Holiday Roundup: December 13th | Recent developments in AI and Data Science
Welcome to the Burtch Works Holiday Roundup!
Discover the important takeaways from the Understanding AI article, which addresses rumors surrounding OpenAI's Constitutional AI work and highlights crucial considerations for aligning advanced AI systems.
Source: Understanding AI
An insightful YouTube seminar diving deeper into the shared history of graphical causal inference and probabilistic programming. Explore how to effectively perform causal graphical modeling using probabilistic programming techniques.
Source: PyMC Labs
SeattleDataGuy's Newsletter discusses key insights from the Data Engineering And Machine Learning Summit. It covers various topics like data normalization, tangible data science projects, right-sizing data infrastructure for companies, understanding business patterns in data, and the importance of building machine learning systems that match the team's size. The post also highlights the significance of practical, hands-on demonstrations in understanding and applying data engineering and ML concepts.
Source: Seattle Data Guy
The blog post "How to process extremely large (> 100TBs) data sets without burning millions" on EcZachly Data Engineering Newsletter provides detailed insights into optimizing large-scale data pipelines
The author shares their experience at Facebook with handling over 100 TB data sets, focusing on efficient deduplication and data processing methods. Key strategies include using Apache Spark or Flink, effective partitioning, and cost management. The article is valuable for data engineers dealing with large-scale data challenges.
Source: EcZachly Data Engineer