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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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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Piero Molino on the Impact of Declarative ML
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Frank
- August 15, 2023
- "as a code"
- alternative terms
- animation technologies
- API
- Augmented Reality
- Automation
- common functionality
- companies
- Computer Graphics
- configurable processes
- customizability
- DevOps
- engineers
- feasibility
- frameworks
- Generative AI
- impact
- interactive front-end applications
- limitations
- Machine Learning
- machine learning applications
- owning the stack
- platform
- project timelines
- regulated industries
- reinventing the wheel
- solution
- sports game animation
- Startup
- tool
- working in silos
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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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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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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Technically Speaking (E15): Machine learning model drift & MLOps pipelines
Machine learning at the edge is trendy, but what basics do you need to know? In this episode, Kavitha Prasad from Intel joins Chris Wright to talk about machine learning, MLOps and model drift. They discuss causes of model drift, how MLOps is similar to DevOps, how ML pipelines can help make model development and […]
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MLOps on Databricks: A How-To Guide
As companies roll out ML pervasively, operational concerns become the primary source of complexity. Machine Learning Operations (MLOps) has emerged as a practice to manage this complexity.
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How MLOps was Unified at Microsoft
Moe Steller and Setu Chokshi stop by to talk about MLOps (v2) Fundamentals, MLOps (v2) Approach, Architectures, MLOps (v2) and they’ll demo each new feature.
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How To Detect Silent Failures In ML Models
The webinar will teach you how to detect silent ML model failure without accessing the target data. We will cover the most likely causes for ML failure, like data and concept drift. You’ll learn the tools (both statistical and algorithmic) used in detecting and dealing with these failures, their applications, and their limits. By the […]
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New Video Upload : Thursday Thoughts: #MLOps is more than just a buzzword
Thursday Thoughts: #MLOps is more than just a buzzword Always advancing alliterations!
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