Remove Experimentation Remove Reference 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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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. Capabilities Beyond Classic Jupyter for End-to-end Experimentation. Auto-scale compute.

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Digital addiction detox: Streamline tech to maximize impact, minimize risks

CIO Business Intelligence

While tech debt refers to shortcuts taken in implementation that need to be addressed later, digital addiction results in the accumulation of poorly vetted, misused, or unnecessary technologies that generate costs and risks. Implement robust testing: As the CrowdStrike incident demonstrated, thorough testing is crucial.

Risk 108
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Bringing an AI Product to Market

O'Reilly on Data

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. Without clarity in metrics, it’s impossible to do meaningful experimentation. Ongoing monitoring of critical metrics is yet another form of experimentation.

Marketing 363
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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

ML apps need to be developed through cycles of experimentation: due to the constant exposure to data, we don’t learn the behavior of ML apps through logical reasoning but through empirical observation. but to reference concrete tooling used today in order to ground what could otherwise be a somewhat abstract exercise.

IT 352
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Introducing Amazon MWAA micro environments for Apache Airflow

AWS Big Data

Customers maintain multiple MWAA environments to separate development stages, optimize resources, manage versions, enhance security, ensure redundancy, customize settings, improve scalability, and facilitate experimentation. Refer to Amazon Managed Workflows for Apache Airflow Pricing for rates and more details.

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Integrate sparse and dense vectors to enhance knowledge retrieval in RAG using Amazon OpenSearch Service

AWS Big Data

Deploy a dense vector model To get more valuable test results, we selected Cohere-embed-multilingual-v3.0 , which is one of several popular models used in production for dense vectors. Experimental data selection For retrieval evaluation, we used to use the datasets from BeIR. How to combine dense and sparse?

Metrics 100