diff --git a/docs/mlops.md b/docs/mlops.md index 6172d6e6..44e1cf83 100644 --- a/docs/mlops.md +++ b/docs/mlops.md @@ -33,7 +33,6 @@ - [cloud.google.com: How to use a machine learning model from a Google Sheet using BigQuery ML](https://cloud.google.com/blog/topics/developers-practitioners/how-use-machine-learning-model-google-sheet-using-bigquery-ml) - [itnext.io: Building ML Componentes on Kubernetes](https://itnext.io/building-ml-componentes-on-kubernetes-fc7e24cb9269) - [towardsdatascience.com: Deploying An ML Model With FastAPI — A Succinct Guide](https://towardsdatascience.com/deploying-an-ml-model-with-fastapi-a-succinct-guide-69eceda27b21) -- [towardsdatascience.com: Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance](https://towardsdatascience.com/production-machine-learning-monitoring-outliers-drift-explainers-statistical-performance-d9b1d02ac158) A practical deep dive on production monitoring architectures for machine learning at scale using real-time metrics, outlier detectors, drift detectors, metrics servers and explainers. - [cloudblogs.microsoft.com: Simple steps to create scalable processes to deploy ML models as microservices](https://cloudblogs.microsoft.com/opensource/2021/07/09/simple-steps-to-create-scalable-processes-to-deploy-ml-models-as-microservices/) - [ML Platform Workshop](https://github.com/aporia-ai/mlplatform-workshop) Example code for a basic ML Platform based on Pulumi, FastAPI, DVC, MLFlow and more - [rubrix](https://github.com/recognai/rubrix) A free and open-source tool to explore, label, and monitor data for NLP projects. @@ -51,7 +50,6 @@ - [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. -- [valuecoders.com: How AI And ML Have Revamped Mobile App Development?](https://www.valuecoders.com/blog/technology-and-apps/how-ai-and-ml-have-revamped-mobile-app-development/) - [elconfidencial.com: La batalla entre Google y Meta que nadie esperaba: revolucionar la biología 🌟](https://www.elconfidencial.com/tecnologia/ciencia/2022-11-18/carrera-google-meta-revolucionar-biologia_3520865/) El sistema AlphaFold de Google revela la estructura en 3D de las proteínas y ya es utilizado por miles de biólogos, pero Meta contraataca con otro algoritmo. ¿Cuál es mejor? - [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/) @@ -74,7 +72,6 @@ - [marvelousmlops.substack.com: Learn Machine Learning and Neural Networks without Frameworks](https://www.freecodecamp.org/news/learn-machine-learning-and-neural-networks-without-frameworks/) - [==seattledataguy.substack.com: Data Engineering Vs Machine Learning Pipelines==](https://seattledataguy.substack.com/p/data-engineering-vs-machine-learning) - [semaphoreci.com: Why Do We Need DevOps for ML Data?](https://semaphoreci.com/blog/devops-ml-data) -- [nannyml.com: Automating post-deployment Data Collection for ML Monitoring](https://www.nannyml.com/blog/sdk-nannyml-data-collection-ml-monitoring) - [aiml.com: Large Language Models Quiz (Medium)](https://aiml.com/quizzes/deep-learning-large-language-models-quiz-medium/) - [==medium.com/@samiullah6799: Different Roles in MLOps==](https://medium.com/@samiullah6799/different-roles-in-mlops-0918de5321a4) - [==dev.to/pavanbelagatti: Deploy Any AI/ML Application On Kubernetes: A Step-by-Step Guide!==](https://dev.to/pavanbelagatti/deploy-any-aiml-application-on-kubernetes-a-step-by-step-guide-2i37) @@ -104,15 +101,12 @@ ## MLFlow - https://mlflow.org -- [towardsdatascience.com: A Beginner-Friendly Introduction to Kubernetes 🌟](https://towardsdatascience.com/a-beginner-friendly-introduction-to-kubernetes-540b5d63b3d7) With a hands-on MLFlow deployment example -- [towardsdatascience.com: Empowering Spark with MLflow](https://towardsdatascience.com/empowering-spark-with-mlflow-58e6eb5d85e8) - [artifacthub.io: mlflow-server](https://artifacthub.io/packages/helm/mlflowserver/mlflow-server) A Helm chart for MLFlow On Kubernetes - [pypi.org/project/airflow-provider-mlflow](https://pypi.org/project/airflow-provider-mlflow/) An Apache Airflow provider to interact with MLflow using Operators and Hooks ## Kubeflow - [kubeflow](https://www.kubeflow.org/) The Machine Learning Toolkit for Kubernetes -- [medium.com: Machine Learning using Kubeflow](https://medium.com/cloud-techies/machine-learning-using-kubeflow-ad7c9f767df0) - [infracloud.io: Machine Learning Orchestration on Kubernetes using Kubeflow](https://www.infracloud.io/blogs/machine-learning-orchestration-kubernetes-kubeflow/) - [blog.devgenius.io: Kubeflow Cloud Deployment (AWS)](https://blog.devgenius.io/kubeflow-cloud-deployment-aws-46f739ccbb32) How do you deploy Kubeflow on AWS? Kubeflow is resource-intensive and deploying it locally means that you might not have enough resources to run your end-to-end machine learning pipeline. In this article you will learn how to deploy Kubeflow in AWS. - [joseprsm.medium.com: How to build Machine Learning models that train themselves](https://joseprsm.medium.com/how-to-build-machine-learning-models-that-train-themselves-bbc87499ca5) @@ -167,7 +161,6 @@ ## KServe Cloud Native Model Server -- [kserve.github.io](https://kserve.github.io/website/0.8/) Highly scalable and standards based Model Inference Platform on Kubernetes for Trusted AI - [thenewstack.io: KServe: A Robust and Extensible Cloud Native Model Server](https://thenewstack.io/kserve-a-robust-and-extensible-cloud-native-model-server/) - [medium.com/bakdata: Scalable Machine Learning with Kafka Streams and KServe](https://medium.com/bakdata/scalable-machine-learning-with-kafka-streams-and-kserve-85308858d867) In this blog post, you'll learn how to use Apache Kafka and Kafka Streams in combination with the KServe inference platform for an easy integration of ML models with data streams @@ -177,7 +170,6 @@ - [analyticsindiamag.com: Is coding necessary to work as a data scientist?](https://analyticsindiamag.com/is-coding-necessary-to-work-as-a-data-scientist/) Non-programmers with a no-coding background can have a glorious career in data science and programming, and coding knowledge is more like a skill and not a criterion. - [redhat.com: Introducing Red Hat OpenShift Data Science](https://www.redhat.com/en/blog/introducing-red-hat-openshift-data-science) - [towardsdatascience.com: From DevOps to MLOPS: Integrate Machine Learning Models using Jenkins and Docker](https://towardsdatascience.com/from-devops-to-mlops-integrate-machine-learning-models-using-jenkins-and-docker-79034dbedf1) How to automate data science code with Jenkins and Docker: MLOps = ML + DEV + OPS -- [towardsdatascience.com: How to Structure a Data Science Project for Readability and Transparency](https://towardsdatascience.com/how-to-structure-a-data-science-project-for-readability-and-transparency-360c6716800) And How to Create One in One Line of Code ## Machine Learning workloads in kubernetes using Nix and NVIDIA. Running NVIDIA GPUs on Kubernetes