Amazon Web Services (AWS): Courses and Training
Requirements: basic programming knowledge in R, Python or another language, basic knowledge in Git
In many of our data science projects, a one-off analysis turns into a productive application - for example, a dashboard with daily figures or an API for constantly updated forecasts. In a traditional IT landscape, the application would then be installed and maintained by our customer. Doing so you are dependent on the availability of servers and IT staff. Experience has shown that this often leads to considerable delays in the provision of the applications.
Cloud providers such as Amazon Web Services (AWS) enable us to provide our applications in quick feedback loops, so that we can achieve a seamless transition from an initial analysis to a tailor-made data science product. We can react flexibly to changing requirements and adapt computing capacity, reliability and availability as required.
Data science is a very dynamic environment. This applies to both methods and technologies. The AWS Cloud makes it easy to try out a lot. In this way, resource-intensive algorithms for an application can compete against other methods in a benchmark, or it can simply be tried out which technology is best suited for a particular data science task.
Amazon Web Services (AWS) is the largest and best-known provider in this area, with more than 200 services for a wide variety of tasks. Our AWS training provides an introduction to the basic services of Amazon Web Services. In the AWS training we show how these can be used profitably for typical data science applications. The focus is on:
- AWS EC2: Scalable computing capacity in the cloud
- AWS S3: data storage in the cloud
- AWS IAM: access management from AWS
In the AWS course we will provide a simple application for weather forecasting on an EC2 instance of Amazon Web Services. This includes a process of data collection (ETL), model estimation and prediction. In the AWS training, the focus is not on implementation, but on integrating the various modules into the AWS services. As an outlook, we consider how to gradually transform such an application on Amazon Web Services into a highly available and scalable application. We also provide an overview of additional AWS services such as AWS SageMaker, AWS Glue, ECS and EKS.