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Data Intensive AI - Bartosz Mikulski

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Manage episode 472609185 series 2831626
Content provided by DataTalks.Club. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by DataTalks.Club or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

In this podcast episode, we talked with Bartosz Mikulski about Data Intensive AI.

About the Speaker:

Bartosz is an AI and data engineer. He specializes in moving AI projects from the good-enough-for-a-demo phase to production by building a testing infrastructure and fixing the issues detected by tests. On top of that, he teaches programmers and non-programmers how to use AI. He contributed one chapter to the book 97 Things Every Data Engineer Should Know, and he was a speaker at several conferences, including Data Natives, Berlin Buzzwords, and Global AI Developer Days.

In this episode, we discuss Bartosz’s career journey, the importance of testing in data pipelines, and how AI tools like ChatGPT and Cursor are transforming development workflows. From prompt engineering to building Chrome extensions with AI, we dive into practical use cases, tools, and insights for anyone working in data-intensive AI projects. Whether you’re a data engineer, AI enthusiast, or just curious about the future of AI in tech, this episode offers valuable takeaways and real-world experiences.

0:00 Introduction to Bartosz and his background

4:00 Bartosz’s career journey from Java development to AI engineering

9:05 The importance of testing in data engineering

11:19 How to create tests for data pipelines

13:14 Tools and approaches for testing data pipelines

17:10 Choosing Spark for data engineering projects

19:05 The connection between data engineering and AI tools

21:39 Use cases of AI in data engineering and MLOps

25:13 Prompt engineering techniques and best practices

31:45 Prompt compression and caching in AI models

33:35 Thoughts on DeepSeek and open-source AI models

35:54 Using AI for lead classification and LinkedIn automation

41:04 Building Chrome extensions with AI integration

43:51 Comparing Cursor and GitHub Copilot for coding

47:11 Using ChatGPT and Perplexity for AI-assisted tasks

52:09 Hosting static websites and using AI for development

54:27 How blogging helps attract clients and share knowledge

58:15 Using AI to assist with writing and content creation

🔗 CONNECT WITH Bartosz

LinkedIn: https://www.linkedin.com/in/mikulskibartosz/

Github: https://github.com/mikulskibartosz

Website: https://mikulskibartosz.name/blog/

🔗 CONNECT WITH DataTalksClub

Join the community - https://datatalks.club/slack.html

Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ

Check other upcoming events - https://lu.ma/dtc-events

LinkedIn - https://www.linkedin.com/company/datatalks-club/

Twitter - https://twitter.com/DataTalksClub Website - https://datatalks.club/

  continue reading

183 episodes

Artwork

Data Intensive AI - Bartosz Mikulski

DataTalks.Club

29 subscribers

published

iconShare
 
Manage episode 472609185 series 2831626
Content provided by DataTalks.Club. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by DataTalks.Club or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

In this podcast episode, we talked with Bartosz Mikulski about Data Intensive AI.

About the Speaker:

Bartosz is an AI and data engineer. He specializes in moving AI projects from the good-enough-for-a-demo phase to production by building a testing infrastructure and fixing the issues detected by tests. On top of that, he teaches programmers and non-programmers how to use AI. He contributed one chapter to the book 97 Things Every Data Engineer Should Know, and he was a speaker at several conferences, including Data Natives, Berlin Buzzwords, and Global AI Developer Days.

In this episode, we discuss Bartosz’s career journey, the importance of testing in data pipelines, and how AI tools like ChatGPT and Cursor are transforming development workflows. From prompt engineering to building Chrome extensions with AI, we dive into practical use cases, tools, and insights for anyone working in data-intensive AI projects. Whether you’re a data engineer, AI enthusiast, or just curious about the future of AI in tech, this episode offers valuable takeaways and real-world experiences.

0:00 Introduction to Bartosz and his background

4:00 Bartosz’s career journey from Java development to AI engineering

9:05 The importance of testing in data engineering

11:19 How to create tests for data pipelines

13:14 Tools and approaches for testing data pipelines

17:10 Choosing Spark for data engineering projects

19:05 The connection between data engineering and AI tools

21:39 Use cases of AI in data engineering and MLOps

25:13 Prompt engineering techniques and best practices

31:45 Prompt compression and caching in AI models

33:35 Thoughts on DeepSeek and open-source AI models

35:54 Using AI for lead classification and LinkedIn automation

41:04 Building Chrome extensions with AI integration

43:51 Comparing Cursor and GitHub Copilot for coding

47:11 Using ChatGPT and Perplexity for AI-assisted tasks

52:09 Hosting static websites and using AI for development

54:27 How blogging helps attract clients and share knowledge

58:15 Using AI to assist with writing and content creation

🔗 CONNECT WITH Bartosz

LinkedIn: https://www.linkedin.com/in/mikulskibartosz/

Github: https://github.com/mikulskibartosz

Website: https://mikulskibartosz.name/blog/

🔗 CONNECT WITH DataTalksClub

Join the community - https://datatalks.club/slack.html

Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ

Check other upcoming events - https://lu.ma/dtc-events

LinkedIn - https://www.linkedin.com/company/datatalks-club/

Twitter - https://twitter.com/DataTalksClub Website - https://datatalks.club/

  continue reading

183 episodes

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