Introduction to Machine Learning in Production: Free Course

About this course

Study Introduction to Machine Learning in Production through this free English video course from DeepLearning.AI. The classroom brings together all 40 public recordings from the selected source curriculum, totaling approximately 5h 11m. You can watch the teaching sequence, search the lesson list and keep track of the recordings you have completed. The course focuses on the subject named in its title. Its value comes from following the explanations and making your own small practice attempts, with enough time to inspect the result and explain the decisions involved.

Course at a glance

What you’ll learn

• Describe the stages of a production machine-learning system
• Compare model development with operational deployment
• Identify changing-data and monitoring challenges
• Use baselines and error analysis to prioritize improvements
• Explain consistent data definitions and labelling practices
• Connect project scope with measurable operating requirements

Skills you’ll gain

• ML lifecycle
• Deployment planning
• Model monitoring
• Data quality

Course Overview

The 40 public recordings form the original introductory course across its three weeks; not the entire former MLOps specialization.

The source lesson list includes “#1 Machine Learning Engineering for Production (MLOps) Specialization [Course 1, Week 1, Lesson 1]”, “#21 Machine Learning Engineering for Production (MLOps) Specialization [Course 1, Week 2, Lesson 13]”, “#40 Machine Learning Engineering for Production (MLOps) Specialization [Course 1, Week 3, Lesson 16]”. The classroom retains their original titles and positions. Introductions, supplementary explanations and complete-course recordings remain present whenever they are part of the source. A repeated recording is retained at each original position, allowing the course to be checked against the published teaching sequence.

The production lifecycle

Treat the model as one component of a system. Follow the recordings on deployment and changing conditions, and identify the inputs, outputs, dependencies and users that make an application operate in practice.

Modelling priorities

Use the course’s baselines and error-analysis ideas to choose what to improve. Separate a model limitation from a problem in the data or evaluation process, and explain the value of an explicit comparison standard.

Data definition

Examine the teaching examples about data quality and consistency. Write down what a label or observation means, identify ambiguous cases and consider how a shared definition changes the usefulness of the training material.

Scoping and iteration

Connect the technical system with the project’s operating objective. Describe a monitoring and improvement routine, name evidence that would trigger investigation and keep resource requirements tied to the proposed scope.

Who is this for?

This course suits learners who want the specific subject described above and who can work at the stated level. It is also useful as a recorded reference when revisiting a concept or comparing a practice attempt with a demonstrated example. Beginners should give the foundational explanations time before moving to more elaborate applications. Experienced learners can use the lesson names to locate a topic, then inspect the surrounding context before applying a technique.

Before you start

Working knowledge of machine learning, intermediate Python and experience with a learning framework are helpful. This is a conceptual production course; optional software practice should use an isolated environment suited to the recorded examples.

Put the lessons into practice

Write a production plan for a fictional image classifier. Define a baseline, an evaluation dataset, an intended prediction consumer and a monitoring signal. Describe one data-quality issue and one change in operating conditions. Explain how you would investigate each before retraining. Finish with a small decision log showing how evidence would determine whether to improve labels, adjust the model or revise the project scope.

Treat this suggested exercise as independent practice provided by WikiFree. Keep a short working note containing your inputs, assumptions, result and one point you still need to check. Revisit the relevant recording when a step is unclear, then attempt a small variation without following the demonstration line by line. A result you can explain and reproduce is a more useful study outcome than simply recognizing a familiar term. The exercise is not submitted to an instructor for grading.

Certificate / Assessment

Watching these public recordings and using WikiFree completion tracking does not award a certificate, academic credit, an examination voucher or instructor assessment. The video curriculum is the material included here. Separate platform assignments, credentials and paid services are not part of video viewing. Use the practice output to review which concepts you can explain, which steps you can repeat independently and which questions need another look at the teaching.

Frequently asked questions

Provider: DeepLearning.AI. Videos play from the DeepLearning.AI channel on YouTube.