Artificial Intelligence

Tag: MLOps

Automate AIOps with SageMaker Unified Studio Projects, Part 2: Technical implementation

In this post, we focus on implementing this architecture with step-by-step guidance and reference code. We provide a detailed technical walkthrough that addresses the needs of two critical personas in the AI development lifecycle: the administrator who establishes governance and infrastructure through automated templates, and the data scientist who uses SageMaker Unified Studio for model development without managing the underlying infrastructure.

Automate AIOps with Amazon SageMaker Unified Studio projects, Part 1: Solution architecture

This post presents architectural strategies and a scalable framework that helps organizations manage multi-tenant environments, automate consistently, and embed governance controls as they scale their AI initiatives with SageMaker Unified Studio.

How Rocket Companies modernized their data science solution on AWS

In this post, we share how we modernized Rocket Companies’ data science solution on AWS to increase the speed to delivery from eight weeks to under one hour, improve operational stability and support by reducing incident tickets by over 99% in 18 months, power 10 million automated data science and AI decisions made daily, and provide a seamless data science development experience.

Governing the ML lifecycle at scale, Part 4: Scaling MLOps with security and governance controls

This post provides detailed steps for setting up the key components of a multi-account ML platform. This includes configuring the ML Shared Services Account, which manages the central templates, model registry, and deployment pipelines; sharing the ML Admin and SageMaker Projects Portfolios from the central Service Catalog; and setting up the individual ML Development Accounts where data scientists can build and train models.

How Zalando optimized large-scale inference and streamlined ML operations on Amazon SageMaker

This post is cowritten with Mones Raslan, Ravi Sharma and Adele Gouttes from Zalando. Zalando SE is one of Europe’s largest ecommerce fashion retailers with around 50 million active customers. Zalando faces the challenge of regular (weekly or daily) discount steering for more than 1 million products, also referred to as markdown pricing. Markdown pricing is […]

Customized model monitoring for near real-time batch inference with Amazon SageMaker

In this post, we present a framework to customize the use of Amazon SageMaker Model Monitor for handling multi-payload inference requests for near real-time inference scenarios. SageMaker Model Monitor monitors the quality of SageMaker ML models in production. Early and proactive detection of deviations in model quality enables you to take corrective actions, such as retraining models, auditing upstream systems, or fixing quality issues without having to monitor models manually or build additional tooling.

How Thomson Reuters Labs achieved AI/ML innovation at pace with AWS MLOps services

How Thomson Reuters Labs achieved AI/ML innovation at pace with AWS MLOps services

In this post, we show you how Thomson Reuters Labs (TR Labs) was able to develop an efficient, flexible, and powerful MLOps process by adopting a standardized MLOps framework that uses AWS SageMaker, SageMaker Experiments, SageMaker Model Registry, and SageMaker Pipelines. The goal being to accelerate how quickly teams can experiment and innovate using AI and machine learning (ML)—whether using natural language processing (NLP), generative AI, or other techniques. We discuss how this has helped decrease the time to market for fresh ideas and helped build a cost-efficient machine learning lifecycle.

Generate unique images by fine-tuning Stable Diffusion XL with Amazon SageMaker

Stable Diffusion XL by Stability AI is a high-quality text-to-image deep learning model that allows you to generate professional-looking images in various styles. Managed versions of Stable Diffusion XL are already available to you on Amazon SageMaker JumpStart (see Use Stable Diffusion XL with Amazon SageMaker JumpStart in Amazon SageMaker Studio) and Amazon Bedrock (see […]

How Dialog Axiata used Amazon SageMaker to scale ML models in production with AI Factory and reduced customer churn within 3 months

The telecommunications industry is more competitive than ever before. With customers able to easily switch between providers, reducing customer churn is a crucial priority for telecom companies who want to stay ahead. To address this challenge, Dialog Axiata has pioneered a cutting-edge solution called the Home Broadband (HBB) Churn Prediction Model. This post explores the […]

Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries. However, implementing security, data privacy, and governance controls are still key challenges faced by customers when implementing ML […]