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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

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Robust Experimentation and Testing | Reasons for Failure!

Occam's Razor

Since you're reading a blog on advanced analytics, I'm going to assume that you have been exposed to the magical and amazing awesomeness of experimentation and testing. And yet, chances are you really don’t know anyone directly who uses experimentation as a part of their regular business practice. Wah wah wah waaah.

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Corinium Meets: Quantum Metric Head of Behavioural Research Marina Shapira

Corinium

Ahead of her presentation at CDAO UK, we spoke with Quantum Metric’s Marina Shapira about predictive analytics, why companies should embrace a culture of experimentation how and CAOs and CXOs can work together effectively. And what role should it play in an organization's data and analytics strategy?

Metrics 300
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Bringing an AI Product to Market

O'Reilly on Data

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 361
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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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Do You Need a DataOps Dojo?

DataKitchen

Centralizing analytics helps the organization standardize enterprise-wide measurements and metrics. With a standard metric supported by a centralized technical team, the organization maintains consistency in analytics. Central code repository where all data engineering/science/analytics work can be tracked, reviewed and shared.

Metrics 243
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Achieving cloud excellence and efficiency with cloud maturity models

IBM Big Data Hub

Cloud maturity models are a useful tool for addressing these concerns, grounding organizational cloud strategy and proceeding confidently in cloud adoption with a plan. A successful cloud strategy requires a comprehensive assessment of cloud maturity. Level 3 – Scale: Cloud-native strategy is now the preferred approach.