Super Data Science: ML & AI Podcast with Jon Krohn

685: Tools for Building Real-Time Machine Learning Applications, with Richmond Alake

Jun 6, 2023

Richmond Alake, Machine Learning Architect at Slalom Build, discusses his startups, podcasting, and new course on feature stores. He emphasizes the importance of writing for data scientists and shares insights on building real-time ML applications, developing AI companions, and investing in MLOps. The episode covers his use of tools like Databricks, Kinesis, and Swift programming for ML production.

01:06:19

Episode guests

Richmond Alake

AI Summary

AI Chapters

Episode notes

Podcast summary created with Snipd AI

Quick takeaways

Richmond utilizes Databricks and Kinesis for real-time data streaming in ML applications.

Writing accelerates career growth and industry visibility for data scientists.

Richmond recommends following him on various platforms for updates and insights in AI field.

Deep dives

Main Ideas 1

Richmond details the software choices he's made, using Databricks and Kinesis for real-time data streaming, Swift and Firebase for his mini-PT personal training iOS app, Python, Flask, and React for his generative AI startup, and the Beast feature store for production ML.

Intro

2min

Understanding Machine Learning Architecture and Engineering

7min

Building Real-Time Machine Learning Applications and Working on Startups

3min

Virtualizing Personal Training Experience with Real-time Machine Learning

15min

Discussion on AWS Trainium and Inferentia Chips for AI Applications

2min

Challenges of Book Writing, Taking Risks, and Feature Stores in Machine Learning

12min

Exploring AI Companions, Timeless Skills, and Forward-Thinking AI Strategies

3min

Transition from Physics to Data Science, Investment in MLOps, and Writing for Data Scientists

17min

Exploring Tools and Technologies for Real-Time Machine Learning Applications

3min

Richmond Alake, a Machine Learning Architect at Slalom Build, sits down with Jon to share real-time ML insights, tools and career experiences for a high-energy and high impact episode. From his work at Slalom Build to his two AI startups, discover the software choices, ML tools, and front-end development techniques used by a leader in the field.

This episode is brought to you by Posit, the open-source data science company, by AWS Inferentia, and by WithFeeling.ai, the company bringing humanity into AI. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• What is a Machine Learning Architect? [03:09]
• Richmond's startups [12:07]
• Why Richmond started a podcast [29:51]
• Richmond's new course on feature stores [38:05]
• Why Richmond produces data science content [43:25]
• Why All Data Scientists Should Write [51:30]

Additional materials: www.superdatascience.com/685

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Super Data Science: ML & AI Podcast with Jon Krohn

685: Tools for Building Real-Time Machine Learning Applications, with Richmond Alake

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Main Ideas 1

Main Ideas 2

Main Ideas 3

Main Ideas 4

Main Ideas 5

Remember Everything You Learn from Podcasts