AUTOMATED-MACHINE-LEARNING

Automated machine learning is the process of automating the process of applying machine learning to real-world problems.

It mainly focuses on two major aspects - 

1. Data acquisition/collection:- Data acquisition is the process for bringing data that has been created by a source outside the organization, into the organization, for production use.
2. Prediction:- It is a result we get on an algorithm after it training it on a particular dataset and applied it on a new data

AUTOML V/S MACHINE LEARNING

The workflow of traditional machine learning

Firstly Data Preprocessing is very broad in its context because it is one of the most time-consuming tasks and it includes many subtasks such as Data Cleaning, Data Transformation, Feature Selection, etc. Secondly deciding which model is the best for your problem often requires more experience than knowledge and it is also a very time-consuming task.

The workflow of AutoML:

Auto ML tends to automate the maximum number of steps in an ML pipeline - with a minimum amount of human effort and without compromising the model's performance.

BENEFITS OF AUTOML:

1. Applying machine learning models to our problems usually requires computer science skills, domain expertise, and mathematical expertise. Getting an expert with all these skills is not always a walk in the park
2. AutoML also reduces the amount of time it would take to develop and test a machine learning model.
3. AutoML also reduces bias and errors that occur when a human being is designing the machine learning models.
4. AutoML is a step towards making ML accessible to everyone.

AUTOML CONCEPTS:

There are two major concepts to grasp as far as AutoML is concerned: -

1. Neural Architecture Search:

Neural architecture search is the process of automating the design of neural networks. Usually, reinforcement learning or evolutionary algorithms are used in the design of these networks. In reinforcement learning, models are punished for low accuracies and rewarded for high accuracies. Using this technique the model will always obtain higher accuracies.

2. Transfer Learning:

Transfer learning is a machine learning technique where a model is trained on one problem and applying it to a different but related problem. This enables us to obtain high accuracies while using less computation time and power. Transfer learning works best for problems where the datasets are similar to the ones used in pre-trained models.

AUTOML FRAMEWORK

Some common automl framework

1. AUTO SKLEARN
2. AUTO KERAS
3. H20
4. TPOT(Tree-Based Pipeline Optimization Tool)
5. AUTO GLUON
6. ML Box
7. FEATURE TOOLS