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 - Automate ML Tracking and Deployment training are: Proficiency in Python, basics in scikit-learn or TensorFlow, knowledge of Machine Learning.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training MLflow - Automate ML Tracking and Deployment training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

Training MLflow - Automate ML Tracking and Deplo... | 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.

Configure your training

Response within 24hNo commitment100% free

8 spots remaining for the next session

FormatParticipantsDateDetails

Which format do you prefer?

Methods, approaches, and teaching resources

The Training MLflow - Automate ML Tracking and Deployment 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 - Automate ML Tracking and Deployment 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 - Automate ML Tracking and Deployment 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

Install and configure MLflow in a production environment

Track experiments and metrics for optimal reproducibility

Manage the Model Registry to version your ML models

Deploy models via MLflow Serving and cloud integrations

Automate CI/CD pipelines with MLflow Projects

Analyze performance via the MLflow UI and advanced APIs

Apply best practices to scale your ML projects

Training program

Module 1MLflow Installation: Tool and UI Configuration (Python, Docker)

Dive into quick MLflow installation, configure tracking servers and intuitive UI, test logging of first experiments on real datasets like Iris, create your first run with hyperparameters, export artifacts via practical exercises, leave with a ready-to-scale ML project environment in the blink of an eye.

Module 2MLflow Experiment Tracking: Metrics, Parameters, Artifacts

Master automatic logging of metrics, parameters, and models, compare runs via interactive dashboards, optimize hyperparameters on real cases like binary classification, generate custom charts, integrate MLflow with Jupyter for a seamless workflow, boost your productivity from the first collaborative exercise.

Module 3MLflow Model Registry: Model Versioning and Staging

Manage model lifecycles in the Registry, transition from Staging to Production, annotate versions with rich metadata, test automated transitions on real projects, integrate with Git for full traceability, produce audit-ready deliverables, transform chaos into professional ML governance.

Module 4MLflow Deployment: Serving, Kubernetes/AWS Integrations

Deploy models as REST APIs via MLflow Serving, scale with Docker and Kubernetes, monitor drift and performance live, integrate with AWS SageMaker or GCP AI Platform, simulate real loads in workshops, generate production-ready endpoints, launch your ML services without frustrating downtime.

Module 5Advanced MLflow Pipelines: Projects, MLOps Best Practices

Automate end-to-end pipelines with MLflow Projects, CI/CD via GitHub Actions, apply MLOps to real enterprise cases like churn prediction, customize plugins and callbacks, review projects in pairs, conclude with a deliverable portfolio, integrate MLflow into your daily stack for immediate gains.