loader from loading.io

MLG 001 Introduction

Machine Learning Guide

Release Date: 02/01/2017

MLA 021 Databricks show art MLA 021 Databricks

Machine Learning Guide

Discussing Databricks with Ming Chang from (part of )

info_outline
MLA 020 Kubeflow show art MLA 020 Kubeflow

Machine Learning Guide

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 TensorFlow with Kubeflow, combine...

info_outline
MLA 019 DevOps show art MLA 019 DevOps

Machine Learning Guide

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

(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 culture. Explore the new...

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

Machine Learning Guide

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

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

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

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

Client, server, database, etc.

info_outline
MLA 012 Docker show art MLA 012 Docker

Machine Learning Guide

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

info_outline
 
More Episodes

Show notes: ocdevel.com/mlg/1. MLG teaches the fundamentals of machine learning and artificial intelligence. It covers intuition, models, math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your syllabus, with highly-curated resources for each episode's details at ocdevel.com. Audio is a great supplement during exercise, commute, chores, etc.


What is this podcast?
  • "Middle" level overview (deeper than a bird's eye view of machine learning; higher than math equations)
  • No math/programming experience required

Who is it for

  • Anyone curious about machine learning fundamentals
  • Aspiring machine learning developers

Why audio?

  • Supplementary content for commute/exercise/chores will help solidify your book/course-work

What it's not

  • News and Interviews: TWiML and AI, O'Reilly Data Show, Talking machines
  • Misc Topics: Linear Digressions, Data Skeptic, Learning machines 101
  • iTunesU issues

Planned episodes

  • What is AI/ML: definition, comparison, history
  • Inspiration: automation, singularity, consciousness
  • ML Intuition: learning basics (infer/error/train); supervised/unsupervised/reinforcement; applications
  • Math overview: linear algebra, statistics, calculus
  • Linear models: supervised (regression, classification); unsupervised
  • Parts: regularization, performance evaluation, dimensionality reduction, etc
  • Deep models: neural networks, recurrent neural networks (RNNs), convolutional neural networks (convnets/CNNs)
  • Languages and Frameworks: Python vs R vs Java vs C/C++ vs MATLAB, etc; TensorFlow vs Torch vs Theano vs Spark, etc