What is AutoML?


AutoML is the concept of automating the practical aspects of machine learning model development - the creation of models, hyper-parameter tuning, algorithm selection, and feature management. The degree of automation can vary widely - from manual control of algorithm selection and tuning to a fully automated "one-click" solution. It simplifies the process of training and evaluating machine learning models so that data scientists can focus on business problems.

AutoML

Why you need

AutoML?


Advanced Feature Management in a simple IDE
Advanced Feature Management
Transform, encode, and visualize data with a simple interface
Handle missing and outlier values with a few clicks
Enable Deep Feature Synthesis by stacking primitives
Adaptive Machine Learning
Adaptive Machine Learning
Train ML models manually or automatically
State-of-the-art algorithms for fast training
Automated versioning, registration and project management
Simplified Deep Learning
Simplified Deep Learning
Use modern deep-learning frameworks to develop and test neural networks quickly
Enabled with advanced architecture like LSTM, CNN
Simplified transfer learning

Practical machine learning requires many repetitive tasks. Data scientists explore data and transform it in different ways before even choosing to begin machine learning. Machine learning then requires many iterations of tuning and testing before data scientists are confident in a model. This means that an onerous amount of time is spent on repetitive, mundane tasks and on working with code. AutoML simplifies this using a workbench environment that automates all technical aspects of Machine Learning. Data scientists can now spend time working on domain problems and model accuracy than writing code.

How

AutoML

works?


AutoML Working
Wish to learn more about AutoML?

Visit our Cognitive Modeling Studio page.

Cognitive Modeling Studio