MLOps Concepts
Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.
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Course Description
Learn about Machine Learning Operations (MLOps)
Understanding MLOps concepts is essential for any data scientist, engineer, or leader to take machine learning models from a local notebook to a functioning model in production.In this course, you’ll learn what MLOps is, understand the different phases in MLOps processes, and identify different levels of MLOps maturity. After learning about the essential MLOps concepts, you’ll be well-equipped in your journey to implement machine learning continuously, reliably, and efficiently.
Discover How Machine Learning Can be Scaled and Automated
How can we scale our machine learning projects using the minimum time and resources? And how can we automate our processes to reduce the need for manual intervention and improve model performance? These are fundamental Machine Learning questions that MLOps provides the answers to.In this MLOps course, you’ll start by exploring the basics of MLOps, looking at the core features and associated roles. Next, you’ll explore the various phases of the machine learning lifecycle in more detail.
As you progress, you'll also learn about systems and tools to better scale and automate machine learning operations, including feature stores, experiment tracking, CI/CD pipelines, microservices, and containerization. You’ll explore key MLOps concepts, giving you a firmer understanding of their applications.
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Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more- 1
Introduction to MLOps
FreeFirst, you’ll learn about the core features of MLOps. You’ll explore the machine learning lifecycle, its phases, and the roles associated with MLOps processes.
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Design and Development
Next, you’ll learn about the design and development phase in the machine learning lifecycle. You’ll explore added value estimation, data quality, feature stores, and experiment tracking.
MLOps design50 xpDetermine the added value50 xpIdentifying business requirements50 xpKey metrics100 xpData quality and ingestion50 xpWhat is data quality?50 xpData quality dimensions100 xpFeature engineering and the feature store50 xpFeature store50 xpWhen to use a feature store?50 xpExperiment tracking50 xpWhat can we track?50 xpThe order of a machine learning experiment100 xp - 3
Deploying Machine Learning into Production
In this chapter, you’ll dive into the concepts relevant to deploying machine learning into production, such as runtime environments, containerization, CI/CD pipelines, and deployment strategies.
Preparing model for deployment50 xpRuntime environments50 xpContainerization100 xpMachine learning deployment architecture50 xpMicroservice50 xpAPI data flow100 xpCI/CD and deployment strategy50 xpCI/CD pipelines50 xpDeployment strategies100 xpAutomation and scaling50 xpHow components automate and scale100 xpMLOps components50 xp - 4
Maintaining Machine Learning in Production
Finally, you’ll learn about maintaining machine learning in production, with concepts such as statistical and computational monitoring, retraining, different levels of MLOps maturity, and tools that can be used within the machine learning lifecycle to simplify processes.
Monitoring machine learning models50 xpMachine learning monitoring50 xpStatistical vs. computational monitoring100 xpRetraining a machine learning model50 xpExamples of drift100 xpRetraining50 xpLevels of MLOps maturity50 xpMLOps maturity50 xpMLOps maturity levels100 xpMLOps tools50 xpUsing a tool50 xpPick the right tool100 xpRecap: MLOps concepts50 xp
Collaborators
Folkert Stijnman
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ML Engineer
Graduate data scientist with expertise in machine learning and data science. Currently working on a freelance basis for a broad range of companies.
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