Kornél Kovács

Kornél Kovács
About

Kornel is a Solutions Architect and Instructor at Datapao, a cutting edge Big Data and Cloud Service company. He is a Microsoft certified Data Engineer and Databricks certified Apache Spark developer. He focuses on Azure Data Services and has a special edge in working with Machine Learning projects on Databricks. Kornel has a proven track record in Python Programming, Machine Learning, and Courseware Development. He likes to combine technical knowledge with business insight to create value in a data rich world.


Workshop
MLOps best practices with MLflow // ONLINE on 2,9 December 2020

Level: General

!!!!!! The workshop is going to be held ONLINE on 2 afternoons: 1st session is on 2 December, the 2nd session is on 9 December. On both days we start at 13.30 and finish around 17.00 UTC+1 !!!!!

Tracking and productionizing Machine Learning models has never been more important than today. As part of the Data Engineering / Data Science work, you will need to track ML experiments, manage the lifecycle of your models, productionize your models and serve them. MLflow, an open source Model Management tool is here to help you with all of these operations. In this class you will be able to acquire hands-on knowledge about MLOps and MLflow. 

Abstract:

This hands-on course is about the management of Machine Learning models.

As you develop your ML applications, several questions arise:


  • How do I track experiments in the research phase?
  • How do I publish my models?
  • How can a Data Scientist hand over a model to a Data Engineer?
  • How can you productionize a model and track versions?
  • What’s the lifecycle of a machine learning model and how can I track and manage it?
  • How can I serve a trained model either through my own application or through docker?


In this course, you will learn about these topics. We are taking a look at the general MLOps tasks and use a popular open-source tool, MLflow for tackling the problems that arise when you develop and productionize your machine learning models.This course is completely hands-on and all the code will be distributed to participants in a self-contained plug-and-play to work format.

Prerequisites:


  • Basic Python Knowledge
  • Firefox or Chrome web browser with unrestricted internet access


Who is this class for?

Data Engineers, Data Scientists, BI Analysts and Developers

At the end of this course, you will be able to track, manage, productionize and server Machine Learning Models with MLflow. You will also have an understanding of the problems that are faced by conducting these MLOps operations.