The Pragmatic Engineer

APRIL 21, 2024

See a longer version of this article here: Scaling ChatGPT: Five Real-World Engineering Challenges. Sometimes the best explanations of how a technology solution works come from the software engineers who built it. To explain how ChatGPT (and other large language models) operate, I turned to the ChatGPT engineering team. "How does ChatGPT work, under the hood?

Engineering 363

Engineering Software Engineer Software Engineering Programming 363

Data Engineering Podcast

APRIL 21, 2024

Summary Generative AI has rapidly transformed everything in the technology sector. When Andrew Lee started work on Shortwave he was focused on making email more productive. When AI started gaining adoption he realized that he had even more potential for a transformative experience. In this episode he shares the technical challenges that he and his team have overcome in integrating AI into their product, as well as the benefits and features that it provides to their customers.

Data Lake 182

Data Lake High Quality Data Machine Learning Data Pipeline 182

Scott Logic

APRIL 21, 2024

Apache Spark has now reached version 3.5.1, but what if you are still using a 2.4.x version? 2.4.8 went out of support in May 2021, so upgrading is strongly advised. If you go through the pain of updating to the latest version, what do you gain? Apache Spark SQL has gone through a major evolution, now supporting ANSI SQL, and adding many new features and making many performance improvements.

Scala 52

Scala Java SQL Python 52

Data Engineering Weekly

APRIL 21, 2024

Meta: Introducing Meta Llama 3 - The most capable openly available LLM to date Meta is taking an interesting approach in the growing LLM market with the open source approach and distribution across all the leading cloud providers and data platforms. It is exciting to see Llama 3 with 70B parameters on par with GPT-3.5, which I believe has 175B parameters.

Data Engineering 52

Data Engineering Data Engineer Engineering Medical 52

Advertisement

In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate

Data Pipeline