The hottest DataOps Substack posts right now

And their main takeaways
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Top Technology Topics

Why Snowflake’s Clone command changes the game for CI/CD in Data

The Orchestra Data Leadership Newsletter • 19 implied HN points • 26 Oct 23
🕹 Technology DataOps CI/CD Data Engineering Data Quality
  1. The Snowflake Clone command allows for cheap and quick testing of data during Continuous Integration flows, showing significant cost and time improvement compared to traditional create table commands.
  2. Continuous Deployment can be facilitated through Snowflake clones, as they are relatively inexpensive, removing cost barriers for Data Teams in implementing effective CD processes.
  3. The Clone command, available since at least 2016, is invaluable for Data Teams as it enables CI/CD pipelines, essential for deploying data into production reliably and efficiently via a data release pipeline.

Confidential Computing; DataOps and MLOps

Gradient Flow • 99 implied HN points • 23 Nov 21
🕹 Technology DataOps MLOps Data Exchange Podcasts
  1. Confidential Computing involves protecting data in all states: at rest, in use, and in transit.
  2. Confidential Computing tools focus on safeguarding data while being used, a difficult task due to the need for data to be unencrypted for computation.
  3. DataOps and MLOps are important for modern data governance and management, emphasizing the need for strong metadata platforms and strategies to avoid MLOps mistakes.

Gradient Flow #37: Automation in DataOps, Neural RecSys, Self-Supervision

Gradient Flow • 59 implied HN points • 17 Jun 21
🕹 Technology DataOps Cloud Computing Machine Learning
  1. Automation tools are essential in managing data across the machine learning lifecycle, enabling efficient data labeling, storage, and monitoring for computer vision applications.
  2. Questioning the effectiveness of neural recommendation systems sheds light on current trends in deep learning applications for recommendation systems.
  3. Experimentation and combination of modeling techniques, like XGBoost and neural models, are crucial for achieving optimal results in machine learning tasks.

Gradient Flow #33: DataOps, Natural Language Benchmarks, Multimodal ML

Gradient Flow • 0 implied HN points • 22 Apr 21
🕹 Technology DataOps Natural Language Benchmarks Machine Learning Funding Updates
  1. DataOps involves tools, processes, and startups that help organizations efficiently deliver AI and data products.
  2. NLU benchmarks need improvement for better model performance by focusing on better benchmark datasets.
  3. Multimodal Machine Learning and Machine Learning with Graphs are valuable resources for expanding knowledge in AI.
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