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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. Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML.

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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

The development of business intelligence to analyze and extract value from the countless sources of data that we gather at a high scale, brought alongside a bunch of errors and low-quality reports: the disparity of data sources and data types added some more complexity to the data integration process. 3) Artificial Intelligence.

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The unreasonable importance of data preparation

O'Reilly on Data

On the machine learning side, we are entering what Andrei Karpathy, director of AI at Tesla, dubs the Software 2.0 Before you even think about sophisticated modeling, state-of-the-art machine learning, and AI, you need to make sure your data is ready for analysis—this is the realm of data preparation.

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Real-time Data, Machine Learning, and Results: The Evidence Mounts

CIO Business Intelligence

From delightful consumer experiences to attacking fuel costs and carbon emissions in the global supply chain, real-time data and machine learning (ML) work together to power apps that change industries. more machine learning use casesacross the company. more machine learning use casesacross the company.

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RPA and IPA – Their Similarities are Different, but Their Rapid Growth Trajectories are the Same

Rocket-Powered Data Science

Repetition implies that the same steps are repeated many times, for example claims processing or business form completion or invoice processing or invoice submission or more data-specific activities, such as data extraction from documents (such as PDFs), data entry, data validation, and report preparation.

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Beyond the hype: Do you really need an LLM for your data?

CIO Business Intelligence

They can also automate report generation and interpret data nuances that traditional methods might miss. Even basic predictive modeling can be done with lightweight machine learning in Python or R. Weve all seen the demos of ChatGPT, Google Gemini and Microsoft Copilot. Theyre impressive, no doubt.

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Structural Evolutions in Data

O'Reilly on Data

They’d grown tired of learning what is; now they wanted to know what’s next. Stage 2: Machine learning models Hadoop could kind of do ML, thanks to third-party tools. It felt like, almost overnight, all of machine learning took on some kind of neural backend. And it was good.