Player FM - Internet Radio Done Right
Checked 1d ago
اضافه شده در four سال پیش
محتوای ارائه شده توسط The Data Flowcast. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط The Data Flowcast یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
Player FM - برنامه پادکست
با برنامه Player FM !
icon Daily Deals

The Future of AI in Data Engineering With Astronomer’s Julian LaNeve and David Xue

23:36
 
اشتراک گذاری
 

Manage episode 421002920 series 2948506
محتوای ارائه شده توسط The Data Flowcast. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط The Data Flowcast یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
The world of data orchestration and machine learning is rapidly evolving, and tools like Apache Airflow are at the forefront of these changes. Understanding how to effectively utilize these tools can significantly enhance data processing and AI model deployment. This episode features Julian LaNeve, CTO at Astronomer, and David Xue, Machine Learning Engineer at Astronomer. They delve into the intricacies of data orchestration, generative AI and the practical applications of these technologies in modern data workflows. Key Takeaways: (01:51) The pressure to engage in the generative AI space. (02:02) Generative AI can elevate data utilization to the next level. (02:43) The transparency issues with commercial AI models. (04:27) High-quality data in model performance is crucial. (06:40) Running new models on smaller devices, like phones. (12:19) Fine-tuning LLMs to handle millions of task failures. (16:54) Teaching AI to understand specific logs, not general passages, is a goal. (21:56) Using Airflow as a general-purpose orchestration tool. (22:00) Airflow is adaptable for various use cases, including ETL and ML systems. Resources Mentioned: Julian LaNeve - https://www.linkedin.com/in/julianlaneve/ Atronomer - https://www.linkedin.com/company/astronomer/ David Xue - https://www.linkedin.com/in/david-xue-uva/ Apache Airflow - https://airflow.apache.org/ Meta’s Open Source Llama 3 model: https://ai.meta.com/blog/meta-llama-3/https://ai.meta.com/blog/meta-llama-3/ Microsoft’s Phi-3 model: https://www.microsoft.com/en-us/research/publication/phi-3-technical-report-a-highly-capable-language-model-locally-on-your-phone/ GPT-4 - https://www.openai.com/research/gpt-4 Thanks for listening to The Data Flowcast: Mastering Airflow for Data Engineering & AI. If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #ai #automation #airflow #machinelearning
  continue reading

61 قسمت

iconاشتراک گذاری
 
Manage episode 421002920 series 2948506
محتوای ارائه شده توسط The Data Flowcast. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط The Data Flowcast یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
The world of data orchestration and machine learning is rapidly evolving, and tools like Apache Airflow are at the forefront of these changes. Understanding how to effectively utilize these tools can significantly enhance data processing and AI model deployment. This episode features Julian LaNeve, CTO at Astronomer, and David Xue, Machine Learning Engineer at Astronomer. They delve into the intricacies of data orchestration, generative AI and the practical applications of these technologies in modern data workflows. Key Takeaways: (01:51) The pressure to engage in the generative AI space. (02:02) Generative AI can elevate data utilization to the next level. (02:43) The transparency issues with commercial AI models. (04:27) High-quality data in model performance is crucial. (06:40) Running new models on smaller devices, like phones. (12:19) Fine-tuning LLMs to handle millions of task failures. (16:54) Teaching AI to understand specific logs, not general passages, is a goal. (21:56) Using Airflow as a general-purpose orchestration tool. (22:00) Airflow is adaptable for various use cases, including ETL and ML systems. Resources Mentioned: Julian LaNeve - https://www.linkedin.com/in/julianlaneve/ Atronomer - https://www.linkedin.com/company/astronomer/ David Xue - https://www.linkedin.com/in/david-xue-uva/ Apache Airflow - https://airflow.apache.org/ Meta’s Open Source Llama 3 model: https://ai.meta.com/blog/meta-llama-3/https://ai.meta.com/blog/meta-llama-3/ Microsoft’s Phi-3 model: https://www.microsoft.com/en-us/research/publication/phi-3-technical-report-a-highly-capable-language-model-locally-on-your-phone/ GPT-4 - https://www.openai.com/research/gpt-4 Thanks for listening to The Data Flowcast: Mastering Airflow for Data Engineering & AI. If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations. #ai #automation #airflow #machinelearning
  continue reading

61 قسمت

همه قسمت ها

×
 
Loading …

به Player FM خوش آمدید!

Player FM در سراسر وب را برای یافتن پادکست های با کیفیت اسکن می کند تا همین الان لذت ببرید. این بهترین برنامه ی پادکست است که در اندروید، آیفون و وب کار می کند. ثبت نام کنید تا اشتراک های شما در بین دستگاه های مختلف همگام سازی شود.

 

icon Daily Deals
icon Daily Deals
icon Daily Deals

راهنمای مرجع سریع

در حین کاوش به این نمایش گوش دهید
پخش