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. Barranquilla onsite live MLflow trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Barranquilla - Buro 51
Calle 94 #51B-43, Barranquilla, colombia
Buro 51 is a first class mix use complex which incorporates a class “A” office building and a 4 star Movich Hotel. Ground floor offers a variety of retail shops and amenities, and the building offeres unobstructed views of Barranquilla.
The center is located in Buenavista Trade Area, which is the most upscale and exclusive of the city with easy access to main highways to the international airport and port. Offers:
Fully equipped offices, ready to start working
Meeting rooms and videoconferencing studio
Regus IT high speed internet access
Administrative support on request
Major transport link - City/town center
Break-out areas
Elevate your presence with distinction at this prestigious office space situated in an enviable locale nestled between downtown and the Magdalena River. Green Towers boasts proximity to local enterprises and major banks, with convenient access to bus stops for seamless commuting.
This instructor-led, live training in (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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