IBM named a Leader
Gartner releases 2021 Magic Quadrant for Data Science and Machine Learning Platforms.
From AutoML to AutoAI
Accelerating AI and model lifecycle management
What is AutoML?
Automated machine learning (AutoML) is the process of automating the manual tasks that data scientists must complete as they build and train machine learning models (ML models). These tasks include feature engineering and selection, choosing the type of machine learning algorithm; building an analytical model based on the algorithm; hyperparameter optimization, training the model on tested data sets and running the model to generate scores and findings. Researchers developed AutoML to help data scientists build predictive models without having deep ML model expertise. AutoML also frees data scientists from the rote tasks involved in building a machine learning pipeline, allowing them to focus on extracting the insights needed to solve important business problems.
What is AutoAI?
AutoAI is a variation of AutoML. It extends the automation of model building to the entire AI lifecycle. Like AutoML, AutoAI applies intelligent automation to the steps of building predictive machine learning models. These steps include preparing data sets for training; identifying the best type of model for the given data, such as a classification or regression model; and choosing the columns of data that best support the problem the model is solving, known as feature selection. Automation then tests a variety of hyperparameter tuning options to reach the best result as it generates, and then ranks, model-candidate pipelines based on metrics such as accuracy and precision. The best performing pipelines can be put into production to process new data and deliver predictions based on the model training.
Quick capability comparison
AutoAI versus AutoML
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Data preparation
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Feature engineering
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Hyperparameter optimization
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Automated model deployment
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One-click deployment
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Model testing and scoring
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Code generation
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Support for:
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Debiasing and drift mitigation
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Model risk management
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AI lifecycle management
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Transfer learning
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Any AI models
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Advanced data refinery
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Why is AutoAI important?
Intelligent automation empowers everyone
Speed AI lifecycle management
Automatically build machine learning and AI models without deep data science expertise. Empower data scientists, developers, ML engineers and analysts to generate top-candidate model pipelines. Tackle skill set gaps and increase productivity for your machine learning projects.
Accelerate machine learning implementation
Build custom AI and machine learning models in minutes or even seconds. Experiment, train and deploy models more rapidly at scale. Increase repeatability and governance of machine learning and AI model lifecycles while reducing mundane, time-consuming tasks.
Implement trustworthy AI
Address explainability, fairness, robustness, transparency and privacy as part of the AI lifecycle. Mitigate model drift, bias and risk in AI and machine learning. Validate and monitor models to verify that AI and machine learning performance meets business goals. Help meet corporate social responsibility (CSR) and environmental social governance (ESG).
Increase efficiency of ModelOps
Cut costs of AI and machine learning model operations (ModelOps) through unifying tools, processes and people. Reduce spend on managing legacy or point tools and infrastructures. Save time and resources to deliver production-ready models with automated AI and ML lifecycles.
How can you use AutoAI?
Build ModelOps
Build ModelOps
Facilitate collaboration between data scientists and DevOps to optimize integration of AI models into applications.
Foster responsible, explainable AI
Foster responsible, explainable AI
Explore the importance of building trust in production AI while getting results faster and managing risk and compliance.
Automate time-series forecasting
Automate time-series forecasting
Learn how models can predict future values of a time series by incorporating the best performing models from all possible model classes, not just a single class.
Features of AutoAI
Automate key steps in the model lifecycle
Data pre-processing
Apply various algorithms, or estimators, to analyze, clean and prepare raw data for machine learning. Automatically detect and categorize features based on data type, such as categorical or numerical. Use hyperparameter optimization to determine the best strategies for missing value imputation, feature encoding and feature scaling.
Automated model selection
Select models through candidate algorithm testing and ranking against small subsets of the data. Gradually increase the size of the subset for the most promising algorithms. Enable ranking of a large number of candidate algorithms for model selection with the best match for the data.
Feature engineering
Transform raw data into the combination of features that best represents the problem to achieve the most accurate prediction. Explore various feature construction choices in a structured, non-exhaustive manner, while progressively maximizing model accuracy using reinforcement learning.
Hyperparameter optimization
Refine and optimize model pipelines using model training and scoring typical in machine learning. Choose the best model to put into production based on performance.
Model monitoring integration
Integrate monitoring on model drift, fairness and quality though model input and output details, training data and payload logging. Implement passive or active debiasing, while analyzing direct and indirect bias.
Model validation support
Extend with model and data insights and validate if your models meet your expected performance. Continuously improve your models by measuring model quality and comparing model performance.
Gain the power of AutoAI
IBM Watson® Studio on IBM Cloud Pak® for Data
As part of the IBM Cloud Pak for Data end-to-end data and AI platform, IBM Watson Studio features the AutoAI toolkit that automatically prepares data, applies machine learning algorithms and builds model pipelines that are best suited for your data sets and predictive modeling use cases.
Learn more →
Try the product →
AutoAI demo (07:49)
AutoAI in action in IBM Watson Studio
AutoAI configuration
AutoAI configuration
Drag the .csv file and select the column to predict.
Pipeline leaderboard
Pipeline leaderboard
Rank model accuracy and show pipeline information.
Model evaluation
Model evaluation
Review accuracy, precision and recall to assess models.
Model deployment
Model deployment
Promote models to deployment spaces.
Customer stories
Regions Bank develops trustworthy AI
See the benefits gained by this bank using IBM Cloud Pak for Data to analyze data, assess data drift and measure model performance.
Highmark Health cuts model build time by 90%
Learn how this healthcare network built a predictive model that uses insurance claims data to identify patients likely to develop sepsis.
Wunderman Thompson reimagines AI
Learn how this marketing communications agency uses AutoAI to drive high-volume predictions and identify new customers.
Why AutoAI from IBM
Focused development by IBM Research
An IBM Research team is committed to applying state-of-the-art techniques from AI, ML and data management to accelerate and optimize the creation of machine learning and data science workflows. The team’s first efforts around AutoML focused on using hyperband/Bayesian optimization for hyperparameter search and hyperband/ENAS/DARTS for Neural Architecture Search.
They have continued to focus on AutoAI development, including automation of the pipeline configuration and hyperparameter optimization. A significant enhancement is the hyperparameter optimization algorithm, which is optimized for cost function evaluation such as model training and scoring. This helps to expedite convergence to the best solution.
IBM Research is also applying automated artificial intelligence to help ensure trust and explainability in AI models. With AutoAI in IBM Watson Studio, users see visualizations of each stage of the process, from data preparation, to algorithm selection, to model creation. Additionally, IBM AutoAI automates the tasks for continuous improvement of the model and makes it easier to integrate AI model APIs into applications through its ModelOps capabilities. The evolution of AutoAI within the IBM Watson Studio product contributed to IBM being named a Leader in the 2021 Gartner Magic Quadrant for Data Science and Machine Learning Platforms.
Dive deeper
Open source packages
The demand for AutoML has led to the development of open source software that can be used by data science experts and non-experts. Leading open source tools include auto-sklearn, auto-keras and auto-weka. IBM Research contributes to Lale (link resides outside IBM), a Python library that extends the capabilities of scikit-learn to support a broad spectrum of automation, including algorithm selection, hyperparameter tuning and topology search. As described in a paper from IBM Research (PDF, 1.1 MB), Lale works by automatically generating search spaces for established AutoML tools. Experiments show these search spaces achieve results competitive with state-of-the-art tools while offering more versatility.
Get started with AutoAI
Try AutoAI with IBM Watson Studio on IBM Cloud Pak for Data.