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 Cardiff or in NobleProg corporate training centers in Cardiff.
NobleProg -- Your Local Training Provider
Cardiff
Radisson Blu Hotel, Meridian Gate - Bute Terrace, Cardiff, united kingdom, CF10 2FL
The Radisson Blu Hotel in Cardiff city centre is the perfect hub for your Welsh adventure
Close to several public transpor...
The Radisson Blu Hotel in Cardiff city centre is the perfect hub for your Welsh adventure
Close to several public transportation options, our hotel in Cardiff puts the city centre at your fingertips. Catch a train or a bus at one of the nearby stations, or take the M4 motorway and drive wherever you want to go in Wales and beyond. For those flying into the city, the Cardiff International Airport is just a 30-minute drive from the hotel. You’ll find parking around the hotel at the John Lewis, St David’s II and NCP Pellet Street car parks, plus some parking at the hotel. Enjoy shopping and dining within walking distance of the hotel, and explore the colourful history of this thriving capital city.
The hotel is located on Bute Terrace providing easy access to the M4 at junction 32 only 6 km away.
The central train and bus station is located within a five-minute walk from the hotel.
Cardiff International Airport is located 24 km from the hotel and can be reached by bus, train or taxi.
This instructor-led, live training in Cardiff (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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