Awesome Production Machine Learning
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
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Posts
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.
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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.
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Building Generator Pipelines in Python
This article proposes an elegant way to build generator pipelines
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End-to-End ML Pipelines with MLflow: Tracking, Projects & Serving
Definitive tutorial for advanced use of MLflow
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Fine-tune GPT-3 without code
Streamline your OpenAI fine-tuning process via a clean UI
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Forget ChatGPT, Here Are New AI Tools That Will Blow Your Mind
Get more done in less time with these innovative AI tools.
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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.
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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.
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Practical MLOps for better models
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.
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The Annotated Transformer
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.
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The Architecture of a Modern Startup
Hype wave, pragmatic evidence vs the need to move fast
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The Responsible Machine Learning Principles
A practical framework to develop AI responsibly
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Using PyGWalker to Enhance Your Jupyter Notebook EDA Experience
An Introduction to the PyGWalker Library for Easy Data Visualisation
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AGLOVERA - AI Workflows
AI Assistants For Organisations
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