Course : MLOps, deploying Machine Learning in production
Practical course - 3d
- 21h00 - Ref. MLW
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 | Understand the various stages in the life of the model and data after the POC |
| Know how to reduce the dimensions of a model to scale |
| Knowing the different production platforms |
| Know how to set up model explicability algorithms |
| Notions of embeddability |
| Knowledge of distributed training of large models |
Intended audience
Engineers, developers, researchers, data scientists, data analysts and anyone who wants to put MLOps into practice.
Prerequisites
Good knowledge of the Python language. Knowledge of machine learning / deep learning. Use of Docker.
Course schedule
1 Life after the PoC (Proof of Concept)
- What is MLOps?
- Cycle de vie de la data.
- An overview of the different production platforms.
- The curse of dimensionality.
- Technical choices for production start-up.
- Presentation of embeddability platforms.
- Continuous integration, deployment and maintenance of models.
Hands-on work
Set up a cloud environment for model deployment. Testing of off-the-shelf APIs. Manage authentication keys and API entry points.
2 Stages in the production of Deep Learning models
- Dimension reduction algorithms (PCA, SVD).
- Pruning. Quantization.
- Low-rank approximation. Binary weight networks.
- Winograd transformation.
- Evaluation of model performance after reduction.
- Explicability of the model with the LIME and SHAP algorithms.
- Presentation of architectures for distributed training of large models.
Tutored hands-on work
Implementation of a Machine Learning model on credit defaults, with explainability. Implementation of pruning on a pre-trained Deep Learning model for object detection.
3 Docker and Kubernetes integration
- Reminders about Docker.
- Put into practice by deploying a model with FastAPI and Docker.
- Introducing Kubernetes.
- Introducing KubeFlow.
- Presentation of the principles of high-volume management and Big Data architectures for model deployment.
- Best production practices.
Hands-on work
Practice deploying a model with Docker.
Customer reviews
4,5 / 5
Customer reviews are based on end-of-course evaluations. The score is calculated from all evaluations within the past year. Only reviews with a textual comment are displayed.
JOSIAS K.
08/06/26
4 / 5
Good content
MATHIEU C.
08/06/26
4 / 5
The course focuses much more on ML than on operational aspects. And I lacked the mathematical knowledge to grasp certain aspects on the first day. The other two days were fine; I learnt quite a lot, particularly about model monitoring.
TIMOTHÉ B.
01/04/26
4 / 5
This course provides a solid overview of current MLOps practices. Key concepts are covered using practical examples and realistic use cases, which makes them easier to understand. The Docker module is particularly well put together; the Kubernetes section could have gone into a bit more practical detail. MLFlow Server could also have benefited from a dedicated practical session to learn how to use it.
Publication date : 02/01/2024
PARTICIPANTS
Engineers, developers, researchers, data scientists, data analysts and anyone who wants to put MLOps into practice.
PREREQUISITES
Good knowledge of the Python language. Knowledge of machine learning / deep learning. Use of Docker.
TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.
ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.
Engineers, developers, researchers, data scientists, data analysts and anyone who wants to put MLOps into practice.
PREREQUISITES
Good knowledge of the Python language. Knowledge of machine learning / deep learning. Use of Docker.
TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.
ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.
TEACHING AIDS AND TECHNICAL RESOURCES
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.
This programme is an original creation, developed by the teaching teams at ORSYS Formation. Any reproduction, representation, adaptation or use, in whole or in part, without the prior written authorisation of ORSYS, is strictly prohibited. ORSYS reserves the right to take any action necessary to protect its intellectual property rights.
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This programme is an original creation, developed by the teaching teams at ORSYS Formation. Any reproduction, representation, adaptation or use, in whole or in part, without the prior written authorisation of ORSYS, is strictly prohibited. ORSYS reserves the right to take any action necessary to protect its intellectual property rights.