AWS Machine Learning Blog

NOVEMBER 16, 2023

This is a joint blog with AWS and Philips. Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. Philips is a health technology company focused on improving people’s lives through meaningful innovation.

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Data Scientist ML Data Science Automation 91

The MLOps Blog

JANUARY 26, 2024

For this, we have to build an entire machine-learning system around our models that manages their lifecycle, feeds properly prepared data into them, and sends their output to downstream systems. Understanding machine learning pipelines Machine learning (ML) pipelines are a key component of ML systems. But what is an ML pipeline?

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AWS Machine Learning Blog

SEPTEMBER 18, 2023

In this post, we discuss the benefits of using Ray and Amazon SageMaker for distributed ML, and provide a step-by-step guide on how to use these frameworks to build and deploy a scalable ML workflow. SageMaker is a fully managed service for building, training, and deploying ML models.

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The MLOps Blog

OCTOBER 3, 2023

In this episode, Mikiko Bazeley shares her learnings from building the ML Platform at Mailchimp. She is currently the head of MLOps at FeatureForm , a virtual feature store. Before that, she was building machine learning platforms at MailChimp. And later, an MLOps engineer. Nice to have you here, Miki.

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ML Data Scientist Machine Learning Data Science 52

Becoming Human

MAY 15, 2023

Prerequisite to follow this roadmap of data science There are such prerequisites to follow this roadmap, your dedication of 2–3 hours per day till 6–8 months, where your first 4–6 months cover all your learning part and the last 2–3 months cover your end-to-end projects, resume building, networking, and applying for a job. What to do next?

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AWS Machine Learning Blog

JUNE 26, 2023

This approach ensures that individuals have access only to the resources and actions essential for their tasks, reducing the risk of unauthorized actions or breaches. Machine learning (ML) administrators play a critical role in maintaining the security and integrity of ML workloads.

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The MLOps Blog

MARCH 12, 2024

TL;DR LLMOps involves managing the entire lifecycle of Large Language Models (LLMs), including data and prompt management, model fine-tuning and evaluation, pipeline orchestration, and LLM deployment. This article serves as your comprehensive guide to LLMOps. How LLMOps compares to and diverges from traditional MLOps practices.

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