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 trainings in Kortrijk can be carried out locally on customer premises or in NobleProg corporate training centers.
You can rent 3 meeting rooms and an indoor terrace in VORK. Coffee, tea and water are always available. Everything is there t...
You can rent 3 meeting rooms and an indoor terrace in VORK. Coffee, tea and water are always available. Everything is there to prepare the meeting rooms yourself.
Would you like a sandwich lunch? Or would you like to combine your meeting with a lunch in the restaurant? It's all possible in VORK!
Everything is available to prepare the meeting rooms yourself. Possibility of lunch (e.g. soup, sandwiches, etc.) or dinner in the restaurant after consultation with the restaurant manager.
Additional facilities: clickshare available, video conference option.
VORK is centrally located in the city and is therefore easily accessible on foot, by bicycle or by public transport. If you come by car, you can park underground or above ground at the following locations:
Underground: Schouwburg parking, Veemarkt parking, K in Kortrijk, Budabrug parking, Houtmarkt parking
Above ground: Station (Conservatoriumplein), Broeltorens (Ijzerkaai), Harbor (Botenkopersstraat), Kortrijk Weide (Nelson Mandelaplein).
The first hour is always free! If you park for longer than one hour, you will have to pay (from €1).
You can also park your car for free at Parking Expo (P+R) and take a free bus from De Lijn to the center. Redeem ticket with the bus driver: free parking + return ticket to the city center.
More information at www.parko.be
This instructor-led, live training in Kortrijk (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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