Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training MLflow Advanced - Master MLOps in Production training are: Advanced Python proficiency, ML frameworks (TensorFlow/PyTorch), MLflow basics, Docker/Kubernetes.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training MLflow Advanced - Master MLOps in Production training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training MLflow Advanced - Master MLOps in Produ... | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training MLflow Advanced - Master MLOps in Production training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training MLflow Advanced - Master MLOps in Production training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training MLflow Advanced - Master MLOps in Production training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training access terms and deadlines

Training objectives

Configure a scalable MLflow server with custom UI

Implement advanced experimental tracking and custom metrics

Manage the Model Registry for versioning and staging

Automate ML pipelines with MLflow and CI/CD

Deploy models in production via MLflow serving

Optimize performance and scaling with MLflow plugins

Analyze and reproduce large-scale experiments

Training program

Module 1Advanced Tracking: MLflow Experiments and Custom Logging (Python, UI)

Dive into configuring an MLflow server, log parameters, metrics and artifacts via Python API, create interactive dashboards with MLflow UI, perform exercises on real datasets to compare runs, generate automated reports, while integrating TensorFlow for smooth and reproducible tracking that boosts your ML iterations.

Module 2Model Registry: Model Versioning and Governance (MLflow Models)

Master the MLflow Model Registry, version PyTorch/TensorFlow models, manage stages (Staging, Production), annotate with metadata, test automated transitions, apply practical exercises on business cases, produce deliverables like secure registries ready for audit, transform your workflows into reliable and scalable processes.

Module 3Pipelines and CI/CD: MLflow Automation (GitHub Actions, Airflow)

Integrate MLflow into CI/CD pipelines, automate tracking and registry with GitHub Actions/Airflow, code scripts for automated triggers, simulate end-to-end deployments on Kubernetes, solve complex exercises with large data, generate deliverables like Dockerized workflows, accelerate your ML deployments by 50% right away.

Module 4Deployment and Scaling: MLflow Serving, Monitoring (Advanced Plugins)

Deploy models via MLflow Serving REST/gRPC, integrate Prometheus monitoring, scale with Kubernetes plugins, optimize latency on real loads, conduct final project on concrete business case, produce performance dashboards and impact reports, master best practices for robust production, propel your ML projects into profitable operations.