MLOps

Databricks MLOps

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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Machine Learning MLOps

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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AI Data Science Red Hat

Open Data Hub – the origin story (part 2)

In part 2 of the Open Data Hub origin story, fellow Red Hatters Steven Huels and Sherard Griffin describe some of the technical challenges and growth of the Open Data Hub AI meta-project, evolving Elastic Search to multiple data discovery technologies. The evolution to a commercial service offering, Red Hat OpenShift Data Science is also […]

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AI Data Red Hat

Open Data Hub – the origin story (part 1)

Fellow Red Hatters Steven Huels and Sherard Griffin describe how the Open Data Hub meta-project grew from solving practical CI/CD build challenges to where it is today – providing an integrated blueprint stitching together over 20 open source AI tools for running large and distributed AI workloads on OpenShift. Part 1 of a 2 part […]

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Databricks TensorFlow

Quick to Production with Minimal MLOps with the Best of Spark and TensorFlow

In this session, learn how we can get deep learning and ml models with TensorFlow into production quickly right after prototyping. Using TensorFlow with big datasets in a distributed setting has been an issue for small teams like ours due to complicated MLOps code, but with what we cover in the talk, we could now […]

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Machine Learning Red Hat

Accelerating MLOps with Kubernetes, CI/CD & GitOps

Artificial intelligence (AI)-powered applications are growing to deliver more mainstream experiences to users. Whether predicting rainfall and its effects on specific terrain or building a video game, AI models use machine learning (ML) to humanize the user experience in virtual or real world applications. Operationalizing these applications with integrated ML capabilities and keeping them up […]

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Data Science

How to Implement Agile Workflows For Analytics and Machine Learning

Sprints, Scrum, Kanban, Stories, Epics, Retrospectives, Extreme Programming, Velocity…Agile’s opaque terminology and practices, plus the zeal of its advocates, can be off-putting to newcomers. Can it even be applied to data science, analytics and machine learning projects? In this talk we provide a gentle introduction to implementing an agile workflow for a data science team. […]

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AI MLOps

High Level MLOps from Microsoft Data Scientists

On this episode of the AI Show, Seth welcomes Microsoft Data Scientists, Spyros Marketos, Davide Fornelli and Samarendra Panda. They will give a high-level intro into MLOps and share some of the lessons they’ve learned working with customers along the way.

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Databricks MLOps

Learn to Use Databricks for the Full ML Lifecycle

Unlike traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In this video, learn how to operationalize ML across the full lifecycle with Databricks Machine Learning.

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Machine Learning MLOps

Productionalizing Machine Learning Solutions

Here’s an interesting post from the Databricks YouTube channel about productionalizing ML. At Intuit, we have deployed 100’s of Machine Learning models in production to solve various problems as below: Cash Flow forecasting Security, risk and fraud Document understanding Connect customers to right agents With so many models in production, it becomes very important to […]

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