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Why you should care about debugging machine learning models

O'Reilly on Data

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Not least is the broadening realization that ML models can fail.

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The DataOps Vendor Landscape, 2021

DataKitchen

Testing and Data Observability. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. .

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Introducing Cloudera Fine Tuning Studio for Training, Evaluating, and Deploying LLMs with Cloudera AI

Cloudera

Hosting Costs : Even if an organization wants to host one of these large generic models in their own data centers, they are often limited to the compute resources available for hosting these models. Build and test training and inference prompts. The Need for Fine Tuning Fine tuning solves these issues. Data Preparation.

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Automating the Automators: Shift Change in the Robot Factory

O'Reilly on Data

” If none of your models performed well, that tells you that your dataset–your choice of raw data, feature selection, and feature engineering–is not amenable to machine learning. All of this leads us to automated machine learning, or autoML. Is autoML the bait for long-term model hosting?

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What you need to know about product management for AI

O'Reilly on Data

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). 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.

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Unlocking near real-time analytics with petabytes of transaction data using Amazon Aurora Zero-ETL integration with Amazon Redshift and dbt Cloud

AWS Big Data

Within seconds of transactional data being written into Amazon Aurora (a fully managed modern relational database service offering performance and high availability at scale), the data is seamlessly made available in Amazon Redshift for analytics and machine learning. Choose Test Connection. Choose Next if the test succeeded.

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Eight Top DataOps Trends for 2022

DataKitchen

Model developers will test for AI bias as part of their pre-deployment testing. Quality test suites will enforce “equity,” like any other performance metric. Continuous testing, monitoring and observability will prevent biased models from deploying or continuing to operate. Companies Commit to Remote.

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