Kim Berg
Cloud Solution Architect active in Data, AI & Cloud communitys.
Norrköping, Sweden
Azure focused solution architect that loves to design innovative solutions and spread knowledge by mentoring and lectures.
Started the IT career as an operations technician hosting data centers, but found my love for the cloud around 6 years ago.
Started specializing within Azure and the passion grew.
Currently working with Enterprise scale AI, Machine Learning and IoT solutions and platforms as a part of the national CoE at Sogeti.
Contributor to the open source initiative Enterprise Scale Machine Learning (ESML) and responsible for the solution accelerator SARAH.
Also helping companies improving their strategies around microservices and Kubernetes.
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What can you do with Azure OpenAI?
So you have seen and heard about ChatGPT, DALL·E and other OpenAI service, but what can you actually use them for?
Now that Azure have released their offering with Azure OpenAI it's possible to leverage the different services, integrations that Azure offer with built-in responsible AI and access enterprise-grade Azure security.
This session will show some examples on how you can use this powerful offering and give you ideas on what's possible!
Productionize Machine Learning on Azure
Machine Learning has become more and more used by companies, testing out use-cases and trying to find value. But
It's hard to get Machine Learning projects to production and this is where most companies struggle. The accelerator that I'll show you makes that easier as it's including turnkey MLOps and AutoLake among other things.
I'll get you familiar with what is included in this accelerator that helps you speed up your Machine Learning journey and show you some exemples on how to go from RnD to production.
Scaling AI use cases in Azure
With increasing number of AI services in Azure that also integrates with other Azure services, the question about how to implement this at scale and fit it into the Cloud Adoption Framework for Azure arise.
To answer and solve this question, let's build automation on how to implement Enterprise Scale AI landing zones in Azure. These landing zones will accelerate any kind of AI use case in Azure. Machine Learning, Computer Vision, Generative AI or any of the other capabilities that Azure offer.
For example, if you plan to use Azure OpenAI you also need to deploy other supporting services such as Storage Accounts, Key Vault, Private Endpoints, Azure Application Gateway and Api Management to securely expose your deployments and services and to be able to monitor you models.
So, to make sure you harness the full potential of the Azure AI services, a robust and scalable infrastructure is crucial.
But, the infrastructure is just the first step. We also need to include MLOps, monitoring and other features depending on what the use case is.
Join me on this session and I'll show you my take on this!
Kim Berg
Cloud Solution Architect active in Data, AI & Cloud communitys.
Norrköping, Sweden