AWS Machine Learning Blog
Category: AWS Step Functions
Create a cross-account machine learning training and deployment environment with AWS Code Pipeline
A continuous integration and continuous delivery (CI/CD) pipeline helps you automate steps in your machine learning (ML) applications such as data ingestion, data preparation, feature engineering, modeling training, and model deployment. A pipeline across multiple AWS accounts improves security, agility, and resilience because an AWS account provides a natural security and access boundary for your […]
Define and run Machine Learning pipelines on Step Functions using Python, Workflow Studio, or States Language
May 2024: This post was reviewed and updated for accuracy. You can use various tools to define and run machine learning (ML) pipelines or DAGs (Directed Acyclic Graphs). Some popular options include AWS Step Functions, Apache Airflow, KubeFlow Pipelines (KFP), TensorFlow Extended (TFX), Argo, Luigi, and Amazon SageMaker Pipelines. All these tools help you compose […]
Automate continuous model improvement with Amazon Rekognition Custom Labels and Amazon A2I: Part 2
In Part 1 of this series, we walk through a continuous model improvement machine learning (ML) workflow with Amazon Rekognition Custom Labels and Amazon Augmented AI (Amazon A2I). We explained how we use AWS Step Functions to orchestrate model training and deployment, and custom label detection backed by a human labeling private workforce. We described […]
Automate continuous model improvement with Amazon Rekognition Custom Labels and Amazon A2I: Part 1
If you need to integrate image analysis into your business process to detect objects or scenes unique to your business domain, you need to build your own custom machine learning (ML) model. Building a custom model requires advanced ML expertise and can be a technical challenge if you have limited ML knowledge. Because model performance […]
ML model explainability with Amazon SageMaker Clarify and the SKLearn pre-built container
Amazon SageMaker Clarify is a new machine learning (ML) feature that enables ML developers and data scientists to detect possible bias in their data and ML models and explain model predictions. It’s part of Amazon SageMaker, an end-to-end platform to build, train, and deploy your ML models. Clarify was made available at AWS re:Invent 2020. […]
Creating an end-to-end application for orchestrating custom deep learning HPO, training, and inference using AWS Step Functions
Amazon SageMaker hyperparameter tuning provides a built-in solution for scalable training and hyperparameter optimization (HPO). However, for some applications (such as those with a preference of different HPO libraries or customized HPO features), we need custom machine learning (ML) solutions that allow retraining and HPO. This post offers a step-by-step guide to build a custom deep […]
Build a CI/CD pipeline for deploying custom machine learning models using AWS services
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality ML artifacts. AWS Serverless Application Model (AWS SAM) is […]
Rust detection using machine learning on AWS
Visual inspection of industrial environments is a common requirement across heavy industries, such as transportation, construction, and shipbuilding, and typically requires qualified experts to perform the inspection. Inspection locations can often be remote or in adverse environments that put humans at risk, such as bridges, skyscrapers, and offshore oil rigs. Many of these industries deal […]
Active learning workflow for Amazon Comprehend custom classification models – Part 2
Update Sep 2021: Amazon Comprehend has launched a suite of features for Comprehend Custom to enable continuous model improvements by giving developers the ability to version custom models, new training options for custom entity recognition models that reduce data preprocessing, ability to provide specific test sets during training, and live migration to new model endpoints. Refer to […]
Automating complex deep learning model training using Amazon SageMaker Debugger and AWS Step Functions
Amazon SageMaker Debugger can monitor ML model parameters, metrics, and computation resources as the model optimization is in progress. You can use it to identify issues during training, gain insights, and take actions like stopping the training or sending notifications through built-in or custom actions. Debugger is particularly useful in training challenging deep learning model […]