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 Valencia can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Valencian Community
NobleProg Valencia, Plaza Manuel Sanchis Guarner, 10, Valencia, spain, 46004
Valencia is the third most populous city and metropolitan area in Spain, behind Madrid and Barcelona. With a population of mo...
Valencia is the third most populous city and metropolitan area in Spain, behind Madrid and Barcelona. With a population of more than 700,000.
Among its most representative monuments are the Miguelete, the Cathedral, the Towers of Serranos and Quart, the Lonja de la Seda, declared a World Heritage Site by Unesco in 1996, and the City of Arts and Sciences.
In addition to its airport the city of Valencia has lines of rail, tram, metro and buses that circulate throughout the metropolis.
The Hotel Senator Parque Central is located in the heart of Valencia, just a few minutes from the old town. Surrounded by shops, restaurants and entertainment venues, the hotel is well-connected: in front of the property you will find a bus stop that will take you to the beaches (5 km) and Xàtiva station is only a 15-minute walk away.
This instructor-led, live training in Valencia (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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