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. Malaga onsite live MLflow trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Malaga City Center
NobleProg Málaga, Street Cortina del Muelle, 1, Malaga, spain, 29015
Malaga, Andalusian city with more than 3,000 years of history, from its Phoenician origins to the beautiful and cosmopolitan ...
Malaga, Andalusian city with more than 3,000 years of history, from its Phoenician origins to the beautiful and cosmopolitan city that loves citizens from all over the world. Located in the south of the Iberian Peninsula, it is characterized by having multiple geographical sectors, such as Its abundant beaches or its high mountains.
The airport is connected to the city center and with the Málaga-María Zambrano Station by the Cercanías railway line and by the A Express of the EMT, which also has a stop inside the Bus Station, where you can link to the destination To other Spanish cities and the Costa del Sol.
This instructor-led, live training in Malaga (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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