MLOps

MLOps

[Book club] Designing Machine Learning Systems: Chapter 1

This video is from MLOps Learners. Our book club will meet weekly, 10-11am PT on Saturday, to share learnings from Designing Machine Learning Systems (Chip Huyen). Each week, we’ll be discussing one chapter. One group will present the learnings and how the topics discussed have changed with generative AI. Everyone is welcome to follow along, […]

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AI MLOps Red Hat

AI 101: What’s the difference between DevOps and MLOps?

Prasanth Anbalagan (Senior Principal Technical Marketing Manager for AI) will share the differences and similarities between DevOps and MLOps.

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AI MLOps

How to Navigate MLOps with Third-Party Foundation Models

In fast-moving ecosystem of artificial intelligence (AI) and machine learning (ML), the integration of third-party foundation models into the development pipeline has emerged as a pivotal strategy for accelerating innovation and enhancing model performance. However, effectively incorporating these advanced models into the Machine Learning Operations (MLOps) workflow requires a nuanced understanding of both the technical […]

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AI Generative AI Large Language Models MLOps

LLMOps Demystified: A Deep Dive into Large Language Model Operations

Machine learning operations (MLOps) is an important process to make sure Machine Learning applications remain operational, but before you apply the same process to your large language models (LLM), Martin explains why and how LLMs need to be treated differently and the process known as LLMOps

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AI MLOps Open Source Red Hat

Breaking Barriers: How OpenShift AI Connects MLOps and DevOps

The use of generative AI to create meaningful services that help businesses and people has accelerated over the last year. OpenShift AI bridges the gap between data scientists who are creating the models in the MLOps world and the application developers creating applications in the DevOps world. Learn more: https://red.ht/openshift_ai

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AI DevOps MLOps Red Hat

6 Observability Myths in AIOps Uncovered

Observability and monitoring? Aren’t they the same thing? That’s what a lot of people think, but they’re mistaken. This leads them to misinterpret other monitoring considerations. In this video, IBM VP Chris Farrell takes down six different observability myths one-by-one. Debunking the myths of observability → https://ibm.biz/debunking_myths_of_observability

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AI MLOps

What is AIOps and How is it Different from MLOps?

BAILeY explains AI Ops and what makes it different from ML Ops

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AI Data Data Driven MLOps

Piero Molino on the Impact of Declarative ML

Welcome back to another episode of Data Driven! In today’s episode, we have a special guest joining our hosts Andy Leonard, BAILeY, and Frank La Vigne. We are thrilled to have Piero Molino, an expert in declarative ML, sharing his insights with us. We’ll be diving into the world of generative AI and exploring the […]

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AI Large Language Models MLOps

Foundation Models in the Modern Data Stack

This video is from MLOps.community. // Abstract As Foundation Models (FMs) continue to grow in size, innovations continue to push the boundaries of what these models can do on language and image tasks. This talk describes our work on applying foundation models to structured data tasks like data linkage, cleaning, and querying. We discuss the […]

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Databricks MLOps

Model Serving on the Lakehouse

Learn about model serving from a Databricks Lakehouse in this video is from Databricks. Model Serving is built within the Databricks Lakehouse Platform and integrates with your lakehouse data, offering automatic lineage, governance and monitoring across data, features and model lifecycle. Simplify model deployment, reduce infrastructure overheads and accelerate time to production. With built-in auto-scaling […]

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