Machine Learning Ops (MLOps) and Data Science
- cd.foundation: Announcing the CD Foundation MLOps SIG
- dafriedman97.github.io: Machine Learning from Scratch Derivations in Concept and Code.
- cortex.dev: How to build a pipeline to retrain and deploy models
- github: A very Long never ending Learning around Data Engineering & Machine Learning
- towardsdatascience.com: A Kubernetes architecture for machine learning web-application deployments Use Kubernetes to reduce machine learning infrastructure costs and scale resources with ease.
- cloud.google.com: How to use a machine learning model from a Google Sheet using BigQuery ML
- itnext.io: Building ML Componentes on Kubernetes
- towardsdatascience.com: Deploying An ML Model With FastAPI — A Succinct Guide
- towardsdatascience.com: Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance 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
- ML Platform Workshop Example code for a basic ML Platform based on Pulumi, FastAPI, DVC, MLFlow and more
- rubrix A free and open-source tool to explore, label, and monitor data for NLP projects.
- towardsdatascience.com: Automatically Generate Machine Learning Code with Just a Few Clicks Using Traingenerator to easily create PyTorch and scikit-learn template codes for machine learning model training
- towardsdatascience.com: Schemafull streaming data processing in ML pipelines Making containerized Python streaming data pipelines leverage schemas for data validation using Kafka with AVRO and Schema Registry
- analyticsindiamag.com: Top tools for enabling CI/CD in ML pipelines
- towardsdatascience.com: Step-by-step Approach to Build Your Machine Learning API Using Fast API A fast and simple approach to serve your model as an API
- ravirajag.dev: MLOps Basics - Week 10: Summary
- kubeflow The Machine Learning Toolkit for Kubernetes
- medium.com: Machine Learning using Kubeflow
- infracloud.io: Machine Learning Orchestration on Kubernetes using Kubeflow
- blog.devgenius.io: Kubeflow Cloud Deployment (AWS) 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.
KServe Cloud Native Model Server
- kserve.github.io Highly scalable and standards based Model Inference Platform on Kubernetes for Trusted AI
- thenewstack.io: KServe: A Robust and Extensible Cloud Native Model Server
- analyticsvidhya.com: Bring DevOps To Data Science With MLOps
- 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
- towardsdatascience.com: From DevOps to MLOPS: Integrate Machine Learning Models using Jenkins and Docker 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 And How to Create One in One Line of Code
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To my JVM friends looking to explore Machine Learning techniques - you don’t necessarily have to learn Python to do that. There are libraries you can use from the comfort of your JVM environment. 🧵👇— Maria Khalusova (@mariaKhalusova) November 26, 2020
You don't need to go to a university to learn machine learning - you can do it from your living room, for completely free.— Tivadar Danka (@TivadarDanka) September 21, 2021
Here is an extensive list of curated free courses and tutorials, from beginner to advanced. ↓
(Trust me, you want to bookmark this tweet.)
I started taking data science courses last year, after studying and coding for at least 10 hours 6 days a week and doing several ML projects alongside data analysis projects, I finally got my first data analyst offer from a Nigerian bank last week after countless rejections— Sam (@SamsonTontoye) February 20, 2022