Posts

This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, version, scale and secure your production machine learning Link

Build an Etherscan Clone | FULL COURSE | Next.js, Moralis, Nodejs

In this tutorial we’re building a fully functional Etherscan Clone, from start to finish. It will allow users get the current ether price, the latest blocks, the latest transactions, and also display all transactions for a specific wallet address. Link

Building an Efficient Machine Learning API

Learn the techniques we used to build a performant and efficient product categorization endpoint that will be used within our product data pipeline. Link

This article proposes an elegant way to build generator pipelines Link

End-to-End ML Pipelines with MLflow: Tracking, Projects & Serving

Definitive tutorial for advanced use of MLflow Link

Streamline your OpenAI fine-tuning process via a clean UI Link

Forget ChatGPT, Here Are New AI Tools That Will Blow Your Mind

Get more done in less time with these innovative AI tools. Link

Guiding Large Language Models towards Task-Specific Inference — Prompt Design and Soft Prompts

Discover how prompt designs and soft prompts are being used to develop and deploy SOTA models. Link

Industrial Strength DALLE-E:Scaling Complex Large Text & Image Models

Identifying the right tools to enable for high performance machine learning may be overwhelming as the ecosystem continues to grow at break-neck speed. Link

Machine learning operations (MLOps) are often synonymous with large and complex applications, but many MLOps practices help practitioners build better models, regardless of the size. This talk shares best practices for operationalizing a model and practical examples using the open-source MLOps framework vetiver to version, share, deploy, and monitor models. Link

The Transformer has been on a lot of people’s minds over the last year five years. This post presents an annotated version of the paper in the form of a line-by-line implementation. It reorders and deletes some sections from the original paper and adds comments throughout. This document itself is a working notebook, and should be a completely usable implementation. Code is available here. Link