Online or onsite, instructor-led live MLflow training courses demonstrate through interactive hands-on practice how to use MLflow for streamlining and managing the machine learning lifecycle.
MLflow training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live MLflow training can be carried out locally on customer premises in Grenoble or in NobleProg corporate training centers in Grenoble.
NobleProg -- Your Local Training Provider
Grenoble, Centre Ville Proche Gare
NobleProg Grenoble, 34 avenue Felix Viallet, Grenoble, france, 38000
Training courses can be organised at our premises in Grenoble, at your premises, or remotely.
Training courses are given in...
Training courses can be organised at our premises in Grenoble, at your premises, or remotely.
Training courses are given in English or French. We organise a coordination meeting with the trainer and the participants to adapt the training program to your expectations. The courses consist of interactive discussions, theoretical parts and practical exercises.
The training is available as "live on-site training" or "live remote training". Live on-site training can be conducted locally at the client's premises in Grenoble or at the NobleProg corporate training centers in Grenoble.
Remote e-learning is conducted using an interactive desktop computer. NobleProg is the publisher of the DaDesktop software, which is presented in a video at https://www.dadesktop.com/
This instructor-led, live training in Grenoble (online or onsite) is aimed at data scientists who wish to go beyond building ML models and optimize the ML model creation, tracking, and deployment process.
By the end of this training, participants will be able to:
Install and configure MLflow and related ML libraries and frameworks.
Appreciate the importance of trackability, reproducability and deployability of an ML model
Deploy ML models to different public clouds, platforms, or on-premise servers.
Scale the ML deployment process to accommodate multiple users collaborating on a project.
Set up a central registry to experiment with, reproduce, and deploy ML models.
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the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
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