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. Cancun onsite live MLflow trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Cancun, Punta Cancun ICC
Cancun International Convention Center, Blvd. Kukulcan Km. 9, Cancun, mexico, 77500
Work in the heart of "Punta Cancún", the main business area of the city and within the International Convention Center, wher...
Work in the heart of "Punta Cancún", the main business area of the city and within the International Convention Center, where large events and business shows are held.
This modern business center is strategically located in a busy area, surrounded by banks, hotels, restaurants, shops and a wide variety of services.
Fully equipped offices, ready to start working when you enter
Business lounge to work while traveling
Easily booked meeting rooms
Regus IT High Speed Internet Access
Secure parking
Administrative support on request
This instructor-led, live training in Cancun (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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