Nowadays almost everybody is talking about DataScience / Machine Learning, containers, cloud and going serverless.

what if we could combine all those elements? Nice right!? Think of all the possibilities….

* Data Scientists are able to quickly process and iterate over large datasets.

* Data Engineers / Scientists can combine and add pre-trained sets to there current infrastructure.

* Scale when needed.

* Quickly test run trained models.

* Leverage the power of the cloud to train models.

Program:

18:00 food

18:30 talk 1

19:30 short break

19:45 talk 2

20:45 drinks

Talk 1:

Speaker: Rory Sie

In this talk, Rory will demonstrate how to build, train and deploy machine learning algorithms in the AWS cloud using Amazon SageMaker, to:

* provide more transparency

* ease the process of taking algorithms into production.

If time allows, we will show you how to use Lambda and Lambda Layers to pre-process data from S3.

Talk 2:

In this talk Arno will start of with sharing his thoughts about DataScience and Engineering and then do a live demo where he will:

- start containerising a trained model.

- deploy the container with the trained model to a container platform like kubernetes.

- deploy a serverless environment on the same container platform.

- create serverless functions to send and receive data from our trained model.

- create a Single Page Application to visualize our trained model with the data retrieved from the serverless functions.