Workshop / Overview

Material used during the workshop is available here:

Applying machine learning in the real world is hard: reproducibility gets lost, datasets are dirty, data flows break down, the context where models operate keeps evolving. In the last 2-3 years, the emerging MLOps paradigm provided a strong push towards more structured and resilient workflows. 

MLOps is about supporting and automating the assessment of model performance, model deployment and the following monitoring. Valuable tools for an effective MLOps process are data version trackers, model registries, feature stores, and experiment trackers.

During the workshop, we will showcase the challenges of “applied” machine learning and the value of MLOps with a practical case study. We will develop a ML model following MLOps best practices, from raw data to production deployment. Then, we will simulate a further iteration of development, resulting in better performance, and we will appreciate how MLOps allows for easy comparison and evolution of models.

AWS will provide the tools to effectively implement MLOps: the workshop is also intended to offer an overview of the main resources of the cloud platform and to show how they can support model development and operation.

Workshop / Outcome

After the workshop, attendees will understand:

  • The main issues in taking ML models to production;
  • MLOps best practices and their applicability;
  • The main tools supporting MLOps on AWS.

Moreover, they will have experienced hands-on the benefits MLOps can bring to the iterative development of ML models.

Workshop / Difficulty

Intermediate level

Workshop / Prerequisites

  • Attendees are required to bring their own laptop.
  • To get the most out of the workshop, an AWS account is useful. The account can be setup during the workshop: a debit or credit card is required.

The workshop is best suited for researchers and practitioners with at least basic experience of time series forecasting and python coding. An elementary understanding of cloud computing is also beneficial. Detailed knowledge of the services provided by AWS is not required. 

Track / Co-organizers

Gabriele Mazzola

Co-founder & Data scientist, xtream

Emanuele Fabbiani

Head of Data Science @ xtream

Marco Paruscio

Data Scientist, xtream

Matteo Moroni

Technical Account Manager, beSharp spa

Marta Peroni

Data Scientist, xtream

Gabriele Orlandi

Data Scientist, xtream

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