Towards AI

AUGUST 7, 2024

From Solo Notebooks to Collaborative Powerhouse: VS Code Extensions for Data Science and ML Teams Photo by Parabol | The Agile Meeting Toolbox on Unsplash In this article, we will explore the essential VS Code extensions that enhance productivity and collaboration for data scientists and machine learning (ML) engineers.

Data Science 110

Data Science ML ML Engineer Data Scientist 110

AWS Machine Learning Blog

SEPTEMBER 4, 2024

Amazon SageMaker provides capabilities to remove the undifferentiated heavy lifting of building and deploying ML models. SageMaker simplifies the process of managing dependencies, container images, auto scaling, and monitoring. They often work with DevOps engineers to operate those pipelines.

DevOps 101

DevOps ML Engineer Metadata ML 101

AWS Machine Learning Blog

SEPTEMBER 18, 2024

With the SageMaker HyperPod auto-resume functionality, the service can dynamically swap out unhealthy nodes for spare ones to ensure the seamless continuation of the workload. Also included are SageMaker HyperPod cluster software packages, which support features such as cluster health check and auto-resume.

Auto-complete 111

Auto-complete ML Generative AI Deep Learning 111

AWS Machine Learning Blog

JUNE 13, 2023

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.

Generative AI 78

Generative AI Auto-complete AI Modeling Machine Learning 78

The MLOps Blog

JANUARY 23, 2023

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.

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

The MLOps Blog

JUNE 27, 2023

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?

Metadata 134

Metadata Machine Learning Data Scientist Data Quality 134

AWS Machine Learning Blog

NOVEMBER 14, 2024

However, model governance functions in an organization are centralized and to perform those functions, teams need access to metadata about model lifecycle activities across those accounts for validation, approval, auditing, and monitoring to manage risk and compliance. It can take up to 20 minutes for the setup to complete.

ML 89

ML Auto-complete Auto-classification Machine Learning 89