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Experimentation and Testing: A Primer

Occam's Razor

This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). Experimentation and testing help us figure out we are wrong, quickly and repeatedly and if you think about it that is a great thing for our customers, and for our employers.

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The mainframe is dying: Long live the mainframe application!

CIO Business Intelligence

Instead, it’s targeting test and development functions, with the goal of making it easier for enterprises to set up such environments whenever they need them, without having to leave costly excess mainframe capacity sitting idle the rest of the time.

Sales 127
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Try semantic search with the Amazon OpenSearch Service vector engine

AWS Big Data

For the demo, we’re using the Amazon Titan foundation model hosted on Amazon Bedrock for embeddings, with no fine tuning. Amazon OpenSearch Service has long supported both lexical and vector search, since the introduction of its kNN plugin in 2020. With OpenSearch’s Search Comparison Tool , you can compare the different approaches.

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6 best practices to develop a corporate use policy for generative AI

CIO Business Intelligence

In fact, it’s likely your organization has a large number of employees currently experimenting with generative AI, and as this activity moves from experimentation to real-life deployment, it’s important to be proactive before unintended consequences happen. This may include developing training videos and hosting live sessions.

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What’s new with Amazon MWAA support for Apache Airflow version 2.4.3

AWS Big Data

The workflow steps are as follows: The producer DAG makes an API call to a publicly hosted API to retrieve data. Test the feature To test this feature, run the producer DAG. Removal of experimental Smart Sensors. Test the feature Upload the four sample text files from the local data folder to an S3 bucket data folder.

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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. This personalized approach might lead to more effective therapies with fewer side effects.

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What you need to know about product management for AI

O'Reilly on Data

But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. This has serious implications for software testing, versioning, deployment, and other core development processes.