Introduction to AutoML-The future of industry ML execution

• The Field of Data Science is an ever-evolving field. People are researching and experimenting every single day to come up with solutions to a problem.
• With the production of more than a billion data every day, Data Scientists need a way that helps them reduce wasting time on building models and experimenting with the model to come up with the best possible model.
• We know that there is no thumb rule which says data is well fitted for a certain model which makes finding the right model a tedious task.
• Although the Model building(Machine Learning) part of the field is about 10% of the total process, it involves heavy and time-consuming experimentation needed to build a model that handles the business process.
• The experimentation parts include:
  → Hyperparameter tuning.
  → trying out different models.
  → Checking metrics of different models.

These few processes undoubtedly take-up 90% of the time for any Data Science solution that includes Machine Learning.

With the advancing technologies in today’s world Machine learning is currently utilized in almost every field imaginable which has pushed its importance infinitely.That is why the need of AutoML is rising in the industry

Also, Check out our Article on:

Applying AutoML (Part-1) using Auto-Sklearn
Applying AutoML(Part-2) with MLBox
Applying AutoML (Part-3) with TPOT
Applying AutoML (Part-4) using H2O
Automated Hyperparameter tuning
AutoML on the Cloud

What is AutoML?

• Automated machine learning (AutoML) is a way that automates the ML pipeline in real-world applications.
  AutoML covers the total pipeline from the crude dataset to the deployable AI model.
• It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.
• It is an AI solution in order to apply machine learning without much expertise.
• It is a new field and is expected to have more development in the upcoming years.

Why was AutoML Introduced?

The whole idea behind the AutoML was to help Data Scientists to accelerate their process of experimentation so that they can focus majorly on the business problem and come up with a solution as fast as possible.

• A data scientist has to apply the appropriate data pre-processing, parameter engineering, parameter extraction, and parameter selection methods that make the dataset ready for inference and hence for data analysis.
• Following those pre-processing steps, an algorithm must be appropriately selected and hyper-parameter optimization must be performed to maximize the predictive performance of their final machine learning model.

A classic Machine learning flow consists of :

Getting the right set of hyperparameters can be exhausting or even writing down the code of the whole pipeline.
This is why the introduction of AutoML is playing an important role in the industry.

AutoML tends to automate the most extreme number of steps in an ML pipeline — with a base measure of human exertion and without bargaining the model’s exhibition.

• We know that building one model and cross-validating it, hyper tuning it, checking performance takes a lot of time, let alone trying out different models.
• Data scientists are problem solvers and this can only be done when the process is immediate/fast.
• Experimenting takes a lot of time and this causes a distraction from the business goal.

Need for AutoML!

Data scientists, analysts, and developers across industries can use automated ML to:

• Implement ML solutions without extensive programming knowledge
• Save time and resources
• Leverage data science best practices
• Provide agile problem-solving

How ML experts and Data Scientist work with AutoML

• Data scientists can often accelerate their work dramatically — from months to days
• AI-based feature engineering automatically builds, evaluates, and exposes features by combining data from multiple columns, often across different tables and sources
• The solution helps Data Scientists and ML experts find the best model by creating a large number of models and analyzing them as per your business requirements.
• The solution also helps in breaking down the big data silos to provide relevant information about the business and the market it is working in.

Will AutoML replace Data Scientists?

• The data scientists have made predictions that in the coming time, AutoML will help businesses to handle most of the data cleaning process.
• Even though AutoML automates most of the tasks, the idea of replacing data scientists is still decades away.
• Problem identification and solving can only be done by a human brain, so it is safe to say that AutoML would not replace Data Scientists anytime soon.

AutoML Libraries for Python

1. Auto-Sklearn

• It is one of the most popular AutoML packages in Python.
• It uses the Sci-kit Learn machine learning library for transforming data and machine learning algorithms.
• It uses a Bayesian Optimization search procedure to discover a top-performing model pipeline for a given dataset in an effective way.
• One of the key advantages of using Auto-Sklearn is that it not only discovers the data preparation and the best model for the provided data but also, learns from models that performed well on similar datasets and can automatically create an ensemble of top-performing models discovered as part of the optimization process.

2. MLBox

MLBox is an incredible Automated Machine Learning python library.
It provides the following features:

• Fast reading and distributed data preprocessing/cleaning/formatting.
• Highly robust feature selection and leak detection.
• Accurate hyper-parameter optimization in high-dimensional space.
• State-of-the-art predictive models for classification and regression (Deep Learning, Stacking, LightGBM,…).
• Prediction with model interpretation.

3. auto_ml

• It is basically designed for production.
• auto_ml has all three libraries integrated into it.
  DeepLearningClassifier and DeepLearningRegressor, XGBClassifier and XGBRegressor, LGBMClassifier and LGBMRegressor.
• These names can be used to pass in the function in order to train the model.

ml_predictor.train(data, model_names=[‘DeepLearningClassifier’])

• These projects all have prediction time in the 1-millisecond range for a single prediction and can be serialized to disk and loaded into a new environment after training.

4. TPOT

• TPOT stands for the Tree-based Pipeline Optimization Tool.
• It optimizes machine learning pipelines using genetic programming.
• It mechanizes the most monotonous piece of AI by cleverly investigating a large number of potential pipelines to locate the best one for your information.

• When TPOT is done looking (or you become weary of pausing), it gives you the Python code for the best pipeline it found so you can fiddle with the pipeline from that point.

• TPOT is built on top of sci-kit learn, so all of the code it generates should look familiar

5. OPTUNA

• Optuna is a programmed hyperparameter advancement programming system, especially intended for Machine Learning tasks.
• It includes an objective, characterizes by-run style client API. The code composed using Optuna appreciates high seclusion, and the client of Optuna can progressively develop the quest spaces for the hyperparameters.

Optuna has modern functionalities as follows:

• Lightweight, versatile, and platform-agnostic architecture
  Handle a wide assortment of assignments with a straightforward establishment that has barely any prerequisites.
• Pythonic search spaces
  Define search spaces using familiar Python syntax including conditionals and loops.
• Efficient optimization algorithms
  Adopt state-of-the-art algorithms for sampling hyperparameters and efficiently pruning unpromising trials.
• Easy parallelization
  Scale studies to tens or hundreds of workers with little or no changes to the code.
• Quick visualization
  Inspect optimization histories from a variety of plotting functions.

6. PyCaret

One of the latest library in the field which has helped hundreds of Data Scientist Reduce time and effort to code 100 lines of code.

• PyCaret is an open-source low-code machine learning library in Python that aims to reduce the time needed for experimenting with different machine learning models.
• It helps Data Scientist to perform any experiments end-to-end quickly and more efficiently.
• PyCaret being a low-code library makes you more productive. With less time spent coding, you and your team can now focus on business problems.
• PyCaret is a wrapper around many ML models and frameworks such as XGBoost, Scikit-learn, and many more.

AutoDL Libraries for Python

1. talos

• talos is a hyperparameter optimization library for Keras.
• Talos profoundly changes the conventional Keras work process by completely robotizing hyperparameter tuning and model assessment. Talos uncovered Keras usefulness altogether and there is no new language structure or formats to learn.
• Talos is made for AI researchers and Data Scientists that need to stay in the unlimited authority of their Keras models, however are burnt out on thoughtless boundary jumping and befuddling streamlining arrangements that include intricacy as opposed to diminishing it.
• In practically no time, without learning any new sentence structure, Talos permits you to design, perform, and assess hyperparameter streamlining tests that yield best in class results over a wide scope of expectation errands.
• Talos gives the least complex but then most remarkable accessible strategy for hyperparameter streamlining with Keras.

2. HyperAS

• A very simple convenience wrapper around hyperopt for fast prototyping with Keras models.
• Hyperas lets you utilize the intensity of hyperopt without learning its linguistic structure. Rather, simply characterize your Keras model as you are utilized to, yet utilize basic layout documentation to characterize hyper-boundary reaches to tune.
• Hyperas translates your script into hyperopt compliant code.

Also, Check out our Article on:

Applying AutoML (Part-1) using Auto-Sklearn
Applying AutoML(Part-2) with MLBox
Applying AutoML (Part-3) with TPOT
Applying AutoML (Part-4) using H2O
Automated Hyperparameter tuning
AutoML on the Cloud

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