Machine Learning Guide

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Machine Learning Guideinactive

Publisher |: Dept

Media Type |: audio

Podknife tags |: Artificial Intelligence,; Machine Learning,; Technology

Categories Via RSS |: Courses,; Education,; Technology

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.

People |: Tyler Renelle (@lefnire)
Country Of Origin |: USA
Produced In |: Portland, OR
Premiere Date |: 2017-02-01
Frequency |: Periodic
Explicit |: No

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48 Available Episodes (48 Total) / Average duration: 00:39:02

Date Duration Most Reviewed Highest Rated

48 Available Episodes (48 Total)Average duration: 00:39:02

Jun 22, 2022

00:25:45

Discussing Databricks with Ming Chang from Raybeam (part of DEPT®)

Jan 29, 2022

01:07:57

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 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.

TensorFlow Extended (TFX). If using TensorFlow with Kubeflow, combine with TFX for maximum power. (From the website:) TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines. When you're ready to move your models from research to production, use TFX to create and manage a production pipeline.

Alternatives:

Jan 13, 2022

01:14:38

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

Pictures (funny and serious)

Nov 07, 2021

00:06:22

(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, developer tools, developer productivity and what makes software development awesome. Hosted by your friends at Rocket Insights. AKA shipit.io
Brandbeats Podcast by BASIC An agency podcast with views on design, technology, art, and culture. Explore the new microsite at www.brandbeats.basicagency.com

Nov 06, 2021

01:04:04

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 is.html">Client VPN
- Terraform example
- YouTube tutorial
- authentication.html#mutual"> Creating the keys
LocalStack

Infrastructure as Code

Nov 05, 2021

00:59:43

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.)

Nov 04, 2021

00:46:46

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.)

And I forgot to mention JumpStart, I'll mention next time.

Jan 18, 2021

00:51:50

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

Jan 03, 2021

00:46:54

Client, server, database, etc.

Nov 09, 2020

00:30:58

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

Jun 22 | 00:25:45

MLA 021 Databricks

Jan 29 | 01:07:57

MLA 020 Kubeflow

Jan 13 | 01:14:38

MLA 019 DevOps

Nov 07 | 00:06:22

MLA 018 Descript

Nov 06 | 01:04:04

MLA 017 AWS Local Development

Nov 05 | 00:59:43

MLA 016 SageMaker 2

Nov 04 | 00:46:46

MLA 015 SageMaker 1

Jan 18 | 00:51:50

MLA 014 Machine Learning Server

Jan 03 | 00:46:54

MLA 013 Customer Facing Tech Stack

Nov 09 | 00:30:58

MLA 012 Docker

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