MLOps. Software best practices for building ML solutions

In this session we will talk about MLOPs, which is based on DevOps principles and practices to increase the effectiveness of workflows in Machine Learning projects.


Duration 30 minutes

  • Achieve faster model development and experimentation.
  • Achieve faster deployment of production models.
  • Quality control.
  • Continuous integration and delivery through Azure DevOps.
  • Azure Databricks.
  • MLFlow – Machine Learning Lifecycle Platform.

Finally, we will spend the last few minutes answering any questions that may arise during the session.



Fran Pérez

Software Development Engineer en Plain Concepts

I work as Machine Learning Engineer in Plain Concepts, where I can combine two of my passions: machine learning and software engineering. During last five years, I’ve developed many AI solutions using Python, R … and tons of data. In recent months, I’ve been involved in the development and optimization of Machine Learning pipelines over Databricks platforms.

Previously I was an expert in Microsoft technologies, with 15 years of experience delivering desktop and web applications.

Kevin Albes

Software Development Engineer en Plain Concepts

I am a software developer specialized in Azure, Big Data and Machine Learning.

Currently I also play the role of Delivery Lead, helping my team with the methodology and communication with the client.

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