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Aditi @ AI Open_Source_Research_Junction

Greetings AI Community 👋 About me

** AWS & AI Research Scientist-Principal Applied AI Product Engineer [Product-Owner] & Enterprise Architect @PepsiCo | IIM-A | Community Member @Landing.AI | AI Research Specialist [Portfolio] | Author | Quantum AI | Mojo | Next JS | 7+ Years of Experience in Fortune 50 Product Companies | **

** Global Top AI Community Member @Landing.AI @MLOPS Community, @Pandas AI, @Full Stack Deep Learning, @humaneai @H2o.ai Generative AI, @Modular & @Cohere AI @hugging Face Research Papers Group @Papers with Code @DAIR.AI ** ** Completed 90+ Online Technical Paid Courses from Udemy & Coursera as I believe in Continuous Learning and Growth Mindset **

** Aditi_Khare_AI_Open_Source_Research_Junction **

** Aditi Open-Source AI Libraries Portfolio**

Thank you so much for visting my Profile & Happy Reading

** Awesome AI Courses Links **
** Freecodecamp - ChatGPT **
** Fast.ai - Stable Diffusion **
** Chip Huyen's Blog on LLMOPS **
** Databricks Dolly 2.0 **
** Microsoft Azure - Well Architecture Framework **

** Open Source AI Research Junction**

OPEN-SOURCE-AI OPEN-SOURCE-AI-SUMMARIES RESOURCE LINKS OPEN-SOURCE-AI-CATEGORY
1. **Pandas AI-cONVERSIONAL AI ** ** Pandas Conversional AI Library.** Github COVERSIONAL AI
2. **NannyML ** ** Post Production Deployment NannyML.** Github PRODUCTION GRADE MACHINE LEARNING
3. **YOLOv5 in PyTorch ** ** YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite.** Github COMPUTER VISION
4. **ModAL ** ** modAL is an active learning framework for Python3, designed with modularity, flexibility and extensibility in mind. Built on top of scikit-learn, it allows you to rapidly create active learning workflows with nearly complete freedom. What is more, you can easily replace parts with your custom built solutions, allowing you to design novel algorithms with ease.** Github PYTHON
5. **Interpret ML ** ** InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions..** Github Interpretable Machine Learning
6. **PiML ** ** PiML is a new Python toolbox for interpretable machine learning model development and validation. Through low-code interface and high-code APIs, PiML supports a growing list of inherently interpretable ML models.** Github MACHINE LEARNING
7. **Flyle Kubernetes ** ** This guide provides an overview of setting up the Kubernetes Operator backend plugin in your Flyte deployment.** Github KUBERNETES-PRODUCTION-GRADE DEPLOYMENTS
8. **RunML ** ** Demystifying MLOps and DataOps with RunML. As MLOps becomes widespread across the globe due to its game-changing features, businesses are struggling to overcome some of the issues such as-One platform for different data/model pipeline,Build scalable and optimized workflows,Baked-in governance and security,Reducing cost to maintain multiple tools,RunML enables companies to healthify MLOps and DataOps through consistent monitoring and providing insightful analytics..** Github MODEL PIPELINES
9. **BentoML - Unified AI Application Framework ** ** Unified AI Application Framework With BentoML, you can easily build AI products with any pre-trained models, ship to production in minutes, and scale with confidence.** Github MODEL PIPELINES
10. **Docker Swarm ** ** Swarm mode is an advanced feature for managing a cluster of Docker daemons.Use Swarm mode if you intend to use Swarm as a production runtime environment.** Github DOCKER-CONTAINERS-PRODUCTION-GRADE DEPLOYMENTS
11. **Kedro ** ** Kedro is an open-source Python framework hosted by the Linux Foundation (LF AI & Data). Kedro uses software engineering best practices to help you build production-ready data science code.** Github PYTHON
12. **Label Studio ** ** The most flexible data labeling platform to fine-tune LLMs, prepare training data or validate AI models.** Github KUBERNETES-PRODUCTION-GRADE DEPLOYMENTS
13. **Imagen ** ** Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. Imagen builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation. Our key discovery is that generic large language models (e.g. T5), pretrained on text-only corpora, are surprisingly effective at encoding text for image synthesis: increasing the size of the language model in Imagen boosts both sample fidelity and image-text alignment much more than increasing the size of the image diffusion model. Imagen achieves a new state-of-the-art FID score of 7.27 on the COCO dataset, without ever training on COCO, and human raters find Imagen samples to be on par with the COCO data itself in image-text alignment. To assess text-to-image models in greater depth, we introduce DrawBench, a comprehensive and challenging benchmark for text-to-image models. With DrawBench, we compare Imagen with recent methods including VQ-GAN+CLIP, Latent Diffusion Models, and DALL-E 2, and find that human raters prefer Imagen over other models in side-by-side comparisons, both in terms of sample quality and image-text alignment.** Github GENERATIVE AI [LLMs]
14. **PowerBI Python ** ** PowerBI Python.** Github POWER BI - BUSINESS INTELLIGENCE
15. **Jupytab ** ** Jupytab allows you to explore in Tableau data which is generated dynamically by a Jupyter Notebook. You can thus create Tableau data sources in a very flexible way using all the power of Python. This is achieved by having Tableau access data through a web server created by Jupytab.** Github JUPYTER NOTEBOOK-TABLEAU
15. **Lux - A Python API for Intelligent Visual Discover ** ** Lux is a Python library that facilitate fast and easy data exploration by automating the visualization and data analysis process. By simply printing out a dataframe in a Jupyter notebook, Lux recommends a set of visualizations highlighting interesting trends and patterns in the dataset. Visualizations are displayed via an interactive widget that enables users to quickly browse through large collections of visualizations and make sense of their data.** Github DATA VISUALIZATION-CONVERSIONAL AI
15. **Apache Superset ** ** Apache Superset is an open-source software application for data exploration and data visualization able to handle data at petabyte scale.** Github BIG DATA ENGINEERING
15. ** Bamboolib - GUI for Pandas ** ** bamboolib is a GUI for pandas DataFrames that enables anyone to work with Python in Jupyter Notebook or JupyterLab.** Github CONVERSIONAL AI
15. **Arize-AI Observability & LLM Evaluation PlatforM ** ** The AI Observability & LLM Evaluation Platform Monitor, troubleshoot, and evaluate your LLMs.** Github GENERATIVE AI
15. **Data Robot-One Unified Platform for Generative and Predictive AI ** ** Data Robot-One Unified Platform for Generative and Predictive AI.** Github GERNERATIVE AI PLATFORM
15. **Galileo ** ** A text-to-UI platform that empowers you to design beyond imagination with speed.** Github AI PLATFORM
**LIST OF COMPUTER VISION LIBRARIES **
** OpenCV **
** SimpleCV **
** TensorFlow **
** Keras **
** Matlab **
** PCL - Point Cloud Library **
** DeepFace **
** NVIDIA CUDA X **
** NVIDIA Performance Primitives **
** BoofCV **
** OpenVIVO **
** PyTorch **
** Albumentations **
** Caffee **
** Detectorn2 **
** Imtutils **
Top 10 Resources for Computer Vision AI Models
** Papers with Code **
** OpenMode Zoo **
** TensorFlow **
** Keras **
** Matlab **
** TensorFlow Hub **
** Media Pipe **
** Awesome Core ML Models **
** NVIDIA Performance Primitives **
** Jetson Zoo **
** Pinto Model Zo0 **
** ONNX Model Zoo **
**20 Types of MLOPS Tools List **
** CICD for Machine Learning **
** Cron Job Monitoring, healthChecksIO **
** Data Exploration - Apache Zeppelni, Bamboolib, Google Colab **
** Jupyter Notebook , Jupyter Lab **
** Data Management - DVC Arkito Blazing SQL, Delta Lake, Dolt, DVC, GitLFS **
** Data Validation - Cerberms, Great Expectations **
** Fetaure Engineering - Feature Tools - TS Fresh **
** Data Visualization - Superset, Tableau, Facet, Dash **
** Feature Store - ButterFree, ByteHub, Feast, Tecton **
** Hyperparameter Tunning - Hyperas, Hyperopt, Kabit, KerasTurner, Optuna **
** Machine Learning Platform - Sagemaker, Kubflow, H2O, MLReef, Algorithms, DataRobot, DAGsHub. **
** Model Interprestablity - Alibi, Cptum, ELi5, InterpretML, Weight & Biases **
** LIME, LUCID, SAGE, SHAP, Skaster. **
** Model Lifecycle - MLFlow, Netpune AI, Comet, Deepshake, Model DB, Weight & Baises **
** Model Serving - BentoML, Tensorflow Serving, KFServing, SeldonCore, Streamlit,TorchService, Gradio, Graphippie, Hydrosphereout. **
** Model Testing & Validation - Deepchecks **
** Optimzation Tools - Dask, Deepspeed, Horovod, Tpot, Ray, Rapids. **
** Simplication Tools for ML - PyCaret, Herninioe, Hydra, Koalas, TuriCreate(apple), Train Generator. **
** Visual Analysis and Debugugging - Aporia, Evidently AI, Yellowricks, Netron, Fiddler, Manifold. **
** Model Interprestablity - Alibi, Cptum, ELi5, InterpretML, Weight & Biases **
** Machine Learning Platform - Sagemaker, Kubflow, H2O, MLReef, Algorithms, DataRobot, DAGsHub. **
** Model Interprestablity - Alibi, Cptum, ELi5, InterpretML, Weight & Biases **
** Machine Learning Platform - Sagemaker, Kubflow, H2O, MLReef, Algorithms, DataRobot, DAGsHub. **
** WorkFlow Tools - MLRun, Flyte, MetaFlow, Ploomber, ZenML, Kedro **
** Lens Deskstop - Open Lens Kubernetes tools to supercharge your kubernetes workflow **
** Minikube **
** Kubebox **
** Kops (K) Clusters **
** Kube-burner **
** Kube-Hunter **
** K9S. **
** Helm **
** Nacos **
** Kaniko **
** Kube-money **
** Teleport **
** Kubespray **
** kubebench **
** Kubetail **
** Kube-state-metrics **