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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

Transparency and explainability : Making sure that AI systems are transparent, explainable, and accountable. However, explaining why that decision was made requires next-level detailed reports from each affected model component of that AI system. Mitigation strategies : Implementing measures to minimize or eliminate risks.

ML 90
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[Updated] 100+ Top Data Science Interview Questions

Mlearning.ai

Hey guys, in this blog we will see some of the most asked Data Science Interview Questions by interviewers in [year]. Data science has become an integral part of many industries, and as a result, the demand for skilled data scientists is soaring. What is Data Science?

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Alex Ratner, CEO & Co-Founder of Snorkel AI – Interview Series

Unite.AI

In model-centric AI, data scientists or researchers assume the data is static and pour their energy into adjusting model architectures and parameters to achieve better results. When that’s the case, the best way to improve these models is to supply them with more and better data.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking. With built-in components and integration with Google Cloud services, Vertex AI simplifies the end-to-end machine learning process, making it easier for data science teams to build and deploy models at scale.

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Introduction to Graph Neural Networks

Heartbeat

They are as follows: Node-level tasks refer to tasks that concentrate on nodes, such as node classification, node regression, and node clustering. Edge-level tasks , on the other hand, entail edge classification and link prediction. Graph-level tasks involve graph classification, graph regression, and graph matching.

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Falcon 2 11B is now available on Amazon SageMaker JumpStart

AWS Machine Learning Blog

trillion token dataset primarily consisting of web data from RefinedWeb with 11 billion parameters. It’s built on causal decoder-only architecture, making it powerful for auto-regressive tasks. The last tweet (“I love spending time with my family”) is left without a sentiment to prompt the model to generate the classification itself.

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Top 5 Challenges faced by Data Scientists

Pickl AI

Data Science is the process in which collecting, analysing and interpreting large volumes of data helps solve complex business problems. A Data Scientist is responsible for analysing and interpreting the data, ensuring it provides valuable insights that help in decision-making.