AWS Machine Learning Blog

OCTOBER 16, 2024

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. With over 15 years of experience, he supports customers globally in leveraging AI and ML for innovative solutions and building ML platforms on AWS.

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AWS Machine Learning Blog

DECEMBER 9, 2024

MLflow , a popular open-source tool, helps data scientists organize, track, and analyze ML and generative AI experiments, making it easier to reproduce and compare results. Amazon SageMaker with MLflow is a capability in SageMaker that enables users to create, manage, analyze, and compare their ML experiments seamlessly.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

Use case and model governance plays a crucial role in implementing responsible AI and helps with the reliability, fairness, compliance, and risk management of ML models across use cases in the organization. Keshav Chandak is a Software Engineer at AWS with a focus on the SageMaker Repository Service.

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Pickl AI

JULY 14, 2023

Researchers began addressing the need for Explainable AI (XAI) to make AI systems more understandable and interpretable. Ethical considerations, such as bias mitigation, privacy protection, and responsible AI deployment, gained prominence. The average salary of a ML Engineer per annum is $125,087.

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ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

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The MLOps Blog

JUNE 27, 2023

Collaborative workflows : Dataset storage and versioning tools should support collaborative workflows, allowing multiple users to access and contribute to datasets simultaneously, ensuring efficient collaboration among ML engineers, data scientists, and other stakeholders. Check out the documentation to get started.

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The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. In this comprehensive guide, we’ll explore everything you need to know about machine learning platforms, including: Components that make up an ML platform.

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