrupt the entire data science technological sector. The first people to master it and build upon it to formalize the rules of invariance in the case of general data dynamics will for sure make a killing.
It is an exciting time to be a data science researcher!
Data Science is a broad field and it would require quite a few things to learn to master all these skills.
There are a huge number of ML algorithms out there. Trying to classify them leads to the distinction being made in types of the training procedure, applications, the latest advances, and some of the standard algorithms used by ML scientists in their daily work. There is a lot to cover, and we shall proceed as given in the following listing:
1. Statistical Algorithms
Statistics is necessary for every machine learning expert. Hypothesis testing and confidence intervals are some of the many statistical concepts to know if you are a data scientist. Here, we consider here the phenomenon of overfitting. Basically, overfitting occurs when an ML model learns so many features of the training data set that the generalization capacity of the model on the test set takes a toss. The tradeoff between performance and overfitting is well illustrated by the following illustration:
Overfitting – from Wikipedia
Here, the black curve represents the performance of a classifier that has appropriately classified the dataset into two categories. Obviously, training the classifier was stopped at the right time in this instance. The green curve indicates what happens when we allow the training of the classifier to ‘overlearn the features’ in the training set. What happens is that we get an accuracy of 100%, but we lose out on performance on the test set because the test set will have a feature boundary that is usually similar but definitely not the same as the training set. This will result in a high error level when the classifier for the green curve is presented with new data. How can we prevent this?
Cross-Validation is the killer technique used to avoid overfitting. How does it work? A visual representation of the k-fold cross-validation process is given below:
The entire dataset is split into equal subsets and the model is trained on all possible combinations of training and testing subsets that are possible as shown in the image above. Finally, the average of all the models is combined. The advantage of this is that this method eliminates sampling error, prevents overfitting, and accounts for bias. There are further variations of cross-validation like non-exhaustive cross-validation and nested k-fold cross validation (shown above). For more on cross-validation, visit the following link.
There are many more statistical algorithms that a data scientist has to know. Some examples include the chi-squared test, the Student’s t-test, how to calculate confidence intervals, how to interpret p-values, advanced probability theory, and many more. For more, please visit the excellent article given below:
Classification refers to the process of categorizing data input as a member of a target class. An example could be that we can classify customers into low-income, medium-income, and high-income depending upon their spending activity over a financial year. This knowledge can help us tailor the ads shown to them accurately when they come online and maximises the chance of a conversion or a sale. There are various types of classification like binary classification, multi-class classification, and various other variants. It is perhaps the most well known and most common of all data science algorithm categories. The algorithms that can be used for classification include:
Support Vector Machines
Linear Discriminant Analysis
and many more. A short illustration of a binary classification visualization is given below:
For more information on classification algorithms, refer to the following excellent links:
Regression is similar to classification, and many algorithms used are similar (e.g. random forests). The difference is that while classification categorizes a data point, regression predicts a continuous real-number value. So classification works with classes while regression works with real numbers. And yes – many algorithms can be used for both classification and regression. Hence the presence of logistic regression in both lists. Some of the common algorithms used for regression are
Support Vector Regression
Partial Least-Squares Regression
For more on regression, I suggest that you visit the following link for an excellent article:
Both articles have a remarkably clear discussion of the statistical theory that you need to know to understand regression and apply it to non-linear problems. They also have source code in Python and R that you can use.
Clustering is an unsupervised learning algorithm category that divides the data set into groups depending upon common characteristics or common properties. A good example would be grouping the data set instances into categories automatically, the process being used would be any of several algorithms that we shall soon list. For this reason, clustering is sometimes known as automatic classification. It is also a critical part of exploratory data analysis (EDA). Some of the algorithms commonly used for clustering are:
Hierarchical Clustering – Agglomerative
Hierarchical Clustering – Divisive
K-Nearest Neighbours Clustering
EM (Expectation Maximization) Clustering
Principal Components Analysis Clustering (PCA)
An example of a common clustering problem visualization is given below:
The above visualization clearly contains three clusters.
Another excellent article on clustering refer the link
Dimensionality Reduction is an extremely important tool that should be completely clear and lucid for any serious data scientist. Dimensionality Reduction is also referred to as feature selection or feature extraction. This means that the principal variables of the data set that contains the highest covariance with the output data are extracted and the features/variables that are not important are ignored. It is an essential part of EDA (Exploratory Data Analysis) and is nearly always used in every moderately or highly difficult problem. The advantages of dimensionality reduction are (from Wikipedia):
It reduces the time and storage space required.
Removal of multi-collinearity improves the interpretation of the parameters of the machine learning model.
It becomes easier to visualize the data when reduced to very low dimensions such as 2D or 3D.
It avoids the curse of dimensionality.
The most commonly used algorithm for dimensionality reduction is Principal Components Analysis or PCA. While this is a linear model, it can be converted to a non-linear model through a kernel trick similar to that used in a Support Vector Machine, in which case the technique is known as Kernel PCA. Thus, the algorithms commonly used are:
Ensembling means combining multiple ML learners together into one pipeline so that the combination of all the weak learners makes an ML application with higher accuracy than each learner taken separately. Intuitively, this makes sense, since the disadvantages of using one model would be offset by combining it with another model that does not suffer from this disadvantage. There are various algorithms used in ensembling machine learning models. The three common techniques usually employed in practice are:
Simple/Weighted Average/Voting: Simplest one, just takes the vote of models in Classification and average in Regression.
Bagging: We train models (same algorithm) in parallel for random sub-samples of data-set with replacement. Eventually, take an average/vote of obtained results.
Boosting: In this models are trained sequentially, where (n)th model uses the output of (n-1)th model and works on the limitation of the previous model, the process stops when result stops improving.
Stacking: We combine two or more than two models using another machine learning algorithm.
(from Amardeep Chauhan on Medium.com)
In all four cases, the combination of the different models ends up having the better performance that one single learner. One particular ensembling technique that has done extremely well on data science competitions on Kaggle is the GBRT model or the Gradient Boosted Regression Tree model.
We include the source code from the scikit-learn module for Gradient Boosted Regression Trees since this is one of the most popular ML models which can be used in competitions like Kaggle, HackerRank, and TopCoder.
In the last decade, there has been a renaissance of sorts within the Machine Learning community worldwide. Since 2002, neural networks research had struck a dead end as the networks of layers would get stuck in local minima in the non-linear hyperspace of the energy landscape of a three layer network. Many thought that neural networks had outlived their usefulness. However, starting with Geoffrey Hinton in 2006, researchers found that adding multiple layers of neurons to a neural network created an energy landscape of such high dimensionality that local minima were statistically shown to be extremely unlikely to occur in practice. Today, in 2019, more than a decade of innovation later, this method of adding addition hidden layers of neurons to a neural network is the classical practice of the field known as deep learning.
Deep Learning has truly taken the computing world by storm and has been applied to nearly every field of computation, with great success. Now with advances in Computer Vision, Image Processing, Reinforcement Learning, and Evolutionary Computation, we have marvellous feats of technology like self-driving cars and self-learning expert systems that perform enormously complex tasks like playing the game of Go (not to be confused with the Go programming language). The main reason these feats are possible is the success of deep learning and reinforcement learning (more on the latter given in the next section below). Some of the important algorithms and applications that data scientists have to be aware of in deep learning are:
Long Short term Memories (LSTMs) for Natural Language Processing
Recurrent Neural Networks (RNNs) for Speech Recognition
Convolutional Neural Networks (CNNs) for Image Processing
Deep Neural Networks (DNNs) for Image Recognition and Classification
Hybrid Architectures for Recommender Systems
Autoencoders (ANNs) for Bioinformatics, Wearables, and Healthcare
Deep Learning Networks typically have millions of neurons and hundreds of millions of connections between neurons. Training such networks is such a computationally intensive task that now companies are turning to the 1) Cloud Computing Systems and 2) Graphical Processing Unit (GPU) Parallel High-Performance Processing Systems for their computational needs. It is now common to find hundreds of GPUs operating in parallel to train ridiculously high dimensional neural networks for amazing applications like dreaming during sleep and computer artistry and artistic creativity pleasing to our aesthetic senses.
Artistic Image Created By A Deep Learning Network. From blog.kadenze.com.
For more on Deep Learning, please visit the following links:
In the recent past and the last three years in particular, reinforcement learning has become remarkably famous for a number of achievements in cognition that were earlier thought to be limited to humans. Basically put, reinforcement learning deals with the ability of a computer to teach itself. We have the idea of a reward vs. penalty approach. The computer is given a scenario and ‘rewarded’ with points for correct behaviour and ‘penalties’ are imposed for wrong behaviour. The computer is provided with a problem formulated as a Markov Decision Process, or MDP. Some basic types of Reinforcement Learning algorithms to be aware of are (some extracts from Wikipedia):
Q-Learning is a model-free reinforcement learning algorithm. The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances. It does not require a model (hence the connotation “model-free”) of the environment, and it can handle problems with stochastic transitions and rewards, without requiring adaptations. For any finite Markov decision process (FMDP), Q-learning finds a policy that is optimal in the sense that it maximizes the expected value of the total reward over any and all successive steps, starting from the current state. Q-learning can identify an optimal action-selection policy for any given FMDP, given infinite exploration time and a partly-random policy. “Q” names the function that returns the reward used to provide the reinforcement and can be said to stand for the “quality” of an action taken in a given state.
State–action–reward–state–action (SARSA) is an algorithm for learning a Markov decision process policy. This name simply reflects the fact that the main function for updating the Q-value depends on the current state of the agent “S1“, the action the agent chooses “A1“, the reward “R” the agent gets for choosing this action, the state “S2” that the agent enters after taking that action, and finally the next action “A2” the agent choose in its new state. The acronym for the quintuple (st, at, rt, st+1, at+1) is SARSA.
3.Deep Reinforcement Learning
This approach extends reinforcement learning by using a deep neural network and without explicitly designing the state space. The work on learning ATARI games by Google DeepMind increased attention to deep reinforcement learning or end-to-end reinforcement learning. Remarkably, the computer agent DeepMind has achieved levels of skill higher than humans at playing computer games. Even a complex game like DOTA 2 was won by a deep reinforcement learning network based upon DeepMind and OpenAI Gym environments that beat human players 3-2 in a tournament of best of five matches.
For more information, go through the following links:
If reinforcement learning was cutting edge data science, AutoML is bleeding edge data science. AutoML (Automated Machine Learning) is a remarkable project that is open source and available on GitHub at the following link that, remarkably, uses an algorithm and a data analysis approach to construct an end-to-end data science project that does data-preprocessing, algorithm selection,hyperparameter tuning, cross-validation and algorithm optimization to completely automate the ML process into the hands of a computer. Amazingly, what this means is that now computers can handle the ML expertise that was earlier in the hands of a few limited ML practitioners and AI experts.
AutoML has found its way into Google TensorFlow through AutoKeras, Microsoft CNTK, and Google Cloud Platform, Microsoft Azure, and Amazon Web Services (AWS). Currently it is a premiere paid model for even a moderately sized dataset and is free only for tiny datasets. However, one entire process might take one to two or more days to execute completely. But at least, now the computer AI industry has come full circle. We now have computers so complex that they are taking the machine learning process out of the hands of the humans and creating models that are significantly more accurate and faster than the ones created by human beings!
The basic algorithm used by AutoML is Network Architecture Search and its variants, given below:
Network Architecture Search (NAS)
PNAS (Progressive NAS)
ENAS (Efficient NAS)
The functioning of AutoML is given by the following diagram:
If you’ve stayed with me till now, congratulations; you have learnt a lot of information and cutting edge technology that you must read up on, much, much more. You could start with the links in this article, and of course, Google is your best friend as a Machine Learning Practitioner. Enjoy machine learning!
Europe has more than 307 million people on Facebook
There are five new Facebook profiles created every second!
More than 300 million photos get uploaded per day
Every minute there are 510,000 comments posted and 293,000 statuses updated (on Facebook)
And all this data was gathered 21st May, last year!
Photo by rawpixel on Unsplash
So I decided to do a more up to date survey. The data below was from an article written on 25th Jan 2019, given at the following link:
By 2020, the accumulated volume of big data will increase from 4.4 zettabytes to roughly 44 zettabytes or 44 trillion GB.
Originally, data scientists maintained that the volume of data would double every two years thus reaching the 40 ZB point by 2020. That number was later bumped to 44ZB when the impact of IoT was brought into consideration.
The rate at which data is created is increased exponentially. For instance, 40,000 search queries are performed per second (on Google alone), which makes it 3.46 million searches per day and 1.2 trillion every year.
Freshers in Analytics get paid more than then any other field, they can be paid up-to 6-7 Lakhs per annum (LPA) minus any experience, 3-7 years experienced professional can expect around 10-11 LPA and anyone with more than 7-10 years can expect, 20-30 LPA.
Opportunities in tier 2 cities can be higher, but the pay-scale of Tier 1 cities is much higher.
E-commerce is the most rewarding career with great pay-scale especially for Fresher’s, offering close to 7-8 LPA, while Analytics service provider offers the lowest packages, 6 LPA.
It is advised to combine your skills to attract better packages, skills such as SAS, R Python, or any open source tools, offers around 13 LPA.
Machine Learning is the new entrant in analytics field, attracting better packages when compared to the skills of big data, however for a significant leverage, acquiring the skill sets of both Big Data and Machine Learning will fetch you a starting salary of around 13 LPA.
Combination of knowledge and skills makes you unique in the job market and hence attracts high pay packages.
Picking up the top five tools of big data analytics, like R, Python, SAS, Tableau, Spark along with popular Machine Learning Algorithms, NoSQL Databases, Data Visualization, will make you irresistible for any talent hunter, where you can demand a high pay package.
As a professional, you can upscale your salary by upskilling in the analytics field.
So there is no doubt about the demand or the need for data scientists in the 21st century.
Now we have done a survey for India. but what about the USA?
The following data is an excerpt from an article by IBM< which tells the story much better than I ever could:
Jobs requiring machine learning skills are paying an average of $114,000.
Advertised data scientist jobs pay an average of $105,000 and advertised data engineering jobs pay an average of $117,000.59% of all Data Science and Analytics (DSA) job demand is in Finance and Insurance, Professional Services, and IT.
Annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020.
By 2020, the number of jobs for all US data professionals will increase by 364,000 openings to 2,720,000 according to IBM.
Data Science and Analytics (DSA) jobs remain open an average of 45 days, five days longer than the market average.
And yet still more! Look below:
By 2020 the number of Data Science and Analytics job listings is projected to grow by nearly 364,000 listings to approximately 2,720,000 The following is the summary of the study that highlights how in-demand data science and analytics skill sets are today and are projected to be through 2020.
There were 2,350,000 DSA job listings in 2015
By 2020, DSA jobs are projected to grow by 15%
Demand for Data scientists and data engineers is projectedto grow byneary40%
DSA jobs advertise average salaries of 80,265 USD$
81% of DSA jobs require workers with 3-5 years of experience or more.
Machine learning, big data, and data science skills are the most challenging to recruit for and potentially can create the greatest disruption to ongoing product development and go-to-market strategies if not filled.
So where does Dimensionless Technologies, with courses in Python, R, Deep Learning, NLP, Big Data, Analytics, and AWS coming soon, stand in the middle of all the demand?
The answer: right in the epicentre of the data science earthquake that is no hitting our IT sector harder than ever.The main reason I say this is because of the salaries increasing like your tummy after you finish your fifth Domino’s Dominator Cheese and Pepperoni Pizza in a row everyday for seven days! Have a look at the salaries for data science:
Do you know which city in India pays highest salaries to data scientist?
Mumbai pays the highest salary in India around 12.19L p.a.
Report of Data Analytics Salary of the Top Companies in India
Accenture’s Data Analytics Salary in India: 90% gets a salary of about Rs 980,000 per year
Tata Consultancy Services Limited Data Analytics Salary in India: 90% of the employees get a salary of about Rs 550,000 per year. A bonus of Rs 20,000 is paid to the employees.
EY (Ernst & Young) Data Analytics Salary in India: 75% of the employees get a salary of Rs 620,000 and 90% of the employees get a salary of Rs 770,000.
HCL Technologies Ltd. Data Analytics Salary in India: 90% of the people are paid Rs 940,000 per year approximately.
In the USA
To convert into INR, in the US, the salaries of a data scientist stack up as follows:
Lowest: 86,000 USD = 6,020,000 INR per year (60 lakh per year)
Average: 117,00 USD = 8,190,000 INR per year (81 lakh per year)
Highest: 157,000 USD = 10,990,000 INR per year(109 lakh per year or approximately one crore)
at the exchange rate of 70 INR = 1 USD.
By now you should be able to understand why everyone is running after data science degrees and data science certifications everywhere.
The only other industry that offers similar salaries is cloud computing.
A Personal View
On my own personal behalf, I often wondered – why does everyone talk about following your passion and not just about the money. The literature everywhere advertises“Follow your heart and it will lead you to the land of your dreams”. But then I realized – passion is more than your dreams. A dream, if it does not serve others in some way, is of no inspirational value. That is when I found the fundamental role – focus on others achieving their hearts desires, and you will automatically discover your passion. I have many interests, and I found my happiness doing research in advanced data science and quantum computing and dynamical systems, focusing on experiments that combine all three of them together as a single unified theory. I found that that was my dream. But, however, I have a family and I need to serve them. I need to earn.
Thus I relegated my dreams of research to a part-time level and focused fully on earning for my extended family, and serving them as best as I can. Maybe you will come to your own epiphany moment yourself reading this article. What do you want to do with your life? Personally, I wish to improve the lives of those around me, especially the poor and the malnourished. That feeds my heart. Hence my career decision – invest wisely in the choices that I make to garner maximum benefit for those around me. And work on my research papers in the free time that I get.
So my hope for you today is: having read this article, understand the rich potential that lies before you if you can complete your journey as a data scientist. The only reason that I am not going into data science myself is that I am 34 years old and no longer in the prime of my life to follow this American dream. Hence I found my niche in my interest in research. And further, I realized that a fundamental ‘quantum leap’ would be made if my efforts were to succeed. But as for you, the reader of this article, you may be inspired or your world-view expanded by reading this article and the data contained within. My advice to you is: follow your heart. It knows you best and will not betray you into any false location. Data science is the future for the world. make no mistake about that. And – from whatever inspiration you have received go forward boldly and take action. Take one day at a time. Don’t look at the final goal. Take one day at a time. If you can do that, you will definitely achieve your goals.
The salary at the top, per year. From glassdoor.com. Try not to drool. 🙂
Finding Your Passion
Many times when you’re sure you’ve discovered your passion and you run into a difficult topic, that leaves you stuck, you are prone to the famous impostor syndrome. “Maybe this is too much for me. Maybe this is too difficult for me. Maybe this is not my passion. Otherwise, it wouldn’t be this hard for me.” My dear friend, this will hit you. At one point or the other. At such moments, what I do, based upon lessons from the following course, which I highly recommend to every human being on the planet, is: Take a break. Do something different that completely removes the mind from your current work. Be completely immersed in something else. Or take a nap. Or – best of all – go for a run or a cycle. Exercise. Workout. This gives your brain cells rest and allows them to process the data in the background. When you come back to your topic, fresh, completely free of worry and tension, completely recharged, you will have an insight into the problem for you that completely solves it. Guaranteed. For more information, I highly suggest the following two resources:
or the most popular MOOC of all time, based on the same topic: Coursera
Learning How to Learn – Coursera and IEEE
This should be your action every time you feel stuck. I have completely finished this MOOC and the book and it has given me the confidence to tackle any subject in the world, including quantum mechanics, topology, string theory, and supersymmetry theory. I strongly recommend this resource (from experience).
So Dimensionless Technologies (link given above) is your entry point to all things data science. Before you go to TensorFlow, Hadoop, Keras, Hive, Pig, MapReduce, BigQuery, BigTable, you need to know the following topics first:
All the best. Your passion is not just a feeling. It is a choice you make the day in and a day out whether you like it or not. That is the definition of character – to do what must be done even if you don’t feel like it. Internalize this advice, and there will be no limits to how high you can go.All the best!
TensorFlow 2.0 is coming soon. And boy, are we super-excited! TensorFlow first began the trend of open-sourcing AI and DL frameworks for use by the community. And what has been the result? TensorFlow has become an entire ML ecosystem for all kinds of AI technology. Just to give you an idea, here are the features that an absolutely incredible community has added to the original TensorFlow package:
Features of TensorFlow contributed from the Open Source Community
Which means – now we have CUDA (library for executing ML code on GPUs) v8-v9-v10 (9.2 left out), GPGPU, GPU-Native Code, TPU (Tensor Processing Unit – custom hardware provided by Google specially designed for TensorFlow), Cloud TPUs, FPGAs (Field-Programmable Gate Arrays – Custom Programmable Hardware), ASIC (Application Specific Integrated Circuits) chip hardware specially designed for TensorFlow, and now MKL for Intel, BLAS optimization, LINPACK optimization (the last three all low-level software optimization for matrix algebra, vector algebra, and linear algebra packages), and so much more that I can’t fit it into the space I have to write this article. To give you a rough idea of what the TensorFlow architecture looks like now, have a look at this highly limited graphic:
Note: XLA stands for A(X)ccelerated Linear Algebra compiler still in development that provides highly optimized computational performance gains.
And Now TensorFlow 2.0
This release is expected shortly in the next six months from Google. Some of its most exciting features are:
Keras Integration as the Main API instead of raw TensorFlow code
Simplified and Integrated Workflow
More Support for TensorFlow Lite and TensorFlow Edge Computing
Extensions to TensorFlow.js for Web Applications and Node.js
TensorFlow Integration for Swift and iOS
TensorFlow Optimization for Android
Unified Programming Paradigms (Directed Acyclic Graph/Functional and Stack/Sequential)
Support for the new upcoming WebGPU Chrome RFC proposal
Integration of tf.contrib best Package implementations into the core package
Expansion of tf.contrib into Separate Repos
TensorFlow AIY (Artificial Intelligence for Yourself) support
Improved TPU & TPU Pod support, Distributed Computation Support
Improved HPC integration for Parallel Computing
Support for TPU Pods up to v3
Community Integration for Development, Support and Research
Domain-Specific Community Support
Extra Support for Model Validation and Reuse
End-to-End ML Pipelines and Products available at TensorFlow Hub
And yes – there is still much more that I can’t cover in this blog.
Wow – that’s an Ocean! What can you Expand Upon?
Yes – that is an ocean. But to keep things as simple as possible (and yes – stick to the word limit – cause I could write a thousand words on every one of these topics and end up with a book instead of a blog post!) we’ll focus on the most exciting and striking topics (ALLare exciting – we’ll cover the ones with the most scope for our audience).
1. Keras as the Main API to TensorFlow
Earlier, comments like these below were common on the Internet:
“TensorFlow is broken” – Reddit user
“Implementation so tightly coupled to specification that there is no scope for extension and modification easily in TensorFlow” – from a post on Blogger.com
“We need a better way to design deep learning systems than TensorFlow” – Google Plus user
Understanding the feedback from the community, Keras was created as an open source project designed to be an easier interface to TensorFlow. Its popularity grew very rapidly, and now nearly 95% of ML tasks happening in the real world can be written just using Keras. Packaged as ‘Deep Learning for Humans’, Keras is simpler to use. Though, of course, PyTorch gives it a real run for the money as far as simplicity is concerned!
In TensorFlow 2.0, Keras has been adopted as the main API to interact with TensorFlow. Support for pure TensorFlow has not been removed, and thus TensorFlow 2.0 will be completely backwards-compatible, including a conversion tool that can be used to convert TensorFlow 1.x to TensorFlow 2.0 where implementation details differ. Kind of like the Python tool 2to3.py! So now, Keras is the main API for TensorFlow deep learning applications – which takes out a huge amount of unnecessary complexity burdens from the ML engineer.
Use tf.data for data loading and preprocessing or use NumPy.
Use Keras or Premade Estimators to do your model construction and validation work.
Use tf.function for DAG graph-based execution or use eager execution ( a technique to smoothly debug and run your deep learning model, on by default in TF 2.0).
For TPUs, GPUs, distributed computing, or TPU Pods, utilize Distribution Strategy for high-performance-computing distributed deep learning applications.
This means that now even novices at machine learning can perform deep learning tasks with relative ease. And of course, did we mention the wide variety of end-to-end pluggable deep learning solutions available at TensorHub and on the Tutorials section? And guess what – they’re all free to download and use for commercial purposes. Google, you are truly the best friend of the open source community!
In all the above platforms, where computational and memory resources are scarce, there is a common trend in TF 2.0 that extends over most of these platforms.
Greater support for various ops in TF 2.0 and several deployment techniques
SIMD+ support for WebAssembly
Support for Swift (iOS) in Colab.
A smaller and lighter footprint for Edge Computing, Mobile Computing and IoT
Better support for audio and text-based models
Easier conversion of trained TF 2.0 graphs
Increased and improved mobile model optimization techniques
As you can see, Google knows that Edge and Mobile is the future as far as computing is concerned, and has designed its products accordingly. TF Mobile should be replaced by TF Lite soon.
4. Unified Programming Models and Methodologies
There are two/three major ways to code deep learning networks in Keras. They are:
We build models symbolically by describing the structure of its DAG (Directed Acyclic Graph) or a sequential stack. This following image is an example of Keras code written symbolically.
From Medium.com TensorFlow publication
This looks familiar to most of us since this is how we use Keras usually. The advantages of this process are that it’s easy to visualize, has debugging errors usually only at compile time, and corresponds to our mental model of the deep learning network and is thus easy to work with.
The following code is an example of the Sequential paradigm or subclassing paradigm to building a deep learning network:
From Medium.com TensorFlow publication (code still in development)
Rather similar to Object Oriented Python, this style was first introduced into the deep learning community in 2015 and has since been used by a variety of deep learning libraries. TF 2.0 has complete support for it. Although it appears simpler, it has some serious disadvantages.
Imperative models are not a data structure that is transparent but an opaque class instead. You are prone to many errors at runtime following this approach. As a deep learning practitioner, you are obliged to know both symbolic as well as imperative and subclassing methodologies of coding your deep neural network. For example, recursive or recurrent neural networks cannot be defined by the symbolic programming model. So it is good to know both. But be aware of the disparate advantages and disadvantages of them!
5. TensorFlow AIY
This is a brand new offering from Google and other AI companies such as Intel. AIY stands for Artificial Intelligence for Yourself (a play on DIY – Do It Yourself) and is a new marketing scheme from Google to show consumers how easy it is to use TensorFlow in your own DIY devices to create your own AI-enabled projects and gadgets. This is a very welcome trend, since it literally brings the power of AI to the masses, at a very low price. I honestly feel that now the day is nearing when schoolchildren will bring their AIY projects for school exhibitions and that the next generation of whiz kids will be chock full of AI expertise and development of new and highly creative and innovative AI products. This is a fantastic trend and now I have my own to-buy-and-play-with list if I can order these products on Google at a minimal shipping charge. So cool!
6. Guidelines and New Incentives for Community Participation and Research Papers
We are running out of the word limit very fast! I hoped to cover TPUs and TPU Pods and Distributed Computation, but for right now, this is my final point. Realizing and recognizing the massive role the open source community has played in the development of TensorFlow as a worldwide brand for deep learning neural nets, the company has set up various guidelines to introduce domain-specific innovation and the authoring of research papers and white papers from the TensorFlow community, in collaboration with each other. To quote:
Grow global TensorFlow communities and user groups.
Collaborate with partners to co-develop and publish research papers.
Continue to publish blog posts and YouTube videos showcasing applications of TensorFlow and build user case studies for high impact application
In fact, when I read more of the benefits of participating in the TensorFlow community open source development process, I could not help it, I joined the TensorFlow development community, myself as well!
A Dimensionless Technologies employee contributing to TensorFlow!
Who knows – maybe, God-willing, one day my code will be a part of TensorFlow 2.0/2.x! Or – even better – there could be a research paper published under my name with collaborators, perhaps. The world is now built around open source technologies, and as a developer, there has never been a better time to be alive!
So don’t forget, on the day of writing this blog article, 31th January 2019, TensorFlow 2.0 is yet to be released, but since its an open source project, there are no secrets and Google is (literally) being completely ‘open’ about the steps it will take to take TF further as the world market leader in deep learning. I hope this article has increased your interest in AI, open source development, Google, TensorFlow, deep learning, and artificial neural nets. Finally, I would like to point you to some other articles on this blog that focus on Google TensorFlow. Visit any of the following blog posts for more details on TensorFlow, Artificial intelligence Trends and Deep Learning:
Now unless you’ve been a hermit or a monk living in total isolation, you will have heard of Amazon Web Services and AWS Big Data. It’s a sign of an emerging global market and the entire world becoming smaller and smaller every day. Why? The current estimate for the cloud computing market in 2020, according to Forbes (a new prediction, highly reliable), is a staggering 411 Billion USD$! Visit the following link to read more and see the statistics for yourself:
To know more, refer to Wikipedia for the following terms by clicking on them, which mark, in order the evolution of cloud computing (I will also provide the basic information to keep this article as self-contained as possible):
This was the beginning of the revolution called cloud computing. Companies and industries across verticals understood that they could let experts manage their software development, deployment, and management for them, leaving them free to focus on their key principle – adding value to their business sector. This was mostly confined to the application level. Follow the heading link for more information, if required.
PaaS began when companies started to understand that they could outsource both software management and operating systems and maintenance of these platforms to other companies that specialized in taking care of them. Basically, this was SaaS taken to the next level of virtualization, on the Internet. Amazon was the pioneer, offering SaaS and PaaS services worldwide from the year 2006. Again the heading link gives information in depth.
After a few years in 2011, the big giants like Microsoft, Google, and a variety of other big names began to realize that this was an industry starting to boom beyond all expectations, as more and more industries spread to the Internet for worldwide visibility. However, Amazon was the market leader by a big margin, since it had a five-year head start on the other tech giants. This led to unprecedented disruption across verticals, as more and more companies transferred their IT requirements to IaaS providers like Amazon, leading to (in some cases) savings of well over 25% and per-employee cost coming down by 30%.
After all, why should companies set up their own servers, data warehouse centres, development centres, maintenance divisions, security divisions, and software and hardware monitoring systems if there are companies that have the world’s best experts in every one of these sectors and fields that will do the job for you at less than 1% of the cost the company would incur if they had to hire staff, train them, monitor them, buy their own hardware, hire staff for that as well – the list goes on-and-on. If you are already a tech giant like, say Oracle, you have everything set up for you already. But suppose you are a startup trying to save every penny – and there and tens of thousands of such startups right now – why do that when you have professionals to do it for you?
There is a story behind how AWS got started in 2006 – I’m giving you a link, so as to not make this article too long:
OK. So now you may be thinking, so this is cloud computing and AWS – but what does it have to do with Big Data Speciality, especially for startups? Let’s answer that question right now.
A startup today has a herculean task ahead of them.
Not only do they have to get noticed in the big booming startup industry, they also have to scale well if their product goes viral and receives a million hits in a day and provide security for their data in case a competitor hires hackers from the Dark Web to take down their site, and also follow up everything they do on social media with a division in their company managing only social media, and maintain all their hardware and software in case of outages. If you are a startup counting every penny you make, how much easier is it for you to outsource all your computing needs (except social media) to an IaaS firm like AWS.
You will be ready for anything that can happen, and nothing will take down your website or service other than your own self. Oh, not to mention saving around 1 million USD$ in cost over the year!If you count nothing but your own social media statistics, every company that goes viral has to manage Big Data! And if your startup disrupts an industry, again, you will be flooded with GET requests, site accesses, purchases, CRM, scaling problems, avoiding downtime, and practically everything a major tech company has to deal with!
Bro, save your grey hairs, and outsource all your IT needs (except social media – that you personally need to do) to Amazon with AWS!
And the Big Data Speciality?
Having laid the groundwork, let’s get to the meat of our article. The AWS certified Big Data Speciality website mentions the following details:
The AWS Certified Big Data – Specialty exam validates technical skills and experience in designing and implementing AWS services to derive value from data. The examination is for individuals who perform complex Big Data analyses and validates an individual’s ability to:
Implement core AWS Big Data services according to basic architecture best practices
Design and maintain Big Data
Leverage tools to automate data analysis
So, what is an AWS Big Data Speciality certified expert? Nothing more than an internationally recognized certification that says that you, as a data scientist can work professionally in AWS and Big Data’s requirements in Data Science.
Please note: the eligibility criteria for an AWS Big Data Speciality Certification is:
Minimum five years hands-on experience in a data analytics field
Background in defining and architecting AWS Big Data services with the ability to explain how they fit in the data life cycle of collection, ingestion, storage, processing, and visualization
Experience in designing a scalable and cost-effective architecture to process data
To put it in layman’s terms, if you, the data scientist, were Priyanka Chopra, getting the AWS Big Data Speciality certification passed successfully is the equivalent of going to Hollywood and working in the USA starring in Quantico!
Suddenly, a whole new world is open at your feet!
But don’t get too excited: unless you already have five years experience with Big Data, there’s a long way to go. But work hard, take one step at a time, don’t look at the goal far ahead but focus on every single day, one day, one task at a time, and in the end you will reach your destination. Persistence, discipline and determination matters. As simple as that.
Five Advantages of an AWS Big Data Speciality
1. Massive Increase in Income as a Certified AWS Big Data Speciality Professional (a long term 5 years plus goal)
Everyone who’s anyone in data science knows that a data scientist in the US earns an average of 100,000 USD$ every year. But what is the average salary of an AWS Big Data Speciality Certified professional? Hold on to your hat’s folks; it’s 160,000 $USD starting salary. And with just two years of additional experience, that salary can cross 250,000 USD$ every year if you are a superstar at your work. Depending upon your performance on the job! Do you still need a push to get into AWS? The following table shows the average starting salaries for specialists in the following Amazon products: (from www.dezyre.com)
Top Paying AWS Skills According to Indeed.com
Elastic MapReduce (EMR)
Key Management Service
2. Wide Ecosystem of Tools, Libraries, and Amazon Products
Amazon Web Services, compared to other Cloud IaaS services, has by far the widest ecosystem of products and tools. As a Big Data specialist, you are free to choose your career path. Do you want to get into AI? Do you have an interest in ES3 (storage system) or HIgh-Performance Serverless computing (AWS Lambda). You get to choose, along with the company you work for. I don’t know about you, but I’m just writing this article and I’mseriouslyexcited!
3. Maximum Demand Among All Cloud Computing jobs
If you manage to clear the certification in AWS, then guess what – AWS certified professionals have by far the maximum market demand! Simply because more than half of all Cloud Computing IaaS companies use AWS. The demand for AWS certifications is the maximum right now. To mention some figures: in 2019, 350,000 professionals will be required for AWS jobs. 60% of cloud computing jobs ask for AWS skills (naturally, considering that it has half the market share).
4. Worldwide Demand In Every Country that Has IT
It’s not just in the US that demand is peaking. There are jobs available in England, France, Australia, Canada, India, China, EU – practically every nation that wants to get into IT will welcome you with open arms if you are an AWS certified professional. And look no further than this site. AWS training will be offered soon, here: on Dimensionless.in. Within the next six months at the latest!
5. Affordable Pricing and Free One Year Tier to Learn AWS
Amazon has always been able to command the lowest prices because of its dominance in the market share. AWS offers you a free 1 year of paid services on its cloud IaaS platform. Completely free for one year. AWS training materials are also less expensive compared to other offerings. The following features are offered free for one single year under Amazon’s AWS free tier system:
The following is a web-scrape of their free-tier offering:
AWS Free Tier One Year Resources Available
There were initially seven pages in the Word document that I scraped from www.aws.com/free. To really have a look, go to the website on the previous link and see for yourself on the following link (much more details in much higher resolution). Please visit this last mentioned link. That alone will show you why AWS is sitting pretty on top of the cloud – literally.
Right now, AWS rules the roost in cloud computing. But there is competition from Microsoft, Google, and IBM. Microsoft Azure has a lot of glitches which costs a lot to fix. Google Cloud Platform is cheaper but has very high technical support charges. A dark horse here: IBM Cloud. Their product has a lot of offerings and a lot of potential. Third only to Google and AWS. If you are working and want to go abroad or have a thirst for achievement, go for AWS. Totally. Finally, good news, all Dimensionless current students and alumni, the languages that AWS is built on has 100% support for Python! (It also supports, Go, Ruby, Java, Node.js, and many more – but Python has 100% support).
Keep coming to this website – expect to see AWS courses here in the near future!