MLOps, part-time (5 months) (Titre RNCP)by DataScientest
Practical course - 6d
- 42h00 - Ref. 3ML
|
 | Build, deploy and secure an API. |
| Manage, orchestrate and automate tasks within . |
| Monitor AI models used in production. |
Intended audience
Anyone wishing to learn the MLOps trade.
Prerequisites
Un diplôme ou un titre de niveau bac+3 ainsi qu'une première formation ou expérience en tant que Data Scientist.
Une dérogation est possible sur dossier et test écrit.
Une dérogation est possible sur dossier et test écrit.
Certification
At the end of the course, the teaching team will evaluate the learner's project with a written report and an oral presentation. Validation of the skills developed during MLOps training will enable you to obtain blocks 3 and 4 of the RNCP level 7 certification "Expert en ingénierie de l'intelligence artificielle" issued by ANAPIJ and registered with the RNCP on 09-02-2024 under n°RNCP38587.
Practical details
Digital activities
Online courses and exercises, group masterclasses, question/answer sessions, support classes, e-mail coaching, red thread projects, individualized career coaching, social learning.
Mentoring
An expert trainer accompanies learners throughout their training. He or she regularly discusses the learner's project and provides individual mentoring. Several trainers also lead the various masterclasses (group classes) and answer learners' questions at any time from a dedicated forum. In addition, numerous question-and-answer sessions can be organized to help learners.
Pedagogy and practice
Upon registration, the learner is assigned to a class (dates to be defined at the time of registration) and receives a training schedule. The training course is divided into "Sprint" sessions lasting several weeks on a dedicated theme. Each week, the learner is invited to a time of exchange with the trainer, in the form of a masterclass (group class) or mentoring sessions (individual). For 80% of the time, the learner works independently on the teaching platform. All modules include practical exercises to put into practice the concepts developed in class. Learners are also required to work in pairs or trios on a common theme throughout the course. This will enable them to develop and gain recognition for their skills. In addition, themed events and workshops are regularly offered to enable learners to discover the latest innovations in Data Science. In order to follow the course effectively, we estimate the time required at between 10 and 12 hours per week.
Course schedule
1 Upcoming session dates
- October 2025: Start date 07/10/25
- November 2025: Start date 04/11/25
- December 2025: Start date 02/12/25
2 Linux & Bash
- Linux & Bash programming: Linux systems, using a terminal, Bash scripts.
- Docker: concept, how Docker works, images, communication, data, Dockerfile, DockerHub, docker-compose.
- MLflow: architecture, Tracking, Projects, Models, Registry, Machine Learning project lifecycle.
- Unit testing: Pytest, integration testing, benefits in a development environment.
3 Versioning & Isolation
- DVC & Dagshub: Data Version Control, Dagshub and DVC projects, integration into development environments.
- Jenkins: automation, pipeline, no CI/CD.
- AirFlow: concept, principles and benefits, DAGs, operators, specific operators, DAG monitoring.
4 Deployment & Model Serving
- Bento ML: concepts, deploying Docker containers with BentoML models.
- SQL: queries, join types, nested queries.
- Prometheus & Grafana: benefits, Prometheus Query Language, Dashboard with Grafana, production integration.
5 Scaling & Orchestration Platform
- Kubernetes: deploy and manage containers, orchestrate multiple services and manage scalability.
- ZenML: principles and roles, tracking and managing machine learning experiments, ZenML integration.
- Weight & Biases: functionalities, use with frameworks such as TensorFlow.
- AWS Cloud Practitioner: AWS tools, preparation for AWS Cloud Practitioner certification.
PARTICIPANTS
Anyone wishing to learn the MLOps trade.
PREREQUISITES
Un diplôme ou un titre de niveau bac+3 ainsi qu'une première formation ou expérience en tant que Data Scientist.
Une dérogation est possible sur dossier et test écrit.
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.
Anyone wishing to learn the MLOps trade.
PREREQUISITES
Un diplôme ou un titre de niveau bac+3 ainsi qu'une première formation ou expérience en tant que Data Scientist.
Une dérogation est possible sur dossier et test écrit.
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.
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.