Machine Learning Guide

Jun 21 2022
Machine Learning Guide Podcast artwork

Machine learning audio course, teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models (shallow and deep), 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.

RSS feed Share
Copy RSS


Subscribe on Podcast Addict


MLA 021 Databricks

Jun 21 2022 25 mins  
Discussing Databricks with Ming Chang from Raybeam (part of DEPT®)

MLA 020 Kubeflow

Jan 28 2022 67 mins  
Conversation with Dirk-Jan Kubeflow (vs cloud native solutions like SageMaker) Dirk-Jan Verdoorn - Data Scientist at Dept Agency Kubeflow. (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 service

MLA 019 DevOps

Jan 13 2022 74 mins  
Chatting with co-workers about the role of DevOps in a machine learning engineer's life Expert coworkers at Dept Matt Merrill - Principal Software Developer Jirawat Uttayaya - DevOps Lead The Ship It Podcast (where Matt features often) Devops tools Terraform Ansible Pictures (funny and serious) Which AWS container service should I use? A visual guide on troubleshooting Ku

MLA 018 Descript

Nov 06 2021 6 mins  
(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. Descript The Ship It Podcast How to ship software, from the front lines. We talk with software developers about their craft, devel

MLA 017 AWS Local Development

Nov 05 2021 64 mins  
Show notes: ocdevel.com/mlg/mla-17 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 (Lambda, SageMaker Studio, Cloud9 Connect to deployed infrastructure via Client VPN Terraform example YouTube tutorial Creating the keys

MLA 016 SageMaker 2

Nov 04 2021 59 mins  
Part 2 of deploying your ML models to the cloud with SageMaker (MLOps) MLOps is deploying your ML models to the cloud. See MadeWithML for an overview of tooling (also generally a great ML educational run-down.) SageMaker Jumpstart Deploy Pipelines Monitor Kubernetes Neo

MLA 015 SageMaker 1

Nov 03 2021 46 mins  
Show notes Part 1 of deploying your ML models to the cloud with SageMaker (MLOps) MLOps is deploying your ML models to the cloud. See MadeWithML for an overview of tooling (also generally a great ML educational run-down.) SageMaker DataWrangler Feature Store Ground Truth Clarify Studio AutoPilot Debugger Distributed Training And I forgot to mention JumpStart, I'll mention next t

MLA 014 Machine Learning Server

Jan 17 2021 51 mins  
Server-side ML. Training & hosting for inference, with a goal towards serverless. AWS SageMaker, Batch, Lambda, EFS, Cortex.dev

MLA 013 Customer Facing Tech Stack

Jan 02 2021 46 mins  
Client, server, database, etc.

MLA 012 Docker

Nov 08 2020 30 mins  
Use Docker for env setup on localhost & cloud deployment, instead of pyenv / Anaconda. I recommend Windows for your desktop.