After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
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.
Feels like what you want to learn?
Start Course for FreeWhat you'll learn
- Assess data quality dimensions, feature engineering methods, and experiment-tracking tools to determine their impact on model performance and reproducibility
- Differentiate the business and technical roles within an MLOps team and match each role to its specific responsibilities across the lifecycle
- Evaluate containerization options, microservices architectures, CI/CD pipelines, and deployment strategies (basic, shadow, canary) to determine the most appropriate production solution for a given scenario
- Identify the three primary phases of the machine-learning lifecycle and the MLOps practices that support continuous, reliable, and efficient workflows in each phase
- Recognize indicators such as data drift, concept drift, and computational metrics that trigger monitoring alerts, retraining actions, and progression through MLOps maturity levels.
Prerequisites
Curriculum
Course outline
1
Introduction to MLOps
First, 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.
2
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.
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.
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.
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MLOps Concepts
Course
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