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.
18:30 talk 1
19:30 short break
19:45 talk 2
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.
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.