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Deploy Amazon SageMaker pipelines using AWS Controllers for Kubernetes

AWS Machine Learning Blog

Its scalability and load-balancing capabilities make it ideal for handling the variable workloads typical of machine learning (ML) applications. SageMaker simplifies the process of managing dependencies, container images, auto scaling, and monitoring. They often work with DevOps engineers to operate those pipelines.

DevOps 114
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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning Blog

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.

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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

Lived through the DevOps revolution. 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. Some are my 3–4 year bets.

DevOps 59
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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning Blog

Create a KMS key in the dev account and give access to the prod account Complete the following steps to create a KMS key in the dev account: On the AWS KMS console, choose Customer managed keys in the navigation pane. Choose Create key. For Key type , select Symmetric. For Script Path , enter Jenkinsfile. Choose Save.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Can you see the complete model lineage with data/models/experiments used downstream? Some of its features include a data labeling workforce, annotation workflows, active learning and auto-labeling, scalability and infrastructure, and so on. MLOps workflows for computer vision and ML teams Use-case-centric annotations.

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An open-source, low-code Python wrapper for easy usage of the Large Language Models such as…

Mlearning.ai

autogpt : Auto-GPT is an “Autonomous AI agent” that given a goal in natural language, will allow Large Language Models (LLMs) to think, plan, and execute actions for us autonomously. The complete code of the APP can be found here. It is built on top of OpenAI’s Generative Pretrained Transformer (GPT-3.5 If you liked the blog post pls.