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EvalML is an open-source AutoML library written in python that automates a large part of the machine learning process and we can easily evaluate which machine learning pipeline works better for the given set of data

Automated machine learning (AutoML)?

Automated machine learning (AutoML) is the process of applying machine learning (ML) models to real-world problems using automation. More specifically, it automates the selection, composition and parameterization of machine learning models. Automating the machine learning process makes it more user-friendly and often provides faster, more accurate outputs than hand-coded algorithms.

AutoML software platforms make machine learning more user-friendly and give organizations without a specialized data scientist or machine learning expert access to machine learning. These platforms can be acquired from a third-party vendor, accessed through open source repositories like GitHub or built in-house.

How does the AutoML process work?

AutoML is typically a platform or open source library that simplifies each step in the machine learning process, from handling a raw dataset to deploying a practical machine learning model. In traditional machine learning, models are developed by hand, and each step in the process must be handled separately.

AutoML automatically locates and uses the optimal type of machine learning algorithm for a given task. It does this with two concepts:

• Neural architecture search, which automates the design of neural networks. This helps AutoML models discover new architectures for problems that require them.
• Transfer learning, in which pretrained models apply what they've learned to new data sets. Transfer learning helps AutoML apply existing architectures to new problems that require it.

Pros and cons of AutoML

The main benefits of AutoML are:

• Efficiency -- It speeds up and simplifies the machine learning process and reduces training time of machine learning models.
• Cost savings -- Having a faster, more efficient machine learning process means a company can save money by devoting less of its budget to maintaining that process.
• Accessibility -- Having a simpler process allows companies to save money on training staff or hiring experts. It also makes machine learning a viable possibility for a wider range of companies.
• Performance -- AutoML algorithms also tend to be more efficient than hand-coded models.

Best Model Get By AutoML by EvalML

Auto ML in EvalML

EvalML is an open-source Python library for automatically building, optimizing, and evaluating machine learning pipelines for a given dataset. Like any other AutoML library, it also performs data pre-processing, feature engineering, selection, model building, hyper-parameter tuning, cross-validation, EvalML is an AutoML library which builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.

Key Functionality

• Automation - Makes machine learning easier. Avoid training and tuning models by hand. Includes data quality checks, cross-validation and more.
• Data Checks - Catches and warns of problems with your data and problem setup before modeling.
• End-to-end - Constructs and optimizes pipelines that include state-of-the-art preprocessing, feature engineering, feature selection, and a variety of modeling techniques.
• Model Understanding - Provides tools to understand and introspect on models, to learn how they'll behave in your problem domain.
• Domain-specific - Includes repository of domain-specific objective functions and an interface to define your own.