Remove Data Processing Remove Experimentation Remove Metrics Remove Testing
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The DataOps Vendor Landscape, 2021

DataKitchen

Testing and Data Observability. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Prefect Technologies — Open-source data engineering platform that builds, tests, and runs data workflows. Testing and Data Observability. Production Monitoring and Development Testing.

Testing 307
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Apply Modern CRM Dashboards & Reports Into Your Business – Examples & Templates

datapine

At its core, CRM dashboard software is a smart vessel for data analytics and business intelligence – digital innovation that hosts a wealth of insightful CRM reports. This most value-driven CRM dashboard and a powerful piece of CRM reporting software host a cohesive mix of visual KPIs. Test, tweak, evolve. Sales Activity.

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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. It similarly codes the query as a vector and then uses a distance metric to find nearby vectors in the multi-dimensional space. With OpenSearch’s Search Comparison Tool , you can compare the different approaches.

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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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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.

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

AWS Big Data

If your updates to a dataset triggers multiple subsequent DAGs, then you can use the Airflow metric max_active_tasks_per_dag to control the parallelism of the consumer DAG and reduce the chance of overloading the system. The workflow steps are as follows: The producer DAG makes an API call to a publicly hosted API to retrieve data.

Testing 100
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Teaching AI to Smell by Using DataRobot

DataRobot

Traditionally, experimentation and observation was the only way to understand the physical-chemical properties of the molecule. To foster innovation in this area, AICrowd hosted a competition to predict the olfactory properties of a molecule. DataRobot also provides per-label metrics so that metrics per class can be compared.

Metrics 52