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docs/ai.md
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# Artificial Intelligence
- [hipertextual.com: Diferencias entre Inteligencia Artificial, Machine Learning y Deep Learning](https://hipertextual.com/2023/02/diferencias-ia-machine-learning)
1. [Introduction](#introduction)
2. [The MAD (ML/AI/Data) Landscape](#the-mad-mlaidata-landscape)
## Introduction
- [==guru99.com: Artificial Intelligence Tutorial for Beginners: Learn Basics of AI== 🌟🌟🌟](https://www.guru99.com/ai-tutorial.html)
- [==technologyreview.com: Andrew Ng: Forget about building an AI-first business. Start with a mission== 🌟](https://www.technologyreview.com/2021/03/26/1021258/ai-pioneer-andrew-ng-machine-learning-business) An AI pioneer reflects on how companies can use machine learning to transform their operations and solve critical problems.
- [==technologyreview.es: "Las empresas que empiezan a lo grande con la IA fracasan más"== 🌟](https://www.technologyreview.es/s/13258/las-empresas-que-empiezan-lo-grande-con-la-ia-fracasan-mas) El pionero de la inteligencia artificial Andrew Ng asegura que es más importante tener buenos datos, aunque sean escasos, que muchos, pero mal etiquetados. Cree que todas las empresas deben empezar a pensar en la tecnología con proyectos rápidos, pero pequeños, y escalarlos si resulta que funcionan.
- [cio.com: Make Better AI Infrastructure Decisions: Why Hybrid Cloud is a Solid Fit 🌟](https://www.cio.com/article/350337/make-better-ai-infrastructure-decisions-why-hybrid-cloud-is-a-solid-fit.html) The unique demands of AI workloads drive increasing popularity of pairing on-premises infrastructure with cloud.
- [hipertextual.com: Diferencias entre Inteligencia Artificial, Machine Learning y Deep Learning](https://hipertextual.com/2023/02/diferencias-ia-machine-learning)
## The MAD (ML/AI/Data) Landscape
- [mad.firstmark.com: The MAD (ML/AI/Data) Landscape](https://mad.firstmark.com/)
docs/mlops.md
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# Machine Learning Ops (MLOps) and Data Science
1. [Introduction. MLOps](#introduction-mlops)
2. [The MAD (ML/AI/Data) Landscape](#the-mad-mlaidata-landscape)
3. [Object Detection Libraries](#object-detection-libraries)
4. [MLFlow](#mlflow)
5. [Kubeflow](#kubeflow)
6. [Flyte](#flyte)
7. [Azure ML](#azure-ml)
8. [KServe Cloud Native Model Server](#kserve-cloud-native-model-server)
9. [Data Science](#data-science)
10. [Machine Learning workloads in kubernetes using Nix and NVIDIA](#machine-learning-workloads-in-kubernetes-using-nix-and-nvidia)
11. [Other Tools](#other-tools)
12. [Samples](#samples)
13. [ML Courses](#ml-courses)
14. [ML Competitions and Challenges](#ml-competitions-and-challenges)
15. [Polls](#polls)
16. [Tweets](#tweets)
2. [Object Detection Libraries](#object-detection-libraries)
3. [MLFlow](#mlflow)
4. [Kubeflow](#kubeflow)
5. [Flyte](#flyte)
6. [Azure ML](#azure-ml)
7. [KServe Cloud Native Model Server](#kserve-cloud-native-model-server)
8. [Data Science](#data-science)
9. [Machine Learning workloads in kubernetes using Nix and NVIDIA](#machine-learning-workloads-in-kubernetes-using-nix-and-nvidia)
10. [Other Tools](#other-tools)
11. [Samples](#samples)
12. [ML Courses](#ml-courses)
13. [ML Competitions and Challenges](#ml-competitions-and-challenges)
14. [Polls](#polls)
15. [Tweets](#tweets)
## Introduction. MLOps
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- [==mikeroyal/Kubernetes-Guide: Machine Learning== 🌟](https://github.com/mikeroyal/Kubernetes-Guide/blob/main/README.md#machine-learning)
- [medium.com/workday-engineering: Implementing a Fully Automated Sharding Strategy on Kubernetes for Multi-tenanted Machine Learning Applications](https://medium.com/workday-engineering/implementing-a-fully-automated-sharding-strategy-on-kubernetes-for-multi-tenanted-machine-learning-4371c48122ae)
- [==medium.com/globant: Advantages of Deploying Machine Learning models with Kubernetes== 🌟](https://medium.com/globant/advantages-of-deploying-machine-learning-models-with-kubernetes-8454cc7c565e)
- [==technologyreview.com: Andrew Ng: Forget about building an AI-first business. Start with a mission== 🌟](https://www.technologyreview.com/2021/03/26/1021258/ai-pioneer-andrew-ng-machine-learning-business) An AI pioneer reflects on how companies can use machine learning to transform their operations and solve critical problems.
- [==technologyreview.es: "Las empresas que empiezan a lo grande con la IA fracasan más"== 🌟](https://www.technologyreview.es/s/13258/las-empresas-que-empiezan-lo-grande-con-la-ia-fracasan-mas) El pionero de la inteligencia artificial Andrew Ng asegura que es más importante tener buenos datos, aunque sean escasos, que muchos, pero mal etiquetados. Cree que todas las empresas deben empezar a pensar en la tecnología con proyectos rápidos, pero pequeños, y escalarlos si resulta que funcionan.
- [cio.com: Make Better AI Infrastructure Decisions: Why Hybrid Cloud is a Solid Fit 🌟](https://www.cio.com/article/350337/make-better-ai-infrastructure-decisions-why-hybrid-cloud-is-a-solid-fit.html) The unique demands of AI workloads drive increasing popularity of pairing on-premises infrastructure with cloud.
- [medium.com/pythoneers: MLOps: Tool Stack Requirement in Machine Learning Pipeline](https://medium.com/pythoneers/mlops-tool-stack-requirement-in-machine-learning-pipeline-474b39f09dfc) Tools and technologies in machine learning lifecycle
- [medium.com/formaloo: How no-code platforms are democratizing data science and software development 🌟](https://medium.com/formaloo/making-databases-as-easy-as-playing-with-legos-no-code-no-problem-ed41d4fde269)
- [towardsdatascience.com: From Jupyter Notebooks to Real-life: MLOps 🌟](https://towardsdatascience.com/from-jupyter-notebooks-to-real-life-mlops-9f590a7b5faa) Why is it a must-have?
- [==guru99.com: Artificial Intelligence Tutorial for Beginners: Learn Basics of AI== 🌟🌟🌟](https://www.guru99.com/ai-tutorial.html)
- [datarevenue.com: Airflow vs. Luigi vs. Argo vs. MLFlow vs. KubeFlow](https://www.datarevenue.com/en-blog/airflow-vs-luigi-vs-argo-vs-mlflow-vs-kubeflow) Choosing a task orchestration tool
- [infoworld.com: 13 open source projects transforming AI and machine learning](https://www.infoworld.com/article/3673976/13-open-source-projects-transforming-ai-and-machine-learning.html) From deepfakes to natural language processing and more, the open source world is ripe with projects to support software development on the frontiers of artificial intelligence and machine learning.
- [towardsdatascience.com: From Dev to Deployment: An End to End Sentiment Classifier App with MLflow, SageMaker, and Streamlit](https://towardsdatascience.com/from-dev-to-deployment-an-end-to-end-sentiment-classifier-app-with-mlflow-sagemaker-and-119043ea4203) In this tutorial, we’ll build an NLP app starting from DagsHub-MLflow, then diving into deployment in SageMaker and EC2 with the front end in Streamlit.
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- [swirlai.substack.com: SAI #08: Request-Response Model Deployment - The MLOps Way, Spark - Executor Memory Structure and more... 🌟](https://swirlai.substack.com/p/sai-08-request-response-model-deployment)
- [about.gitlab.com: How is AI/ML changing DevOps?](https://about.gitlab.com/blog/2022/11/16/how-is-ai-ml-changing-devops/)
## The MAD (ML/AI/Data) Landscape
- [mad.firstmark.com: The MAD (ML/AI/Data) Landscape](https://mad.firstmark.com/)
## Object Detection Libraries
- [medium.com/mlearning-ai: The Best Object Detection Libraries That I Work With](https://medium.com/mlearning-ai/the-best-object-detection-libraries-that-i-work-with-835428a1e01e)