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 trainings in San José can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
San Jose - Plaza Roble Las Terrazas
Centro Corporativo Plaza Roble, San José, Costa rica
The San Jose Plaza Roble Las Terrazas Center is situated in one of six buildings connected by covered walkways that make up the secure Plaza Roble complex. With direct access to a major highway intersection, it is easy to get to downtown San Jose, 7km away, and a 15-minute drive to the international airport.
San Jose - Boulevard Dent
6184 Boulevard Dent San Jose San Pedro , San Jose, Costa rica, 11501
Create a fresh headquarters for your company in Costa Rica, featuring fully equipped office facilities at 6184 Boulevard Dent. Dive right into operations in the rapidly expanding capital, situated within the vibrant business hub of The Alameda. Benefit from convenient access to public transportation via bus and train for commuting, as well as the attractions of Downtown San Jose.
This instructor-led, live training in (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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