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 Denver can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
CO, Denver - DTC Corporate Center III
7900 East Union Avenue Suite 1100, Denver, united states, 80237
The office in Denver Corporate is located at the heart of the Denver Tech Center - the district that is home to many of th...
The office in Denver Corporate is located at the heart of the Denver Tech Center - the district that is home to many of the city's major businesses and corporations. The Rocky Mountains can be seen from these 11th-floor offices, as well as the downtown skyline. The Denver Tech Center itself is strategically located at the junction between Interstates 225 and 25. It's minutes away from Belleview Light Rail station, with hotels and restaurants surrounding it. Its state-of-the-art office facilities come with beautiful mountain views.
This instructor-led, live training in Denver (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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