AWS Machine Learning Blog

JANUARY 5, 2024

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.

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IBM Journey to AI blog

AUGUST 12, 2024

Whereas AIOps is a comprehensive discipline that includes a variety of analytics and AI initiatives that are aimed at optimizing IT operations, MLOps is specifically concerned with the operational aspects of ML models, promoting efficient deployment, monitoring and maintenance.

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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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Heartbeat

OCTOBER 30, 2023

Once the best model is identified, it is usually deployed in production to make accurate predictions on real-world data (similar to the one on which the model was trained initially). Ideally, the responsibilities of the ML engineering team should be completed once the model is deployed. But this is only sometimes the case.

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AWS Machine Learning Blog

FEBRUARY 27, 2024

Axfood has a structure with multiple decentralized data science teams with different areas of responsibility. Together with a central data platform team, the data science teams bring innovation and digital transformation through AI and ML solutions to the organization.

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AWS Machine Learning Blog

MARCH 1, 2023

Challenges In this section, we discuss challenges around various data sources, data drift caused by internal or external events, and solution reusability. These challenges are typically faced when we implement ML solutions and deploy them into a production environment.

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The MLOps Blog

JUNE 27, 2023

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. Metaflow Metaflow helps data scientists and machine learning engineers build, manage, and deploy data science projects.

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