diff --git a/docs/ai.md b/docs/ai.md index 5df3c955..ed66ab55 100644 --- a/docs/ai.md +++ b/docs/ai.md @@ -1,3 +1,16 @@ # Artificial Intelligence -- [hipertextual.com: Diferencias entre Inteligencia Artificial, Machine Learning y Deep Learning](https://hipertextual.com/2023/02/diferencias-ia-machine-learning) \ No newline at end of file +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/) \ No newline at end of file diff --git a/docs/mlops.md b/docs/mlops.md index 9cd4b421..362d23cd 100644 --- a/docs/mlops.md +++ b/docs/mlops.md @@ -1,21 +1,20 @@ # 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 @@ -39,13 +38,9 @@ - [==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. @@ -54,10 +49,6 @@ - [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)