Remove Data Processing Remove Data Science Remove Experimentation Remove Testing
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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. The AGI analyzes the data and identifies a rare genetic mutation linked to a specific disease.

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DataRobot Notebooks: Enhanced Code-First Experience for Rapid AI Experimentation

DataRobot Blog

ML model builders spend a ton of time running multiple experiments in a data science notebook environment before moving the well-tested and robust models from those experiments to a secure, production-grade environment for general consumption. 42% of data scientists are solo practitioners or on teams of five or fewer people.

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The DataOps Vendor Landscape, 2021

DataKitchen

Testing and Data Observability. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Genie — Distributed big data orchestration service by Netflix.

Testing 300
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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. For example, consider a smaller website that is considering adding a video hosting feature to increase engagement on the site. Here, day-of-week is a time-based confounder.

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Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Let’s look through some antidotes. Machine learning model interpretability. Ergo, less interpretable.

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Keys to AI Success for IT Staff

DataRobot Blog

This approach gives freedom to move its AI artifacts around, regardless of whether they are hosted on a major cloud platform or its own on-premise infrastructure. Machine learning operations (MLOps) solutions allow all models to be monitored from a central location, regardless of where they are hosted or deployed.

IT 52
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Advancing AI Cloud with Release 7.2

DataRobot

And for those that do make it past the experimental stage, it typically takes over 18 months for the value to be realized. Even models that are tested for bias during the development can become biased once in production. DataRobot also now has an integrated and cloud-hosted notebook solution from our recent acquisition of Zepl.