A Practical Guide to Building Monolithic and Microservice Chatbot Applications with Streamlit, Huggingface, and FastAPI — With the advent of OpenAI’s ChatGPT, chatbots are exploding in popularity! Every business seeks ways to incorporate ChatGPT into its customer-facing and internal applications. Further, with open-source chatbots catching up so rapidly that even Google engineers seem to conclude they and OpenAI have “no moat,” there’s never been a better…

Monetize your Machine Learning models using FastAPI, Docker, GCP Cloud Run, and Rapid API in 5 easy steps! — ️The dream of every FIRE (Financial Independence, Retire Early) enthusiast is to earn enough passive income to retire early and sip coconuts on a tropical beach 🏖️ ! In my pursuit of FIRE, I stumbled upon a neat way to monetize my Data Science side projects by deploying models as…

From Jaccard to OpenAI, implement the best NLP algorithm for your semantic textual similarity projects — 🎬 Introduction Natural Language Processing (NLP) has tremendous real-world applications in information extraction, natural language understanding, and natural language generation. Comparing the similarity between natural language texts is essential to many information extraction applications such as Google search, Spotify’s Podcast search, Home Depot’s product search, etc. The semantic textual similarity (STS) problem…

Long live ANNs for their whopping 380X speedup over sklearn’s KNN while delivering 99.3% similar results. — We’re living through an extinction-level event. No, not COVID19. I’m talking about the demise of the popular KNN algorithm that is taught in pretty much every Data Science course! Read on to find out what’s replacing this staple in every Data Scientists' toolkit. KNN Background Finding “K” similar items to any given…

Run semantic search queries on ~640K papers in <50ms using Dask, SBERT SPECTRE, and Milvus Vector database — Introduction One of the best ways to learn any latest Data Science topic is by reading open-sourced research papers on arxiv.org. However, the vast number of research papers can be overwhelming even for the most seasoned researcher to sort through. …

Use domain knowledge to generate large labeled datasets with state-of-the-art NLP Weak Supervision. — 🚧 The challenge of contemporary Machine Learning One of the major bottlenecks for developing modern machine learning (ML) models in real-world applications is the need for substantial amounts of manually-labeled training data [Paper]. For example, the ImageNet dataset consists of over 14Million manually labeled images of various real-world objects. …

Blazing Fast, Highly Scalable Text-to-Image Search with CLIP embeddings and Milvus — Introduction I introduced three simple methods to convert images into embeddings for similarity search applications using state-of-the-art neural networks in my previous post. In this post, let's discuss how we can use those embeddings together with Milvus, one of the most popular open-source vector search databases, to create a production scale…

Easiest ways to perform image-to-image and text-to-image similarity search — A whopping 90% of data created since the dawn of human civilization was produced in the past two years! The rate of data creation continues to increase with the proliferation of digital technologies such as social media and the internet of things (IoT) together with ever-faster wireless communication technologies such…

Find out a name’s likely gender using Natural Language Processing in Tensorflow, and Streamlit. — Choosing a name for your child is one of the most stressful decisions you’ll have to make as a new parent. Especially for a data-driven guy like me, having to decide on a name without any prior data about my child’s character and preferences is a nightmare come true! Since…