Algorithm exploration for AI projects

Training an AI model is an iterative process. At the beginning of an AI project, we don't know which AI algorithm will produce the best performing model. Based on domain expertise, there is usually a small set of AI algorithms that perform well in a given domain. But, each of these algorithms must be tried and evaluated. This article explores approaches for automatically finding an appropriate algorithm for use in AI model development

AutoML

Automated machine learning (automated ML or AutoML) is the process of partially automating algorithm and hyperparameter exploration during model development. It allows data scientists, analysts, and developers to build ML models at high scale, while sustaining model quality.

Traditional machine learning model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. Automated machine learning accelerates the time it takes to get production-ready ML models in cases with standard problem formulations, sufficient data quality, and sufficient data quantity.

Refer to the Ways to use AutoML in Azure Machine Learning for how to get started in Azure ML.

Below are some AutoML resources for Azure:

• Azure Machine Learning examples
• Microsoft NNI
  • An open source toolkit for automating the ML lifecycle. The toolkit includes feature engineering, neural architecture search, model compression and hyperparameter tuning.
• Set up AutoML training with the Azure ML Python SDK v2
• Sample project for an AML v2 pipeline + AutoML object detection
  • Provides a sample implementation of an AML pipeline using the v2 Python SDK to use AutoML on image training data for object detection for AutoML object detection; dataset registration and versioning for consistent data lineage records; and stratified splitting techniques for consistent feature representation across the training and validation sets.
• AutoML Vision Classifier
  • Illustrates how to deploy an AutoML generated model to a Docker container, using FastAPI and Uvicorn to check for best performing model.
• Prevent overfitting and imbalanced data with AutoML

Other useful resources:

• TPOT: TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.