11 skills found
MelihGulum / Comprehensive Data Science AI Project PortfolioA curated collection of AI, data engineering, and DevOps projects featuring real-world applications, advanced techniques, and tutorials—ideal for learners and practitioners exploring data science and machine learning.
shahadot786 / Complete Full Stack RoadmapThe Ultimate Learning Hub: Master Frontend, Backend, Mobile, DevOps, Data Science, AI/ML, and everything in between. Your one-stop repository for becoming a complete tech professional.
jay-johnson / Sci PypeA Machine Learning API with native redis caching and export + import using S3. Analyze entire datasets using an API for building, training, testing, analyzing, extracting, importing, and archiving. This repository can run from a docker container or from the repository.
marvinbuss / MLDevOpsML DevOps using GitHub Actions and Azure Machine Learning
rickfarmer / Data Science VmA Big Data Analytics VM for doing Data Science. It provides a huge kickstart to those working with the Big Data Analytics side of Data Science. Essentially, this project automates the creation of the Big Data Scientist's toolbox on a virtual machine (VM). In a few minutes one can begin working with a fully configured data science lab instead of performing the complex installations and configuration required for a functioning development environment. The Data Scientist's VM includes R, Git, Python, Cloudera, Hadoop, YARN, MRv2, Mahout, MongoDB, Spark, Neo4j, etc. pre-installed. The Data Scientist's Toolbox VM is automatically built for you on a single CentOS VM using the Vagrant DevOps tool with Chef and shell-scripts for VMware Fusion.
rohanmistry231 / Tech World RoadmapsYour ultimate guide to mastering tech in 2025! This repo offers structured roadmaps for AI/ML, Blockchain, Cloud Computing, Cyber Security, Data Science, DevOps/SRE, Game Development, IoT, Quantum Computing, and Software Engineering.
SQLShark / DataScienceDevopsHello. This is the repo for all the content relating to my session "Deploy models faster with Data Science DevOps". If you have attend this session before then you have my thanks. If you have any questions, or you need support. Please let me know. I am on a personal mission to educate Data Scientists on best-practices from engineering. When I interview Data Scientists I will ask them about a model they built which had a benefit. I will then ask how they deployed it. This is often met with black looks and hushed mentioned that either the model was never deployed or it was deployed by the data engineering team. Heed my advice learn how to deploy a model . It is not enough to be able to write models anymore. The unicorns are on the rise!
sjaydhar / ZabbixdatacollectorOpen Source Zabbix API data collector service with Kafka Integration
chronicle17 / DSDevOpsRepository for Data Science DevOps Workshop
amitvkulkarni / Bring DevOps To Machine Learning With CMLLeveraging the powerful features of DevOps like CI/CD, automation, workflows and apply them to our data science projects & experiments with MLOps. The CML – Continuous Machine Learning is a very handy tool have for tracking the experiment results, collaborate with others, and automating the entire workflow.
EticaAI / Eticaai InfrastructureQuick notes on part of what is done in the Etica.AI project infrastructure
