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DeepSeek vs. OpenAI: The Battle of Open Reasoning Models

Unite.AI

Both DeepSeek and OpenAI are playing key roles in developing more innovative and more efficient technologies that have the potential to transform industries and change the way AI is utilized in everyday life. The Rise of Open Reasoning Models in AI AI has transformed industries by automating tasks and analyzing data.

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MLOps and the evolution of data science

IBM Journey to AI blog

MLOps, which stands for machine learning operations, uses automation, continuous integration and continuous delivery/deployment (CI/CD) , and machine learning models to streamline the deployment, monitoring and maintenance of the overall machine learning system. How to use ML to automate the refining process into a cyclical ML process.

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Steven Hillion, SVP of Data and AI at Astronomer – Interview Series

Unite.AI

It’s been fascinating to see the shifting role of the data scientist and the software engineer in these last twenty years since machine learning became widespread. Having worn both hats, I am very aware of the importance of the software development lifecycle (especially automation and testing) as applied to machine learning projects.

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Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

A major online media company uses data science to develop personalized content, enhance marketing through targeted ads and continuously update music streams, among other automation decisions. to learn more) In other words, you get the ability to operationalize data science models on any cloud while instilling trust in AI outcomes.

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Introducing the Topic Tracks for ODSC East 2025: Spotlight on Gen AI, AI Agents, LLMs, & More

ODSC - Open Data Science

Generative AI TrackBuild the Future with GenAI Generative AI has captured the worlds attention with tools like ChatGPT, DALL-E, and Stable Diffusion revolutionizing how we create content and automate tasks. AI Engineering TrackBuild Scalable AISystems Learn how to bridge the gap between AI development and software engineering.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics. Automated pipelining and workflow orchestration: Platforms should provide tools for automated pipelining and workflow orchestration, enabling you to define and manage complex ML pipelines.

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A Comprehensive Guide on Deep Learning Engineers

Pickl AI

Collaboratio n: Working with data scientists, software engineers, and other stakeholders to integrate Deep Learning solutions into existing systems. Operational Efficiency Deep Learning can optimise healthcare operations by automating administrative tasks, predicting patient flow, and optimising resource allocation.