Is The Age of Extention for Data Scientists and Machine Learning Engineers near?

Artificial Intelligence is introduced to automate the tasks done by humans, making the machine do things better, what currently humans are doing better. Humans vs Machine is another topic of debate, we are not going to do this now. We are currently focused on the jobs of these machine learning pioneers and some of the arising questions.
➔ Can a machine do the task done by these pioneers?
➔ Can a machine out power a machine learning professional?
➔ Will there be no need for machine learning engineers or data scientists in the future?

First, let’s take a look at those technologies that will force you to ask those questions.

We use machine learning for automation but, now machine learning itself is becoming automatic.
It’s like “Automation of Automation” interesting, isn’t it? , let’s get into it.

Neural Networks are a very popular machine learning technique used currently. If you are not familiar with it so don’t worry I will give you a brief insight into it later in this article.
But for now understand that From training to experimenting with different parameters, the process of designing neural networks is labor-intensive, challenging, and often cumbersome. But imagine if it was possible to automate this process. Yes, it is possible now, let’s see how.
Before we dive into this magical automation world we need to understand some basic concepts, that are explained ahead.

Reinforcement Learning

Reinforcement learning is a type of dynamic programming that trains algorithms using a system of reward and punishment.
A reinforcement learning algorithm learns by interacting with its environment. The algorithm reward for performing correctly and penalize for performing incorrectly. The agent learns without intervention from a human by maximizing its reward and minimizing its penalty.

Neural Network

A neural network is a type of machine learning which models itself after the human brain, creating an artificial neural network that via an algorithm allows the computer to learn by incorporating new data.

The above image depicts a Perceptron. The simplest and oldest model of Neuron, as we know it. Takes some inputs, sums them up, applies activation function, and passes them to the output layer. No magic here.

Deep Neural network(DNN)

A deep neural network (DNN) is an artificial neural network (ANN) with multiple layers (called hidden layers) between the input and output layers.

DNN is somewhat different than traditional machine learning as here we don’t need to do feature extraction manually, it removes a lot of workload from us.

Convolutional Neural networks(CNN)

A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm that can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image, and be able to differentiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms. While in primitive methods filters are hand-engineered, with enough training, ConvNets can learn these filters/characteristics.

Recurrent Neural Network (RNN)

Recurrent Neural Network remembers the past and its decisions are influenced by what it has learned from the past. Note: Basic neural networks “remember” things too, but they remember things they learned during training.
While RNNs learn similarly while training, in addition, they remember things learned from prior inputs while generating outputs

I think that this much is sufficient for understanding further concepts, so let’s dive into it.

Neural Architecture Search with Reinforcement Learning

This uses a recurrent neural network (RNN) to generate the model of neural networks. It consists of two parts Controller and Child Network. The RNN is trained with reinforcement learning in order to improve its accuracy on the validation set.
The controller now has a set of “actions”: Choosing the size of these parameters. Given the controller’s choice (actions), a neural network is created (but weight not yet assigned). This network will be then trained in the training data to get its weight updated

So now due to this we no longer need to manually create a neural network. Because there is a neural network to create a neural network. Isn’t it’s interesting but wait it is not the end, but just a beginning there is more automation going on let’s see.

Automated Machine Learning (AutoMl)
It is the process of automating the process of applying machine learning to real-world problems. Automated machine learning can target various stages of the machine learning process like:-
● data preparation
● feature engineering
● model selection
● selection of evaluation metrics
● hyperparameter optimization

Cloud AutoML

Cloud AutoML enables developers with limited machine learning expertise to train high-quality models. It relies on Google’s transfer learning and neural architecture search technology.
You can get hands-on experience here: Cloud AutoML

These are the works done by a machine learning engineer on routine bases. So in the presence of these technologies do we don’t need Machine Learning and data Scientists any more. No, we still need them these are just the tools for them but still, they need to implement.
The main problem is how to implement machine learning to solve real-world problems. These tools will help them and enable them to focus more on finding a better solution.

An Insight into the Future…

Even if there are many automation available and many pre-trained models are also there but they are built for specific tasks by using specific datasets.
For other tasks and for other datasets you need to have a good knowledge of machine learning for selecting appropriate architectures and create the neural network right from scratch.
But as technology grows and machines are becoming more and more intelligent its time to let the machine take care of the design. Although the real action in machine learning is going to be in two areas.

First, Discovering what data to collect, designing the collection and curation of the data, and preprocessing the data into a form that’s amenable for machine learning.
The second area that’s going to stay highly relevant is knowing what questions to ask and what models to build from that data.

So, at last, the imagination and entrepreneurship that’s required to collect, curate and process data will be highly valued. You will be a better data engineer, a better data analyst, and a better machine learning engineer if you understand what machines in the middle are doing.