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The Training: Mastering MLflow: Management of Experiments and Tracking of Machine Learning Models 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.
To ensure the quality of the Training: Mastering MLflow: Management of Experiments and Tracking of Machine Learning Models training, Learni provides the following 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.
The assessment of skills acquired during the Training: Mastering MLflow: Management of Experiments and Tracking of Machine Learning Models training is carried out through:
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Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Presentation of the Machine Learning project lifecycle and MLOps challenges. Introduction to experimentation, traceability, and reproducibility concepts. Presentation of alternatives. Installation of MLflow locally: architecture, backend choice, initial configuration. First steps with the user interface and command line.
Discovery of the MLflow Tracking module. Code instrumentation to log hyperparameters, metrics, and artifacts. Structuring of experiments and runs. Visualization, filtering, and comparison of results in the UI. Hands-on with Python APIs to automate logging. Best practices for organizing experiments.
Initiation to model packaging with MLflow Projects: project structuring, configuration files, local or remote execution. Model lifecycle management with MLflow Models: logging, versioning, signature management, supported formats (Python, scikit-learn, TensorFlow, PyTorch…). Quick deployment via MLflow Serve and integration with clouds and tools (Azure ML, Sagemaker, Docker…). MLOps workflow automation and feedback.
Target audience
Data scientists, Machine Learning engineers, AI project managers wishing to optimize the management and traceability of their ML experiments
Prerequisites
Basic knowledge of Python and Machine Learning
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