[SOLD OUT] AI to Production and Its Pitfalls

Purchase your ticket for this workshop for $150

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Key Takeaways

1Learn how to utilize Sklearn’s pipeline class to build reproducible machine learning pipelines

2Tracking machine learning experiments with Mlflow

3Deploy a machine learning model to production utilizing FastApi and Docker

4Track the performance, inputs and outputs of a model in production

During the workshop, we will learn how to maintain a machine learning project end to end. We will show you common problems we have seen when deploying machine learning applications,

  • Shipping models consistently to production
  • Not ensuring data sanity upon requests
  • Tracking inputs and outputs to our model
  • Input value shift over time
  • Previously unknown categories

The workshop will be run in a provided AWS environment. This allows us to work with no prior setup of your local machines. We will anyhow give you all the tools and guides to set it up locally, so you can apply all the knowledge later in whatever environment you have available.


Jendrik Jördening

Head of Data Science @Nooxit

Jendrik is Head of Data Science at Nooxit. He formerly worked at Aurubis and Akka Germany on Data Science and Deep Learning in the field of industry 4.0 and autonomous machines.

At the same time he took part in the Udacity Self-Driving Car Nanodegree, participating with a group of other Udacity student in the Self-Racing Cars event at the Thunderhill race-track in California.

There, the group of students taught a car to drive around every turn of the race track autonomously. 

Find Jendrik Jördening at:

Friday Nov 13 / 09:00AM PST (3 hours )



TOPICS Artificial Intelligence ADD TO CALENDAR Add to calendar

Proficiency in Python
Prior experience with Sklearn and Machine Learning
A computer with a browser

3 weeks of live software engineering content designed around your schedule.

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