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 Amiens or in NobleProg corporate training centers in Amiens.
NobleProg -- Your Local Training Provider
Amiens, City Center
Centre International Jules Vernes, 72 rue des Jacobins, Amiens, france, 80000
Nearest airport to the training center: Roissy Charles-de-Gaulle International Airport
Nearest train station: Amiens train s...
Nearest airport to the training center: Roissy Charles-de-Gaulle International Airport
Nearest train station: Amiens train station
Nearest parking lot to training center: Parking EFFIA Amiens The Jacobins
Nearby cafés, restaurants and bars: Restaurant L'adresse, Restaurant From's bistrot à Fromages, Bar Tower's Pub
Training can also be organized on your premises in Strasbourg or remotely. The 3rd option is to rent a room specifically for the duration of the course.
Training sessions can be given in English or French. For each session, we organize a coordination call with the trainer and participants to adapt the program to your expectations and level. Training includes theory, discussion, questions/answers and practical exercises.
Remote training is delivered via DaDesktop for training, a software package published by NobleProg that enables participants to have a workstation (VM) set up specifically for the training.
This instructor-led, live training in Amiens (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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