Workshop Half Day (3 hours) Details

ML Summit Summit 2018
1. - 2. Oktober 2018 | Berlin
Das große Trainingsevent für Python Developer

Christian Petters

en

01 Okt 2018
14:00 - 17:30
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Quickly and easily build, train, and deploy machine learning models at any scale

01 Okt 2018
14:00 - 17:30

Monday, 1. October 2018 | 14:00 - 17:30

The machine learning process often feels a lot harder than it should be to most developers because the process to build and train models, and then deploy them into production is too complicated and too slow.
First, you need to collect and prepare your training data. Then, you need to select which algorithm and framework you’ll use. After deciding on your approach, you need to teach the model how to make predictions by training, which requires a lot of compute. Then, you need to tune the model so it delivers the best possible predictions, which is often a tedious and manual effort. After you’ve developed a fully trained model, you need to integrate the model with your application and deploy this application on infrastructure that will scale. It’s not a surprise that the whole thing feels out of reach for most developers.
This workshop starts with a brief review of the machine learning process, followed by an introduction and deep dive into the individual components of Amazon SageMaker. Amazon SageMaker removes the complexity that holds back developer success with each of the steps mentioned above. As part of the workshop we will train artificial neural networks, get insight into some of the built-in machine learning algorithms of SageMaker that you can use for a variety of problem types, and after successfully training a model, look at options on how to deploy and scale a model as a service.
This workshop is aimed at developers that are new to machine learning, as well as data scientists that continue to be challenged by the operational challenges of the machine learning process. Bring your own laptop with Python and Jupyter Notebook, and (ideally) your own activated AWS account to follow through the examples.

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