Suggestions
Sahil Sinha
Product @ QuickBooks - Intuit
Sahil Sinha is a product manager with experience in fintech and AI. He worked at Intuit from August 2020 to August 2022, holding various product management roles including QuickBooks Live, Discovery for Brazil, Money by QuickBooks, and Emerging Markets.1
Prior to Intuit, Sahil:
- Interned at Square as a Data Intern (January - July 2018)1
- Co-founded The Learning Ladder, an early childhood education initiative in India (June 2017 - May 2018)1
- Served as Managing Director at Claremont Consulting Group (August 2016 - May 2020)1
Education:
- Bachelor's degree in Economics and Data Science from Claremont McKenna College (2016-2020)1
After leaving Intuit in August 2022, Sahil pursued entrepreneurial ventures:
- Co-founded Spare Change, a startup that went through Y Combinator23
- Currently working on Lytic, a company focused on monitoring and product analytics for AI applications4
Sahil's career demonstrates a strong interest in product management, fintech, and AI technologies. He left his job at Intuit to pursue startup opportunities, driven by a desire to have a direct impact and participate in the economic activity of startups.4
Highlights
This LORAX does NOT 🙅 speak for the trees 🌲🌳🌴- he speaks for finetuned models everywhere 🔊
LORAX, by Predibase, is an open sourced service (https://lnkd.in/guKYfYXU) that helps AI developers train and host their own suite of small language models. Specifically finetuned models can be more cost-effective and reliable than general purpose models, especially for teams exiting the prototyping stage. But the developer experience of actually training and deploying a finetuned model, has been fiddly and cumbersome thus far. LORAX solves this in a couple major ways 👇
First, a declarative framework for training, deploying, and calling finetuned models. This provides a far smoother developer experience for training, deploying and calling finetuned models, reducing the lines of code you need to train a smaller model from 20+ lines of code to just 3-4.
Second, LORAX makes hosting suites of small models cheaper, faster, and more scalable - all with less developer hassle. All your small models are hosted on a single GPU (more cost-effective than dedicating a resources for each model). LORAX also makes your deployment more memory efficient and scalable (via a strategy they call “tiered weight caching”).
And for folks curious how this looks under the hood 👀
- As the name suggests, LORAX finetunes via LORA.
- For the declarative framework, LORAX is built on Ludwig (https://lnkd.in/gmJHhbju, an open source framework developed by Uber for declarative AI/ML development).
- And for more context on LORAX and Predibase: LoRAX: Serve 1000s of Fine-Tuned LLMs on a Single GPU @ The Linux Foundation https://lnkd.in/g9sKpG_s A more hands on demo: https://lnkd.in/g8Bab697 Blogs n docs: https://lnkd.in/gSPCP32b, https://lnkd.in/gh38ssg2, https://lnkd.in/guKYfYXU
