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Our stack: React (front-end), Node.js (API backend), Docker & Kubernetes for deployments, AI/LLM APIs. We spend half a day a week reading documentation and doing nothing else, and another full day coaching the team on cutting edge tech (how to design, build, train, deploy ai models).
In today’s highly competitive market, performing data analytics using machine learning (ML) models has become a necessity for organizations. It enables them to unlock the value of their data, identify trends, patterns, and predictions, and differentiate themselves from their competitors.
Data contains information, and information can be used to predict future behaviors, from the buying habits of customers to securities returns. Businesses are seeking a competitive advantage by being able to use the data they hold, apply it to their unique understanding of their business domain, and then generate actionable insights from it.
This is a post with code that builds a benchmark for our What's Up, Docs? The goal of this competition is to build a computer program that will summarize long English documents for us. LLMs are big and can't be run on most regular-person computers, so the biggest and best are mostly available as APIs that cost money to use.
Its the effort to build engineering structures within the Game of Life cellular automaton. In the end, we can think of the set of things that we can in principle engineer as being laid out in a kind of metaengineering space, much as we can think of mathematical theorems we can prove as being laid out in metamathematical space.
To address these gaps and maximize their utility in specialized scenarios, fine-tuning with domain-specific data is essential to boost accuracy and relevance. On the other end of the spectrum, the larger Llama-3.2-11B SageMaker JumpStart allows for full customization of pre-trained models to suit specific use cases using your own data.
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