Venkata Ravuri's Projects
A system design interview assignment by 24/7.
A collection of ML Models & Scripts.
A collection of AI/ML articles, guides and projects.
A curated list of software design articles, tutorials, engineering blog entries and videos that I come across everyday.
A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure Cognitive Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.
An example app demonstrates Angular.js capabilities
A repisitory of datastructures & algorithms implementation in python language.
A sample application follows Domain-driven Design (DDD), CQRS and Event Sourcing approaches built using Spring Cloud (Microservices).
A sample application based on Domain-driven Design approach, follows CQRS and Event Sourcing patterns.
A fictitious cloud-native e-commerce application using microservices architecture powered by polyglot languages & databases, deployable to Kubernetes & AWS
A centralized configuration repository for e-commerce-microservice-sample
A 5-part series to build a sample e-commerce product catalog search functionality using Elasticsearch & ReactJS
Flexible-Jekyll is a simple and clean theme for Jekyll
A Currency Exchange sample application that showcases JSF, Facelets, Spring, Hibernate integration.
About Health & Lifestyle
This repository is for Kaggle Challenge - TalkingData AdTracking Fraud Detection Challenge Can you detect fraudulent click traffic for mobile app ads? https://www.kaggle.com/c/talkingdata-adtracking-fraud-detection
Kaggle competition Avito demand prediction challenge
This repository includes machine learning artifacts such as articles, source code and more.
Solution for Kaggle Competiton - Home Depot Product Search Relevance, Predict the relevance of search results on homedepot.com
A curated list of Machine Learning Articles, Tutorials, Guides, Notes & Videos :book: :tv:
Material design for AngularJS
A sample app using Memcache & Mongodb in Java
Multi-Modality Evals is an evaulation and benchmarking framework for multi-modality AI/ML models on a large number of different evaluation tasks. Evaluates AI/ML models on metrics such as answer relevancy, summarization, faithfulness, hallucination, toxicity, bias, etc.
A fictitious use case to demonstrate Multichain Blockchain functionality
nextthing assignment
Android + Node.js + MongoDB
Functional data structures in Scala
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent