--- title: MLOps description: This practice focuses on unifying machine learning system development and operations to standardize the deployment and maintenance of models. Learners will understand how to build automated pipelines for continuous integration, delivery, and monitoring of machine learning workflows. category: programming-tech subcategory: artificial-intelligence difficulty: beginner, intermediate, advanced url: /subject/mlops --- # MLOps This practice focuses on unifying machine learning system development and operations to standardize the deployment and maintenance of models. Learners will understand how to build automated pipelines for continuous integration, delivery, and monitoring of machine learning workflows. ## Available Resources 2 Books • 3 Courses • 3 Websites ## Courses ### 1. LLMOps Builds an end-to-end tuning pipeline on Google Cloud: pulling and transforming training data in BigQuery, running a supervised fine-tuning pipeline with Kubeflow, then deploying and safety-checking a chatbot that answers Python questions. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://lnkd.in/g7bHU37w **Tags:** llmops, fine-tuning, mlops, google-cloud, deployment ### 2. Full Stack Deep Learning Learn MLOps best practices to build, deploy, and scale robust machine learning systems with this comprehensive Full Stack Deep Learning course. **Difficulty:** Intermediate | **Price:** Free **Link:** https://fullstackdeeplearning.com/ **Tags:** mlops, deep-learning, model-deployment, ml-infrastructure, production-ml ### 3. Machine Learning Engineering for Production Opening course of DeepLearning.AI's Machine Learning Engineering for Production specialization, taught by Andrew Ng. Covers the production lifecycle, deployment patterns and monitoring, error analysis and baselines, and the data definition work behind consistent labels. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://www.coursera.org/learn/introduction-to-machine-learning-in-production **Tags:** courses, technology-computer-science, ai-ml ## Websites ### 1. Designing Machine Learning Systems Covers the full lifecycle of production machine learning: framing business problems, data engineering and sampling, feature engineering, model evaluation, deployment, monitoring for distribution shift, and the team structures that keep systems running reliably. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://lnkd.in/dEx8sQJK **Tags:** mlops, production-ml, system-design, data-engineering, model-deployment ### 2. Made With ML Goku Mohandas's free course builds one production machine learning application end to end: system design, data preparation, training with experiment tracking, testing code and models, then serving, CI/CD, and monitoring. Now maintained under Anyscale. **Difficulty:** Beginner | **Price:** Free **Link:** https://madewithml.com/ **Tags:** mlops, production-ml, ci-cd, model-serving, testing, python ### 3. mlops.community MLOps.community is a community-driven hub for learning and practicing MLOps, curating articles, tutorials, talks, case studies, tools, and practical resources from practitioners. It also hosts discussions and events that connect you with ongoing conversations and guidance in the MLOps community. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://mlops.community **Tags:** websites, technology-computer-science, ai-ml ## Books ### 1. Building Machine Learning Pipelines **Author:** Hannes Hapke, Catherine Nelson Hapke and Nelson construct an automated TensorFlow Extended pipeline stage by stage: data ingestion and validation, preprocessing, training, model analysis, and deployment with TensorFlow Serving, then orchestrate the whole thing through Apache Beam, Airflow, and Kubeflow Pipelines. **Difficulty:** Intermediate | **Language:** English | **Price:** Paid **Link:** https://www.amazon.com/dp/1492053198?tag=edmonddante07-20 **Tags:** books, technology-computer-science, ai-ml ### 2. Practical MLOps **Author:** Noah Gift, Alfredo Deza Gift and Deza apply DevOps practice to models: continuous delivery pipelines, containers, and deployment on AWS, Azure, and Google Cloud, plus AutoML, edge devices, and monitoring. Written for engineers moving prototypes into maintained production services. **Difficulty:** Intermediate | **Language:** English | **Price:** Paid **Link:** https://www.amazon.com/dp/1098103017?tag=edmonddante07-20 **Tags:** books, technology-computer-science, ai-ml --- *This content is part of Dantes.io - Your Treasure Map to Knowledge* *Curated by humans at Dantes.io. Personal study use welcome; republishing this curation requires permission (team@dantes.io).* View this page online: https://dantes.io/subject/mlops