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MLOps and DevOps: Why Data Makes It Different

O'Reilly Media

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. Can’t we just fold it into existing DevOps best practices?

DevOps 144
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MLOps and the evolution of data science

IBM Journey to AI blog

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning. How MLOps will be used within the organization.

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Mastering MLOps : The Ultimate Guide to Become a MLOps Engineer in 2024

Unite.AI

Understanding MLOps Before delving into the intricacies of becoming an MLOps Engineer, it's crucial to understand the concept of MLOps itself. ML Experimentation and Development : Implement proof-of-concept models, data engineering, and model engineering. ML Pipeline Automation : Automate model training and validation.

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Accelerating AI/ML development at BMW Group with Amazon SageMaker Studio

Flipboard

With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. Data scientists and ML engineers require capable tooling and sufficient compute for their work.

ML 153
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Principles of MLOps

Heartbeat

Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models. In this article, we’ll learn everything there is to know about these operations and how ML engineers go about performing them. Learn more lessons from the field with Comet experts.

DevOps 96
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How Earth.com and Provectus implemented their MLOps Infrastructure with Amazon SageMaker

AWS Machine Learning Blog

Earth.com’s leadership team recognized the vast potential of EarthSnap and set out to create an application that utilizes the latest deep learning (DL) architectures for computer vision (CV). That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in.

DevOps 115
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How Amazon Music uses SageMaker with NVIDIA to optimize ML training and inference performance and cost

AWS Machine Learning Blog

These two crucial parameters influence the efficiency, speed, and accuracy of training deep learning models. The following figure illustrates an SDK for high-performance deep learning inference. As part of his PhD, he worked on physics-based deep learning for numerical simulations at scale.

ML 116