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MLOps and the evolution of data science

IBM Journey to AI blog

Because ML systems require significant resources and hands-on time from often disparate teams, problems arose from lack of collaboration and simple misunderstandings between data scientists and IT teams about how to build out the best process. How to use ML to automate the refining process into a cyclical ML process.

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Career in Python: Trending Job Roles

Pickl AI

A Software Developer Uses Python: Backend Development : Python finds applications in developing server-side applications and APIs. The developer will use frameworks such as Django and Flask for this. Model Development: Use libraries such as TensorFlow, Keras, PyTorch, scikit-learn, etc.,

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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. What is MLOps? Learn more lessons from the field with Comet experts.

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Top 5 Generative AI Integration Companies to drive Customer Support in 2023

Chatbots Life

10CLOUDS Year Founded : 2009 HQ : Warsaw, Poland Team Size : 51–200 employees Clients : TrustStamp (Identity verification), Emergent Tech (G-Coin), AlephZero (Blockchain), Tapeke (BitCoin Software Development), Tagasauris (Crowdsourcing Software Development), CallerSmart.

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MLOps Is an Extension of DevOps. Not a Fork — My Thoughts on THE MLOPS Paper as an MLOps Startup CEO

The MLOps Blog

Just so you know where I am coming from: I have a heavy software development background (15+ years in software). Lived through the DevOps revolution. Came to ML from software. Founded two successful software services companies. If you’d like a TLDR, here it is: MLOps is an extension of DevOps.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly Media

While there isn’t an authoritative definition for the term, it shares its ethos with its predecessor, the DevOps movement in software engineering: by adopting well-defined processes, modern tooling, and automated workflows, we can streamline the process of moving from development to robust production deployments.

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

AWS Machine Learning Blog

Prior to working at Amazon Music, Siddharth was working at companies like Meta, Walmart Labs, Rakuten on E-Commerce centric ML Problems. Tarun Sharma is a Software Development Manager leading Amazon Music Search Relevance. Siddharth spent early part of his career working with bay area ad-tech startups.

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