Ram Singh
Finds and fixes your real bottlenecks, moving you from AI-curious and AI-stalled to AI-capable.
Nashville, Tennessee, United States
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Ram is a technologist and operator who diagnoses and fixes broken products, organizations, and operations. He assesses the whole system — the technology, data, people, processes, and culture — and fits those parts together to produce simple, trusted, and useful solutions. He is an early advocate for “sovereign AI”: organizations and individuals building, deploying, and governing AI on their own infrastructure, with their own data, models, and talent.
Ram runs 10io, his consulting business and innovation incubator, and is developing coreograf, a federated system to help humans and AI collaborate more effectively on complex, enterprise-scale software. He co-organizes the Artificial Intelligencers and Beerly Functional meetups.
His interest in AI was piqued by childhood exposure to computers, LISP, and classic sci-fi robots like Asimov’s R. Daneel Olivaw, Simak’s Jenkins, and Adams’ Marvin. When he’s not in the guts of a startup or a client's business, you’ll find him jeeping and camping with (Grace) Hopper, a dog.
Area of Expertise
Topics
Leveraging Local LLMs for the Resource-Constrained Data Practitioner
This one-day hands-on-keyboards working session is for data practitioners curious to explore how Large Language Models (LLMs) can be integrated into their workflows, but who have concerns about cost, security, and complexity.
On their own laptops, starting from scratch, practitioners will learn how to:
* Find, install, configure, and manage LLMs on their own devices
* Integrate those LLMs into a working environment
* And, utilize LLMs in a data workflow
They will also learn:
* How LLMs in general work and what they are (and are not) good for
* LLMOps criteria to consider when deciding whether to use open/closed, local/hosted LLMs
* How to make domain-specific data available to their LLMs
* And, “context engineering” techniques to elicit the best possible results from LLMs
Practitioners will leave the session with:
* A functional local LLM working environment set up on their laptops
* Experience using this environment in a simplified but realistic use case
* Sufficient understanding about how to customize this setup to suit their specific/evolving needs
* And, a basis to continue exploring and refining how they employ LLMs to achieve desired data-centric outcomes
[Note to conference organizers: This is an expanded, hand-on version of a ~40 minute presentation providing an overview of the same subjects.]
Not Your Models, Not Your Data? Private Local AI for the Cautious Data Practitioner
Cloud-hosted AI models offer undeniable convenience. But every prompt or API call to a hosted AI model includes the possibility of your company’s "secret sauce" leaking into a provider’s operational or training data sets.
In this session, we will identify the "hidden lifecycles of data" in AI systems, and criteria you can use to determine if those risks or unknowns are acceptable or not. We will also outline a "local-first" AI stack that brings your own models to your data and workflows, for those instances where the risks, costs, or unknowns of using hosted models are not acceptable.
Your takeaways will include:
* The Risk Surface: Understanding the vulnerabilities that can arise by bringing your data and workflows to hosted AI models.
* The Decision Framework: Criteria for balancing privacy, cost, and performance to determine when you should stay local and when you may need hosted models.
* The Local Stack: A curated guide to discovering, deploying, and operating your own AI models and tools.
The Bottom Line: Treat your data and workflows like the valuable, protected assets that they are. Deploying a local AI stack gives you modern AI capabilities on your own terms and in your own infrastructure.
Ram Singh
Finds and fixes your real bottlenecks, moving you from AI-curious and AI-stalled to AI-capable.
Nashville, Tennessee, United States
Actions
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