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. Brasilia onsite live MLflow trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Brasilia - Corporate Financial Center
SCN, Qd2 - Block A - Cj. 503/504 , Brasília, Brazil
The Brasilia Corporate Financial Center is situated in a striking office complex in the Northern Commercial Sector, one of the main business districts. It is close to the Monumental Axis road which divides the city. The Brasilia Corporate Financial Center has an auditorium and cyber café and the complex offers restaurant and gym facilities, a helipad and parking.
Brasilia - Parque Cidade Corporate Building
Edifício Parque Cidade Corporate Brasilia DF , Brasilia, Brazil, 70308-200
Elevate the pace of your business operations at our workspace located in the thriving Parque Cidade Corporate Building in Brasília. Adorned with a striking glass façade, this triple-A-rated high-rise not only offers an impressive exterior but also provides unparalleled networking and growth prospects right at your fingertips.
Welcome your clients to a bright and conducive environment, boasting breathtaking views of Brasília from the rooftop terrace. When it’s time to recharge, unwind amidst the tranquil ambiance of Parque da Cidade, just a short stroll away. And for entertaining your guests, indulge in a variety of bars and restaurants conveniently situated nearby.
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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