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Deploying machine learning models
An AI model delivers lasting value only when its operation is controlled. With MLOps, connect deployment, monitoring and maintenance to organise the move into production. Build your ability to coordinate data and operations around models monitored over time.
- Duration
- 3 days 21 hours
- Code
- FC-DML Code
Presentation
Implementing a machine learning model can help address anomalies effectively. A model can only do so when it is in production and actively used by its customers. Model deployment is therefore an important stage in the development of this technology.
This level 2 machine learning course teaches you to develop models with clean code for production use. You will also explore and master several machine learning model deployment approaches.
By the end of this 3-day course, you will be able to deploy your own models independently for production use, making them accessible to others. Preparing data pipelines and applying effective deployment processes are among the steps you will practise through the hands-on activities.
Objectives
After completing the machine learning model deployment course, you will be able to:
- design predictive machine learning models with clean, production-ready code;
- understand and use different deployment solutions to put existing machine learning models into production;
- deploy your own models using an approach suited to an organisation's infrastructure and requirements.
Program
Python for production machine learning
- Introduction to the PyCharm integrated development environment.
- Converting notebook code into Python modules.
- Managing dependencies and programming environments.
- Testing code and machine learning models through test-driven development.
- Using a Python API to make requests.
Putting a machine learning model into production
- Introduction to and use of different deployment systems.
- Creating a machine learning API with FastAPI and Docker.
- Deploying a machine learning model in the cloud.
Delivering an end-to-end team project (labs)
- Build a machine learning model in groups of 2 to 3, from data recovery through to production deployment. Participants choose the use case.
Audience
This course is intended for:
- IT professionals such as data analysts and digital development managers;
- project managers and managers seeking to specialise in data management and learn more about the importance of data;
- IT professionals seeking to learn or develop new skills in Big Data and artificial intelligence technologies.
Prerequisites
To attend the machine learning model deployment course, you should:
- have completed the machine learning fundamentals course.
Teaching and assessment methods
- Initial skills assessment
- Training materials provided to participants
- Continuous assessment throughout the course
- End-of-course feedback questionnaire
- Combination of theory and practical application
- Attendance records
- Post-course follow-up evaluation
- Practical exercises
- Case study
Course highlights
Theoretical and practical training delivered in person or online; learning materials; labs covering real-world use cases; and rapid skills development.
Dates and sessions
Choose the date and delivery format that suit you.
No upcoming sessions are currently available.