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 Swansea or in NobleProg corporate training centers in Swansea.
NobleProg -- Your Local Training Provider
Swansea
Princess House, Princess Way, Swansea, united kingdom, SA1 3LW
The Princess House Centre is located in the heart of Swansea with panoramic views over the city, Swansea Bay and the Gower ar...
The Princess House Centre is located in the heart of Swansea with panoramic views over the city, Swansea Bay and the Gower area of outstanding natural beauty. These offices are in a prime location and have recently undergone a major refurbishment. Swansea is an inspiring place to do business in a superb natural environment. It is Wales' second largest city and the regional shopping, leisure, cultural, education and administrative centre for south west Wales. In recent years, it has experienced dramatic economic growth generated by a structured investment programme. This has transformed the city into a modern economy dominated by the service sector, which is estimated to employ about 86 per cent of the local workforce. Swansea is now home to a wide range of national and international companies in the financial, telecommunications and technology industries and is fast building a reputation as a city of innovation with a significant knowledge-based economy.
This instructor-led, live training in Swansea (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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