loader from loading.io

MLG 007 Logistic Regression

Machine Learning Guide

Release Date: 02/19/2017

MLA 021 Databricks show art MLA 021 Databricks

Machine Learning Guide

Support my new podcast: Discussing Databricks with Ming Chang from  (part of )

info_outline
MLA 020 Kubeflow show art MLA 020 Kubeflow

Machine Learning Guide

Support my new podcast: Conversation with Dirk-Jan Kubeflow (vs cloud native solutions like SageMaker)  - Data Scientist at Dept Agency . (From the website:) The Machine Learning Toolkit for Kubernetes. The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow. . If using...

info_outline
MLA 019 DevOps show art MLA 019 DevOps

Machine Learning Guide

Support my new podcast: Chatting with co-workers about the role of DevOps in a machine learning engineer's life Expert coworkers at Dept  - Principal Software Developer  - DevOps Lead  (where Matt features often) Devops tools Pictures (funny and serious)

info_outline
MLA 018 Descript show art MLA 018 Descript

Machine Learning Guide

Support my new podcast: (Optional episode) just showcasing a cool application using machine learning Dept uses Descript for some of their podcasting. I'm using it like a maniac, I think they're surprised at how into it I am. Check out the transcript & see how it performed.  How to ship software, from the front lines. We talk with software developers about their craft, developer tools, developer productivity and what makes software development awesome. Hosted by your friends at Rocket Insights. AKA shipit.io  An agency podcast with views on design, technology, art, and...

info_outline
MLA 017 AWS Local Development show art MLA 017 AWS Local Development

Machine Learning Guide

Support my new podcast: Show notes:  Developing on AWS first (SageMaker or other) Consider developing against AWS as your local development environment, rather than only your cloud deployment environment. Solutions: Stick to AWS Cloud IDEs (, ,  Connect to deployed infrastructure via  Infrastructure as Code

info_outline
MLA 016 SageMaker 2 show art MLA 016 SageMaker 2

Machine Learning Guide

Support my new podcast: Part 2 of deploying your ML models to the cloud with SageMaker (MLOps) MLOps is deploying your ML models to the cloud. See  for an overview of tooling (also generally a great ML educational run-down.)

info_outline
MLA 015 SageMaker 1 show art MLA 015 SageMaker 1

Machine Learning Guide

Support my new podcast: Part 1 of deploying your ML models to the cloud with SageMaker (MLOps) MLOps is deploying your ML models to the cloud. See  for an overview of tooling (also generally a great ML educational run-down.) And I forgot to mention , I'll mention next time.

info_outline
MLA 014 Machine Learning Server show art MLA 014 Machine Learning Server

Machine Learning Guide

Support my new podcast: Server-side ML. Training & hosting for inference, with a goal towards serverless. AWS SageMaker, Batch, Lambda, EFS, Cortex.dev

info_outline
MLA 013 Customer Facing Tech Stack show art MLA 013 Customer Facing Tech Stack

Machine Learning Guide

Support my new podcast: Client, server, database, etc.

info_outline
MLA 012 Docker show art MLA 012 Docker

Machine Learning Guide

Support my new podcast: Use Docker for env setup on localhost & cloud deployment, instead of pyenv / Anaconda. I recommend Windows for your desktop.

info_outline
 
More Episodes

Support my new podcast: Lefnire's Life Hacks

Your first classifier: Logistic Regression. That plus Linear Regression, and you're a 101 supervised learner! ocdevel.com/mlg/7 for notes and resources