Data Engineering Podcast
MAY 18, 2024
The team at Zenlytic have leaned on the promise of large language models to build an AI agent that lets you converse with your data. In Code Comments, host Jamie Parker, Red Hatter and experienced engineer, shares the journey of technologists from across the industry and their hard-won lessons in implementing new technologies.
Data Engineering Podcast
FEBRUARY 25, 2024
Summary Building a database engine requires a substantial amount of engineering effort and time investment. Over the decades of research and development into building these software systems there are a number of common components that are shared across implementations. Go to dataengineeringpodcast.com/dagster today to get started.
Data Engineering Podcast
SEPTEMBER 17, 2023
Summary A significant amount of time in data engineering is dedicated to building connections and semantic meaning around pieces of information. Linked data technologies provide a means of tightly coupling metadata with raw information. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free!
The Pragmatic Engineer
JULY 20, 2023
In this issue, we cover: How Akita was founded On cofounders Raising funding Pivoting and growing the company On hiring The tech stack The biggest challenges of building a startup For this article, I interviewed Jean directly. So we started to build API specs on top of our API security product. We pivoted to API observability in 2020.
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Why do some embedded analytics projects succeed while others fail? We surveyed 500+ application teams embedding analytics to find out which analytics features actually move the needle. Read the 6th annual State of Embedded Analytics Report to discover new best practices. Brought to you by Logi Analytics.
KDnuggets
SEPTEMBER 30, 2024
Learn from those who have paved the way and gain practical advice on how to build a successful technology career.
Data Engineering Podcast
OCTOBER 1, 2023
Now that AI has reached the level of sophistication seen in the various generative models it is being used to build new ETL workflows. In this episode Jay Mishra shares his experiences and insights building ETL pipelines with the help of generative AI. How can you get the best results for your use case?
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Think your customers will pay more for data visualizations in your application? Five years ago they may have. But today, dashboards and visualizations have become table stakes. Discover which features will differentiate your application and maximize the ROI of your embedded analytics. Brought to you by Logi Analytics.
Speaker: Dr. Greg Loughnane and Chris Alexiuk
Technology professionals developing generative AI applications are finding that there are big leaps from POCs and MVPs to production-ready applications. However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are less understood.
Speaker: Christophe Louvion, Chief Product & Technology Officer of NRC Health and Tony Karrer, CTO at Aggregage
Christophe Louvion, Chief Product & Technology Officer of NRC Health, is here to take us through how he guided his company's recent experience of getting from concept to launch and sales of products within 90 days. Stakeholder Engagement 👥 Learn strategies to secure buy-in from sales, marketing, and executives.
Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage
💥 Anindo Banerjea is here to showcase his significant experience building AI/ML SaaS applications as he walks us through the current problems his company, Civio, is solving. This can be especially difficult when working with a large data corpus, and as the complexity of the task increases.
Speaker: Maher Hanafi, VP of Engineering at Betterworks & Tony Karrer, CTO at Aggregage
He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase
Putting the right LLMOps process in place today will pay dividends tomorrow, enabling you to leverage the part of AI that constitutes your IP – your data – to build a defensible AI strategy for the future.
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“Reimagined: Building Products with Generative AI” is an extensive guide for integrating generative AI into product strategy and careers featuring over 150 real-world examples, 30 case studies, and 20+ frameworks, and endorsed by over 20 leading AI and product executives, inventors, entrepreneurs, and researchers.
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