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MLOps Is an Extension of DevOps. Not a Fork — My Thoughts on THE MLOPS Paper as an MLOps Startup CEO

The MLOps Blog

Came to ML from software. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”. Most of our customers are doing ML/MLOps at a reasonable scale, NOT at the hyperscale of big-tech FAANG companies. . – How about the ML engineer? Let me explain.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. Flexibility, speed, and accessibility : can you customize the metadata structure? Can you see the complete model lineage with data/models/experiments used downstream?

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How Forethought saves over 66% in costs for generative AI models using Amazon SageMaker

AWS Machine Learning Blog

This post is co-written with Jad Chamoun, Director of Engineering at Forethought Technologies, Inc. and Salina Wu, Senior ML Engineer at Forethought Technologies, Inc. In addition, deployments are now as simple as calling Boto3 SageMaker APIs and attaching the proper auto scaling policies. 2xlarge instances.

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How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

AWS Machine Learning Blog

The ETL pipeline, MLOps pipeline, and ML inference should be rebuilt in a different AWS account. To solve this problem, we make the ML solution auto-deployable with a few configuration changes. ML engineers no longer need to manage this training metadata separately.