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 Manchester can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Manchester
Oxford Street, Manchester, united kingdom, M1 5AN
DIRECTIONS
The office and training rooms are located under a 7 minute walk from Manchester Oxford Road Railway Sta...
DIRECTIONS
The office and training rooms are located under a 7 minute walk from Manchester Oxford Road Railway Station and a 10 minute walk from Deansgate Railway Station.
Peter House business center occupies a landmark building at a prominent central location in Manchester's established and vibrant business district, with city views overlooking Barbirolli Square and St. Peter's Square. A stunning central atrium fills the ground floor and reception with an abundance of natural light. The building is conveniently located with car parks, transport links and retail and leisure facilities on the doorstep. Recently ranked as the second best place to do business in the UK, the city is the UK's fifth largest financial center outside London. Manchester has a growing economy with many people (90,000) employed in financial services. It is also a centre for life sciences, professional services, creative, cultural and media, communications and manufacturing. The centre has 12 meeting rooms and conference suites.
This instructor-led, live training in Manchester (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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