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Machine Learning Lifecycles
Every successful AI initiative, regardless of size or complexity, follows a repeatable process known as the machine learning (ML) lifecycle. Learn the phases of a typical ML lifecycle and why adopting one is the single most important factor in delivering reliable, ethical, and cost-effective AI solutions.
Course Content
Every successful AI initiative, regardless of size or complexity, follows a repeatable process known as the machine learning (ML) lifecycle. This lifecycle guides teams from the earliest idea to a functioning model that produces measurable business value.
By the end of this course, you will understand not only the phases of a typical ML lifecycle but also why adopting one is the single most important factor in delivering reliable, ethical, and cost-effective AI solutions.
Course Objectives
By completing this lesson, you will be able to:
- Explain the purpose of an ML lifecycle and how it improves project consistency and quality.
- Describe how lifecycle discipline reduces failure risk and ensures business relevance.
- Compare common industry reference models such as CRISP-DM, Team Data Science Process, and MLOps loops.
- Identify the key differences between ML and traditional software lifecycles.
- Explain how lifecycle awareness influences data governance, model reliability, and ongoing operations.
