I am an associate lead software engineer with SSE's Cloud Services Engineering division. My primary line of work revolves around AWS, Python and web development. I've been with Cerner for over 6 years now.
Continuously AWS - Enriching your Continuous Experience with the AWS CI ecosystem.
The AWS Service plane of today is not just a set of cloud-centric solutions. It is a full-circle group of core solutions that provide cloud capabilities, surrounded by an outer ring of related applications and solutions that cater to every need of an organizational prime mover - from Developers to SysOps admins.
When it comes to CI/CD, AWS provides a bunch of tools that are designed with other AWS services and solutions in mind. AWS has a full set of Amazon-managed developer tools and DevOps tools that can be leveraged instead of third-party tools such as CircleCI, Jenkins, or others.
This talk explores the Developer, CI and DevOps tools that AWS provides today. We start off with a full-fledged online IDE and take the journey through the process of writing, building and deploying code into AWS services - all the time looking at the differences and advantages you gain by using them.
As part of this talk, we intend to demonstrate methods we have used to deploy applications to AWS cloud using DevOps tools and solutions from AWS. These apps range from simple automation jobs to complex react based projects where CI and CD are of key importance.
Who is this for?
This talk focuses on the functionality and tools available for IP development and deployment teams that are planning to move their apps/Solutions to AWS. This is a talk for people who are interested in learning about the CI ecosystem that AWS has built. Developers using CI tools to build and deploy their code to AWS, or planning to do so in the near future should also attend this talk.
Artificial Intelligence and Machine Learning are terms that are commonly thrown around today. A lot of teams in Cerner are already diving into this and producing interesting results.
How does AWS factor into this? What's Next? This session aims to provide a holistic view of ML, AWS's services and offerings in this field, and how Cerner could leverage the same.
AWS offers some interesting AI-driven solutions that can be readily incorporated into medical applications. From Transcribe an automatic speech recognition service to Rekognition - a video and image analysis service, we will explore them all.
If you are a developer and want to build your own AI/ML capabilities, AWS provides you a platform to do just that - Sagemaker - complete with over 200+ (and growing) examples. Developers can use Amazon SageMaker Model Monitor to detect and remediate concept drift and maintain high quality for your deployed ML models. They can also use Amazon Elastic Inference to attach just the right amount of GPU-powered inference acceleration with no code changes, reducing inference costs by up to 75%.
This talk first sets the stage by providing a base understanding of what AI/ML is, and how, it is being used today by biggies like Amazon, Netflix, and others. Next we deep dive into the available application services layer - a set of ready-to-use solutions that AWS offers that are driven by AI. Finally, we get into a deep dive of Sagemaker - where we delve into a use case that needs some machine learning application to derive tangible results.
Various case studies for AI/ML implementation in AWS will be discussed, along with a few associated with HealthCare. We will also show the implementation of a case study and its advantages in depth. We will demonstrate automatically tracking the inputs, parameters, configurations, and results of multiple iterations as trials and how these trials can be organized into experiments.
The idea of this talk is to provide a clear understanding of how AWS is providing ML capabilities for organizations such as Cerner. The main idea is to help associates understand the vast number of AI/ML services being offered, their cost implications, advantages, and demerits.