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 training can be carried out locally on customer premises in Bradford or in NobleProg corporate training centers in Bradford.
NobleProg -- Your Local Training Provider
Bradford
Carlisle Business Centre, 60 Carlisle Road, Bradford, united kingdom, BD8 8BD
Carlisle Business Centre is a not for private profit organisation, driven by a dedicated workforce, that's supported by a vol...
Carlisle Business Centre is a not for private profit organisation, driven by a dedicated workforce, that's supported by a voluntary board of directors. We operate as a social enterprise that donates its generated profit to the Action For Community Ltd charity, enabling us to proudly support the local community.
Situated in a former textile mill, sporting a distinctive clock tower, Carlisle Business Centre is a landmark of Manningham. The building was extended and revived in 1991 to provide a runway for budding entrepreneurs to get off the ground.
Get Directions
You can access our main car park from Carlisle Road coming into Conduit Street (BD8 7PD) and then taking a right onto Church Street. Please report to our Customer Services Desk on arrival.
This instructor-led, live training in Bradford (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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