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. Belfast onsite live MLflow trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Belfast
Cromac Square, Belfast, united kingdom, BT2 8LA
The Cromac Street Centre is located in a office development with a three-storey glass-fronted lobby on a prominent corner sit...
The Cromac Street Centre is located in a office development with a three-storey glass-fronted lobby on a prominent corner site in Belfast's city centre.
It is strategically positioned in a major business area alongside some leading financial and IT institutions, at the apex for main arterial routes through the city and close to major entertainment and retail zones.
Belfast has benefited from sustained investment and rapid economic growth and is now a flourishing and confident city where business is prospering, visitor numbers are increasing and more and more people are drawn to the city to live, work, shop, visit and play.
It has one of the most highly qualified workforces in Europe and has seen strong growth in the knowledge sectors, particularly the financial services, biotechnology, environmental and creative industries as well as having rich and varied cultural and social sectors.
The centre has Life Size Video Conferencing available with Polycom 7000HDX video conferencing, screen sharing and up to 10 sites connectivity Bridging ISDN and IP services available.
This instructor-led, live training in Belfast (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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