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Roadmap for MLOps

Machine Learning Roadmap for 2025. Step-by-step guide to become a Data Scientist. Covers the best free learning resources from Python basics to Deep Learning and MLOps.

Curriculum and roadmap from 0 to Mastery for MLOps. Adding value to your machine learning model by deploying it for people to use it to solve real life problems

MLOps-Roadmap-2023 is a repository that provides a comprehensive and up-to-date guide for anyone who wants to learn and master machine learning operations (MLOps).

AI Engineer Roadmap 2025 – Learn AI, ML, Deep Learning, MLOps, and Big Data with projects & resources.

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This is a github repository that has complete roadmap / guide for a seeker, on which tools and technologies if learnt will lead him/her to become an MLOps expert !! I Hope this helps to you all.

Mi ruta de aprendizaje para convertirme en un Ingeniero ML

MLOps Engineer Roadmap 2026. This repository collects the best 100% FREE video tutorials and official documentation to help you become a production-ready MLOps Engineer.

A complete roadmap to become an AI/LLM Engineer in 2026. Free & paid courses, 5 portfolio projects, interview prep, and resources. For software engineers with Python & ML basics who want to break into AI. Covers Transformers, RAG, fine-tuning, agents, LangChain, MLOps, and deployment.

Interactive DevOps, DevSecOps, and MLOps roadmaps

gliff.ai ROADMAP – a public roadmap for the future development of the gliff.ai privacy-preserving MLOps platform.

A walkthrough on how to implement a MLOps template on your Azure environment using Azure DevOps or Github. It will allow deploying an end-to-end standardized and unified Machine Learning lifecycle scalable across multiple workspaces for Azure MLOPs.

This repository is particularly for practising machine learning operation exercises and learning roadmap to becoming an mlops engineer.

End-to-end machine learning roadmap covering white-box foundations, production ML pipelines, MLOps, and modern GenAI systems.

From zero to ML Engineer — a structured, public roadmap covering foundations, core ML, deep learning, and MLOps. Notes + resources included.

Learning roadmap for AI/ML Engineer roles: 7 phases, study schedules, 100+ resources, progress tracking, interview prep. From math to LLMs, RAG, MLOps, and deployment.

The complete roadmap to becoming an AI Data Engineer in 2026 — tools, frameworks, architecture patterns, learning resources, and interview prep. Vector DBs, RAG, LLM pipelines, MLOps & more.

Structured AI/ML career roadmap with 4 role-based paths. Interactive web app with phases, topics, clickable learning links, and progress tracking. Covers math, ML, deep learning, LLMs, RAG, MLOps, and Big Data.

📊 ML Engineer Transformation: 1,872 learning hours over 24 months Oct 2025 → Sep 2027 | Target: €80-100K Ireland ML role Roadmap: Math → ML Theory → Deep Learning → Production MLOps Building in public | Daily progress tracking

A structured 300-day learning roadmap to master Data Engineering, AI, and Hybrid Cloud (On-Prem + Cloud). This plan covers foundational skills, advanced data processing, security, MLOps, and real-world project implementation to become a Hybrid Data Architect.

A complete 90-Day AI/ML Engineer Roadmap, from zero to pro. Includes daily video lessons, hands-on projects, datasets, and resources for Python, ML, Deep Learning, GenAI, RAG, LangChain, and MLOps. Built for self-learners aiming for 3-4 years of experience in 3 months.

Mlops roadmap

My roadmap to gain knowledge in MLOps

MLOps Learning Roadmap: From Zero to Hybrid Hero

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