AWS Machine Learning Blog

Category: Amazon SageMaker

Extend Amazon SageMaker Pipelines to include custom steps using callback steps

Launched at AWS re:Invent 2020, Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With Pipelines, you can create, automate, and manage end-to-end ML workflows at scale. You can extend your pipelines to include steps for tasks performed outside of Amazon SageMaker by taking advantage […]

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Analyze customer churn probability using call transcription and customer profiles with Amazon SageMaker

Regardless of the industry or product, customers are the most important component in a business’s success and growth. Businesses go to great lengths to acquire and more importantly retain their existing customers. Customer satisfaction links directly to revenue growth, business credibility, and reputation. These are all key factors in a sustainable and long-term business growth […]

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Bring your own container to project model accuracy drift with Amazon SageMaker Model Monitor

The world we live in is constantly changing, and so is the data that is collected to build models. One of the problems that is often seen in production environments is that the deployed model doesn’t behave the same way as it did during the training phase. This concept is generally called data drift or […]

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Use Amazon SageMaker Feature Store in a Java environment

Feature engineering is a process of applying transformations on raw data that a machine learning (ML) model can use. As an organization scales, this process is typically repeated by multiple teams that use the same features for different ML solutions. Because of this, organizations are forced to develop their own feature management system. Additionally, you […]

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Deploy variational autoencoders for anomaly detection with TensorFlow Serving on Amazon SageMaker

Anomaly detection is the process of identifying items, events, or occurrences that have different characteristics from the majority of the data. It has many applications in various fields, like fraud detection for credit cards, insurance, or healthcare; network intrusion detection for cybersecurity; KPI metrics monitoring for critical systems; and predictive maintenance for in-service equipment. There […]

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Run image classification with Amazon SageMaker JumpStart

Last year, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart hosts 196 computer vision models, 64 natural language processing (NLP) models, 18 pre-built end-to-end solutions, and 19 example notebooks to help you get started with using […]

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Automate a centralized deployment of Amazon SageMaker Studio with AWS Service Catalog

This post outlines the best practices for provisioning Amazon SageMaker Studio for data science teams and provides reference architectures and AWS CloudFormation templates to help you get started. We use AWS Service Catalog to provision a Studio domain and users. The AWS Service Catalog allows you to provision these centrally without requiring each user to […]

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Dynamic A/B testing for machine learning models with Amazon SageMaker MLOps projects

In this post, you learn how to create a MLOps project to automate the deployment of an Amazon SageMaker endpoint with multiple production variants for A/B testing. You also deploy a general purpose API and testing infrastructure that includes a multi-armed bandit experiment framework. This testing infrastructure will automatically optimize traffic to the best-performing model […]

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Deploy shadow ML models in Amazon SageMaker

Amazon SageMaker helps data scientists and developers prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. SageMaker accelerates innovation within your organization by providing purpose-built tools for every step of ML development, including labeling, data preparation, feature engineering, statistical bias detection, AutoML, […]

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Announcing managed inference for Hugging Face models in Amazon SageMaker

Hugging Face is the technology startup, with an active open-source community, that drove the worldwide adoption of transformer-based models thanks to its eponymous Transformers library. Earlier this year, Hugging Face and AWS collaborated to enable you to train and deploy over 10,000 pre-trained models on Amazon SageMaker. For more information on training Hugging Face models […]

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