Remove Definition Remove Experimentation Remove Testing
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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. Our experimentation platform supports this kind of grouped-experiments analysis, which allows us to see rough summaries of our designed experiments without much work.

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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

encouraging and rewarding) a culture of experimentation across the organization. A business-disruptive ChatGPT implementation definitely fits into this category: focus first on the MVP or MLP. Keep it agile, with short design, develop, test, release, and feedback cycles: keep it lean, and build on incremental changes.

Strategy 290
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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

While there isn’t an authoritative definition for the term, it shares its ethos with its predecessor, the DevOps movement in software engineering: by adopting well-defined processes, modern tooling, and automated workflows, we can streamline the process of moving from development to robust production deployments. This approach is not novel.

IT 351
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What Is DataOps? Definition, Principles, and Benefits

Alation

However, there is a lot more to know about DataOps, as it has its own definition, principles, benefits, and applications in real-life companies today – which we will cover in this article! Automated testing to ensure data quality. Definition, Principles, and Benefits appeared first on Alation. What Is DataOps? Simplicity.

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The DataOps Vendor Landscape, 2021

DataKitchen

Because it is such a new category, both overly narrow and overly broad definitions of DataOps abound. Testing and Data Observability. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Testing and Data Observability. Production Monitoring and Development Testing.

Testing 304
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Practical Skills for The AI Product Manager

O'Reilly on Data

AI PMs should enter feature development and experimentation phases only after deciding what problem they want to solve as precisely as possible, and placing the problem into one of these categories. Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model.

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What high-performance IT teams look like today — and how to build one

CIO Business Intelligence

While the focus at these three levels differ, CIOs should provide a consistent definition of high performance and how it’s measured. A good starting point is Dale Carnegie’s definition of high-performance teams exceeding their yearly goals.

IT 140