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Learn MLOps
Careers that need MLOps
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Best courses to learn MLOps
Databricks ML in Action
This course offers practical skills to build and deploy machine learning solutions on the Databricks platform, covering the entire ML lifecycle from data ingestion to model deployment. You’ll gain hands-on experience with key tools such as MLflow, Vector Search, and AutoML, while mastering the Databricks Lakehouse architecture. This course will equip you with real-world skills to tackle data science challenges using Databricks' state-of-the-art technologies. The course guides learners through hands-on projects that include tasks like streaming, forecasting, image classification, and retrieval
All levels
Advancing Your Career in Production AI
This course prepares you to leverage your advanced skills in model development, optimization, and AI ethics to pursue senior career opportunities. You will learn how to strategically position yourself for roles such as Senior Machine Learning Engineer, MLOps Engineer, and AI Ethics Specialist by building a portfolio that showcases your end-to-end project experience and preparing for advanced technical interviews focused on system design and responsible AI.
All levels
AI Agents and MLOps for Production-Ready AI
This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will gain in-depth knowledge and hands-on experience with AI agents and MLOps, crucial components for developing and deploying production-ready AI solutions. You will begin by exploring various AI agents, including AutoGen, IBM Bee, LangGraph, CrewAI, and AutoGPT. The course provides practical insights on how these frameworks can automate AI workflow
All levels
Production ML with Hugging Face
Learn to deploy ML models to production using the Sovereign Rust Stack—a pure Rust implementation with zero Python runtime dependencies. This hands-on course teaches you to work with three critical model formats (GGUF, SafeTensors, APR), implement MLOps pipelines with CI/CD and observability, and deploy models across GPU, CPU, WebAssembly, and edge targets. Through real-world projects including a Python-to-Rust transpiler (Depyler), browser-based speech recognition (Whisper.apr), and LLM inference benchmarking (Qwen), you'll master format conversion, cryptographic model signing, and performan
All levels
MLOps and responsible AI practices
This course equips you with the essential skills to take generative AI models from development to production. You will learn to implement robust MLOps practices on Azure, including automated CI/CD pipelines, version control, and full lifecycle management for your models. Simultaneously, you will dive into the critical principles of Responsible AI, using Microsoft’s framework to build fair, transparent, and ethical models that you can deploy with confidence.
All levels
LLM Engineer’s Handbook
In this comprehensive course, you will explore the intricate world of Large Language Models (LLMs) and gain the skills to design, train, and deploy them using cutting-edge MLOps practices. LLMs are revolutionizing the AI landscape, and understanding how to develop and manage them is essential for AI professionals. This course is designed to help you not only grasp the core concepts behind LLMs but also give you hands-on experience to build production-grade LLM systems. You'll learn how to create scalable, efficient LLM systems from scratch, focusing on real-world applications that will make y
All levels
Production Governance and MLOps on Databricks
This intermediate course provides a practical, hands-on exploration of Databricks Governance, focusing on the essential tools and workflows for managing and securing your data lakehouse. You will learn to navigate and control access to your data assets using Unity Catalog, the foundation of Databricks governance. The course covers the core hierarchy of metastores, catalogs, schemas, and tables, and teaches you how to manage them programmatically using the Databricks Python SDK, CLI, and VS Code extension. Beyond foundational access control, you will master the skills to implement modern CI/CD
All levels
Building and Scaling ML Pipelines
This course provides an intermediate-level exploration of MLOps, focusing on how machine learning systems are scaled, productionized, and managed across feature engineering, training, orchestration, serving, and deployment. You will examine how modern ML solutions use feature stores, Kubernetes, Kubeflow, distributed training, advanced serving frameworks, progressive release strategies, and inference optimization techniques. Through hands-on demonstrations and practical exercises, you will gain experience building reliable and scalable workflows with industry-standard technologies such as Fe
All levels
Working with Notebooks in Vertex AI
This course is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.
All levels
Launch Your Hybrid Cloud AI Career
Technical expertise alone won't land your next role—you need to communicate your skills clearly and confidently to hiring teams. This course helps you translate your hybrid cloud and AI infrastructure knowledge into a compelling professional narrative for senior-level positions. You'll learn to articulate your experience with cloud architecture, MLOps, container orchestration, and edge infrastructure in the language hiring managers and technical recruiters look for. You'll optimize your resume and LinkedIn profile with industry-relevant keywords, and practice interview scenarios specific to e
All levels