- MLOps And Machine Learning Roadmap - Aug 12, 2021.
A 16–20 week roadmap to review machine learning and learn MLOps.
- How to Deploy a Flask API in Kubernetes and Connect it with Other Micro-services - Feb 9, 2021.
A hands-on tutorial on how to implement your micro-service architecture using the powerful container orchestration tool Kubernetes.
- 8 New Tools I Learned as a Data Scientist in 2020 - Jan 14, 2021.
The author shares the data science tools learned while making the move from Docker to Live Deployments.
- Kubernetes vs. Amazon ECS for Data Scientists - Nov 19, 2020.
In this article, we’ll look at two container management solutions — Kubernetes and Amazon Elastic Container Service (ECS) — from a perspective that makes sense for aspiring and current data scientists.
- 5 Reasons Why Containers Will Rule Data Science - Nov 9, 2020.
Historically, containers were a way to abstract a software stack away from the operating system. For data scientists, containers have historically offered few benefits.
- Data Science Meets Devops: MLOps with Jupyter, Git, and Kubernetes - Aug 21, 2020.
An end-to-end example of deploying a machine learning product using Jupyter, Papermill, Tekton, GitOps and Kubeflow.
- KDnuggets™ News 20:n31, Aug 12: Data Science Skills: Have vs Want: Vote in the New Poll; Netflix Polynote is a New Open Source Framework to Build Better Data Science Notebooks - Aug 12, 2020.
Vote in the new KDnuggets poll
: which data science skills you have and which ones you want? Netflix is not only for movies - its Polynote is a new open source framework to build better data science notebooks; Learn about containerization of PySpark using Kubernetes; Read the findings from Data Scientist Job Market 2020 analysis; and Explore GPT-3 latest.
- Containerization of PySpark Using Kubernetes - Aug 6, 2020.
This article demonstrates the approach of how to use Spark on Kubernetes. It also includes a brief comparison between various cluster managers available for Spark.
- Deploy Machine Learning Pipeline on AWS Fargate - Jul 3, 2020.
A step-by-step beginner’s guide to containerize and deploy ML pipeline serverless on AWS Fargate.
- Introduction to Kubeflow MPI Operator and Industry Adoption - Mar 27, 2020.
Kubeflow just announced its first major 1.0 release recently. This post introduces the MPI Operator, one of the core components of Kubeflow, currently in alpha, which makes it easy to run synchronized, allreduce-style distributed training on Kubernetes.
- How Kubeflow Can Add AI to Your Kubernetes Deployments - Feb 21, 2020.
As Kubernetes is capable of working with other solutions, it is possible to integrate it with a collection of tools that can almost fully automate your development pipeline. Some of those third-party tools even allow you to integrate AI into Kubernetes. One such tool you can integrate with Kubernetes is Kubeflow. Read more about it here.
- Webinar: Build auto-adaptive machine learning models with Kubernetes - Sep 27, 2019.
This live webinar, Oct 2 2019, will instruct data scientists and machine learning engineers how to build manage and deploy auto-adaptive machine learning models in production. Save your spot now.
- Online Workshop: How to set up Kubernetes for all your machine learning workflows - Jul 17, 2019.
Join this free live online workshop, Jul 31 @12 PM ET, to learn how to set up your Kubernetes cluster, so you can run Spark, TensorFlow, and any ML framework instantly, touching on the entire machine learning pipeline from model training to model deployment.
- How to use continual learning in your ML models, June 19 Webinar - May 29, 2019.
This webinar for professional data scientists will go over how to monitor models when in production, and how to set up automatically adaptive machine learning.
- Implementing Enterprise AI course using TensorFlow and Keras - Nov 27, 2017.
The course is for developers and architects who want to transition their career to Enterprise AI, but also has strategic (non-coding) version. The course starts in Jan 2018 and will take 3 months for the content and up to 3 months for the team project.