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 Orleans can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Orléans, Central Station
NobleProg Orléans, 43 Avenue de Paris, Orléans, france, 45000
La Factory offers renovated offices in the heart of the city of Orléans, near the Central Station. Train, tram, buses, and s...
La Factory offers renovated offices in the heart of the city of Orléans, near the Central Station. Train, tram, buses, and shops are near the building (with elevator). There are 300 m² of offices, Factory offers shared offices and individual offices for 1 to 5 people, equipped or not. The offices are very bright (natural light), and have just been renovated in a "design spirit". Services include ADSL Internet via wifi, a large meeting room. All charges are included: electricity, heating, internet, common areas. It is possible to obtain many additional services: direct debit, telephone line, parking, access 24/24 and 7/7 days ... La Factory is also a service for direct debit and rental of the meeting room for external companies Nonresidents. The meeting room (32 m² / 12 seats), like the other offices, has just been renovated. Services include ADSL Internet via wifi and other charges (electricity, heating, cleaning). Many services are also available, on demand: video projector, coffee machine with pods, tea, chocolate, catering service, drinks, conference call service.
This instructor-led, live training in Orleans (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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