Migrating a monolith to Cloud-Native and the stumbling blocks that you don’t know about

So your company has finally decided to move to the Cloud Native ecosystem. You’ve landed on containerization as your first step. You heard that all you needed to do was containerize your first app and then push it to Kubernetes/OpenShift/Nomad, and the cost savings just come. You’ve done this, and well, things have gone not as planned. Some of the tech didn’t do what you expected, and wait, what do you mean our OpEx has gone up?
Simply said: the promise of containerization or migrating to the Cloud Native ecosystem can be a lie if you don’t do your homework. Sadly most companies don’t. In this talk, I’ll explain a few gotchas that a “few” enterprises, in the guise of AsgharLabs, hit moving towards the Cloud Native world, and hopefully, you’ll learn from their mistakes, so you’re trip down this path will be more comfortable and closer to the promise.

Outline
Introductions
What is AsgharLabs and where they started, what they thought they needed to do
Where I came into the conversation to help AsgharLabs
Questions you should ask after getting your app containerized
Where are the architectural advantages and disadvantages?
Are we doubling up on things?
Isn’t automation good here? Why is this thing so complicated now?
Questions you should ask about the cultural shift that will happen
How the economics of the Cloud can differ from your Datacenter
What do you mean our support is now Stack Overflow?
What do you mean our goal is to move away from the CCB?
Some tangible things you can start with to help become more successful
Build that pipeline extension
Collaborate with other teams
Visibility and Monitoring
Conclusion and where you can go from here

Introduction to Leveraging AI for Your Enterprise

With the emerging AI space, for enterprises, you need to know many terms and concepts before venturing into it. In this session, we will start with the foundational terms and experiences you must have to find the positives to leverage WatsonX to win in business. We will also demo some straightforward but valuable examples to help anchor your understanding. Our goal for this talk is for you to walk out of the room with the language (funny, eh?) and understand the typical workflow to start finding places to leverage WatsonX in your enterprise. The first step on this journey is to know the ecosystem and tooling; from there, you can find things to fit your business and get you on the road to success.

## Outline
- Introductions
- What do you need to know to start
- Let’s talk AI
- Consumer
- Business
- Let’s talk AI terms
- Model
- Training
- Types of Models
- sentiment
- summarization
- text generation
- image classification
- How can Enterprises can make this work
- Positives
- Negatives
- Demo
- Sentiment
- Summarization
- Image Classification
- cached
- downloaded
- Conclusions

Open Source AI and Granite

In a world of fast-moving AI adoption, the big players want you to play with their versions of AI. The problem, though, is that their AI is usually built in a way that is closed off from the eyes of our tech community, with little or no oversight for choices and legal grey areas for usage and adoption.

What if I told you there was a way to get the best of both worlds? An AI solution that can be externally verified and trusted legally, and we want you, yes, you, to join us in building a genuinely transparent AI solution.

This is what the Granite and Granite-Code foundational models are. You can read the paper on how the model was initially trained and have IBM's lawyers back up claims made from using Granite or Granite-Code usage. Can your other AI providers say that? Will they give you the design documents on how they built it from the ground up? Or will they put their lawyers behind your usage of their AI? Would you put your business at risk of using something like this when the legal area is so grey and ever-changing?

But that's only a point in time; you also need to add skills and knowledge to the ever-growing AI system, which is where InstructLab comes into play. During this presentation/workshop, we will be showing you why you should care about Open Source AI, teach you how to leverage a purely Open Source AI for a local "co-pilot" like experience, and then help train the Granite foundational model with new knowledge, giving you the skills to help build a genuinely transparent AI.
Join us and learn with us. We want to build a future of transparency and legal protection for AI engineers.

Leveraging Open Source AI Securely and Privately

Honestly, you're probably jealous of those people saying "ChatGPT" is making their lives easier. You may think that leveraging AI is this generation's adoption of the calculator. I'm here to say yes, you are right, but let's be honest: we have no idea how the ChatGPTs of the world are trained or if they are secure.
If you run a technology company, your data is your secret sauce; if your boss found out you were leveraging ChatGPT to do your job, would they be happy? Probably not.
In this 30-90-120-minute workshop, we will explore how LLMs work locally through hands-on activities, such as setting up some local open-source private and free LLMs to help you get closer to the promise of generative AI.
Overall, this workshop will start with some basic installation leveraging Ollama and VS Code or any of the JetBrains platforms (and you'll learn what that means and what the differences are!), then pull in AnythingLLM and/or OpenWebUI to help give you a straightforward chat-like interface to your LLM. We will provide you with the knowledge, tools, some generic prompts, and use cases to gain more confidence in your daily life by leveraging LLMs.
You'll walk out of the workshop with a working LLM that you can engage with no fees or usage, all locally and securely.

I can make this workshop anywhere from 30 mins to 2 hours+ :)