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 Exeter or in NobleProg corporate training centers in Exeter.
NobleProg -- Your Local Training Provider
Exeter
The Senate, Southernhay Gardens, Exeter, united kingdom, EX1 1UG
The business centre is located in Exeter, with excellent transport links that is served by two mainline train stations, Exete...
The business centre is located in Exeter, with excellent transport links that is served by two mainline train stations, Exeter Central and Exeter St Davids, which provide regular rail services to all major UK cities. Exeter has been identified by as one of the top 10 most profitable business locations and the county of Devon is regularly voted as number one in England for quality of life.
The Senate is the finest office building in Exeter and offers the largest available floors. This exceptional grade A building is adjacent to the city’s new Crown Court and only a short stroll from the vibrant heart of the city centre, with its bars, restaurants and recently completed Princesshay shopping centre.
The building provides over 72,000 sqft of high specification office accommodation over 5 floors and incorporates underground secure parking for 72 cars. As you would expect from a building of this calibre, the specification and finish reflect a commitment to the highest standards. Efficient space management is expressed in the flexibility of the floorplates, which can be tailored to the needs of each occupier.
This instructor-led, live training in Exeter (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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