JJ Asghar
Developer Advocate for IBM
Austin, Texas, United States
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JJ works as a Developer Advocate representing IBM worldwide. He mainly focuses on open-source AI and OpenShift, trying to help companies and users successfully onboard to the Cloud-Native ecosystem. He’s also known in the DevOps tooling ecosystem and generalized Linux communities. If he isn’t building automation to streamline his work, he’s building the groundwork to do just that.
He lives and grew up in Austin, Texas. A father and husband, trying to learn to balance his natural nerdiness with family life. He enjoys a good strong dark ale, hoppy IPA, some team-building Artemis, and epic Gloomhaven campaigning.
He has recently dove headfirst into Fedora since IBM bought Redhat, but still secretly wants FreeBSD everywhere. He’s always trying to become a better web technology developer, though he normally just uses bash to get the job done.
Awards
Area of Expertise
Topics
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
Enterprise AI and Open Source
I grew up engaging in the open-source ecosystem. I have the advantage of being at IBM, where we build the Enterprise ready AI platform watsonx. With the way our industry is going we need some level of guard rails around the data and models that people are using and building, and IBM is trying to help build that.
Coming to this event series will allow me to speak as someone with decades-long exposure and influence from the open source community while learning to balance the Enterprise ready AI platform creating a unique and engaging viewpoint. We all need to understand how our future will pan out with what’s going on in the AI space, and if we don’t all agree on _how_ to make this successful, we will move to the dystopian futures that our movies predict.
A truly community-based Open Source LLM and what it can do for your business and the open community
What if I told you how we're distributing AI models is fundamentally broken? Most people out there leverage the ChatGPTs of the world and just ask it for some simple tasks leveraging Prompt Engineering. That's all well and good, but when you want to bring AI into your business processes, do you want to give your secret sauce to another company to train their models? Probably not.
Built by IBM and Red Hat, InstructLab is an open source project that lets you control your AI by creating easy-to-use tuning for a base model. Today, we’ll talk about what it can do for you as an engineer and how much it can do to improve your business processes. If you’re thinking that you need a trusted AI system that’s truly open source and lets you track back what is put into the tuned data set, we’ve got you covered.
This talk will prepare you to try it out and learn to use it in 45 minutes or less.
It keeps you in complete control of your AI story code and makes sure your company knowledge is in a place that you find secure. Join us to to gain transparency for the future of open source AI.
Open Source AI and InstructLab
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.
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
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