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Predibase exits stealth with a low-code platform for building AI models

TechCrunch

-based companies, 44% said that they’ve not hired enough, were too siloed off to be effective and haven’t been given clear roles. As a result, most machine learning tasks in an organization are bottlenecked on an oversubscribed centralized data science team,” Molino told TechCrunch via email.

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Interpreting predictive models with Skater: Unboxing model opacity

O'Reilly Media - Data

Data Scientist Cathy O’Neil has recently written an entire book filled with examples of poor interpretability as a dire warning of the potential social carnage from misunderstood models—e.g., There is also a trade off in balancing a model’s interpretability and its performance.

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Why Reinvent the Wheel? The Challenges of DIY Open Source Analytics Platforms

Cloudera

In their effort to reduce their technology spend, some organizations that leverage open source projects for advanced analytics often consider either building and maintaining their own runtime with the required data processing engines or retaining older, now obsolete, versions of legacy Cloudera runtimes (CDH or HDP).

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7 data trends on our radar

O'Reilly Media - Ideas

Whether you’re a business leader or a practitioner, here are key data trends to watch and explore in the months ahead. Increasing focus on building data culture, organization, and training. The demand for data skills (“the sexiest job of the 21st century”) hasn’t dissipated.

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Should you build or buy generative AI?

CIO

But many organizations are limiting use of public tools while they set policies to source and use generative AI models. In the shaper model, you’re leveraging existing foundational models, off the shelf, but retraining them with your own data.” Every company will be doing that,” he adds. “In

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Supercharge your Airflow Pipelines with the Cloudera Provider Package

Cloudera

Many customers looking at modernizing their pipeline orchestration have turned to Apache Airflow, a flexible and scalable workflow manager for data engineers. Airflow users can avoid writing custom code to connect to a new system, but simply use the off-the-shelf providers. Step 0: Skip if you already have Airflow.

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Build your gen AI–based text-to-SQL application using RAG, powered by Amazon Bedrock (Claude 3 Sonnet and Amazon Titan for embedding)

AWS Machine Learning - AI

However, off-the-shelf LLMs cant be used without some modification. RAG is a framework for building generative AI applications that can make use of enterprise data sources and vector databases to overcome knowledge limitations. This can be overwhelming for nontechnical users who lack proficiency in SQL.