The global market opportunity for machine learning applications is rapidly increasing ~40% year-over-year.

One of the main prerequisites for successful ML- and analytics projects is access to the right data.

Another important prerequisite is effective collaboration, not only between developers, but across the whole enterprise.

We explain how an Enterprise Insights Platform can be used by developers to quickly enhance a Stock Trading Application.

The platform enables developers to quick find and access the required data, to deploy integrated data (vs. data integration on application level) and helps to extend applications with machine learning capabilities.In the demo part of the session we show a hands-on demo of  IBM Cloud Pak for Data. It is based on Kubernetes and many other Open Source projects. The platform offers a high degree of automation in areas like data quality analysis and enrichment, data integration, selection of machine learning algorithms and model deployment into production. We explain how developers can deploy various services like data virtualization, the data catalog, and the Auto AI experiment for simplified data access, data exploration and simplified ML model selection.

If you run your software in the cloud, you might have already done some Serverless programming. Be it either as glue code that connects existing services or for your entire web API.

In developing Serverless, you soon realize that your application's performance -- and subsequently the money you pay for it -- depends a lot on your function's speed and the memory you use. Two factors the Rust programming language is really good at!

In this talk, we are going to look at how to run Serverless workloads in Rust in Azure Functions and AWS Lambda. In doing so, we will see the fundamental differences between both Serverless providers, and what effect this has on your applications!

Artificial Intelligence (AI) in communication with end users is now an integral part of digitized companies. Simple chatbots are often used to answer general questions. Cognitive Assistants that are based on IBM Watson Assistant, however, should be deeply integrated into business processes to deliver real added value and automate your Customer Care services. During the session, you will not only learn the basic principles of Watson Assistant, but also how to connect it with third-party databases and software, such as Jira Software or Hubspot  CRM System through serverless Node.js Cloud Functions and thus integrate it into existing processes. You will be surprised how easy, intuitive and fast you can get started with Watson Assistant!

As we get deeper into Kubernetes yaml files, we see a lot of duplication. Can we move to a higher level that eliminates this duplication? Let's look at Helm, a tool both for templating k8s yaml files and for installing complex infrastructure dependencies as one package. With Helm 3, we now have deeper integration and more security when working with Kubernetes. Join us on this path to a simpler, more repeatable, and more discoverable yaml experience.

Automotive industry strives to build automated driving capabilities both in and outside our production floors. Come learn how we leveraged a combination of autonomous driving features, LiDAR IoT sensors, AI and AWS enhanced our in-plant logistics without a driver. You’ll learn how we enabled this by leveraging open source AI technology such as Computer Vision as well as AWS services like: Amazon MSK, Amazon RDS, Amazon DynamoDB, Amazon Elasticsearch Service, and Amazon EKS to ensure connectivity to the car and plant respectively, through this effort we enabled an efficient manufacturing and logistics process remotely in addition to autonomously driving over 700 vehicles. Optimizing the logistics processes with utilized parking slots, avoiding delays from distribution points and reducing manual work are major benefits of the platform.