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

AWS Machine Learning Blog

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.

ML 95
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Build a robust text-to-SQL solution generating complex queries, self-correcting, and querying diverse data sources

AWS Machine Learning Blog

Structured Query Language (SQL) is a complex language that requires an understanding of databases and metadata. The solution in this post aims to bring enterprise analytics operations to the next level by shortening the path to your data using natural language. Today, generative AI can enable people without SQL knowledge.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.

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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning Blog

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts.

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Use Amazon SageMaker Model Card sharing to improve model governance

AWS Machine Learning Blog

As Artificial Intelligence (AI) and Machine Learning (ML) technologies have become mainstream, many enterprises have been successful in building critical business applications powered by ML models at scale in production. They provide a fact sheet of the model that is important for model governance.

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Google experts on practical paths to data-centricity in applied AI

Snorkel AI

Abhishek Ratna, in AI ML marketing, and TensorFlow developer engineer Robert Crowe, both from Google, spoke as part of a panel entitled “Practical Paths to Data-Centricity in Applied AI” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Is more data always better? AR : Absolutely.

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Google experts on practical paths to data-centricity in applied AI

Snorkel AI

Abhishek Ratna, in AI ML marketing, and TensorFlow developer engineer Robert Crowe, both from Google, spoke as part of a panel entitled “Practical Paths to Data-Centricity in Applied AI” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Is more data always better? AR : Absolutely.