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Enhancing The Abilities Of Software Engineers With Generative AI At Tabnine

Data Engineering Podcast

Generative AI has accelerated the ability of developer tools to provide useful suggestions that speed up the work of engineers. Tabnine is one of the main platforms offering an AI powered assistant for software engineers. Tabnine is one of the main platforms offering an AI powered assistant for software engineers.

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Top 10 Software Engineer Research Topics for 2023

Knowledge Hut

Software engineering, in general, is a dynamic and rapidly changing field that demands a thorough understanding of concepts related to programming, computer science, and mathematics. As software systems become more complicated in the future, software developers must stay updated on industry innovations and the latest trends.

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Unlocking Your dbt Projects With Practical Advice For Practitioners

Data Engineering Podcast

Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.

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Data Engineering Weekly #167

Data Engineering Weekly

link] Arpit Choudhury: Growth needs to speak the language of Engineering and Data Software engineers and data engineers don’t speak the same language It reminds me of this famous quote The Author highlights that the growth team should be aware of the skill differences.

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Running demand forecasting machine learning models at scale

Picnic Engineering

The rich context provided by our Snowflake-powered Data Warehouse enhances their performance, allowing us to create a robust feature set for training. Training these models on our historical demand and assessing their performance is manageable as a one-time project since concepts like data drift are still not a big concern.

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Mastering Data Quality: 5 Lessons from Data Leaders at Babylist and Nasdaq

Monte Carlo

while overlooking or failing to understand what it really takes to make their tools — and, ultimately, their data initiatives — successful. When it comes to driving impact with your data, you first need to understand and manage that data’s quality. They can really understand [what it means] when data is wrong.”

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5 Hard Truths About Generative AI for Technology Leaders

Monte Carlo

Many data teams make the mistake of excluding key players from their Gen AI tiger teams, and that’s costing them in the long run. Software engineers to develop the code, the user facing application and the API calls. Data scientists to consider new use cases, fine tune your models , and push the team in new directions.