Elevate your career in the new year with Databricks certifications! We offer a variety of role-based learning pathways across key data, analytics, and AI roles so that you can expand and showcase your skillset! Sign up for one (or multiple!) today https://
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Databricks Certifications Elevate Your Data Analytics Career
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Feature Platforms Maturation: MLOps Stack Evolution
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The last few years saw the maturation of a core component of the MLOps stack: feature platforms. This post discusses the evolution of feature platforms & the design decisions / functionality to speed up feature engineering. Feedback always appreciated! https://
huyenchip.com/2023/01/08/sel
f-serve-feature-platforms.html
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Recursive Text Splitter for Improved Chunk Processing
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Recursive Text Splitter The previous text splitter only split on a single character This one recursively splits chunks on different types of characters, until all chunks are below the desired size
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Yipiti Uses Databricks Workflows Analyze Petabytes Data
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@yipitdata analyzes billions of data points daily to provide accurate, detailed insights. Learn how their #datateam uses #DatabricksWorkflows to clean and analyze petabytes of data that many of the world’s largest investment funds depend on! https://
dbricks.co/3PcrdO8 -
Data Volume and Mining Drive Commercial AI Insights
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The driving forces in this brave new world are access to ever-increasing volumes of data and our ever-increasing technological capability to mine that data for commercial insights
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Differential Privacy Results Beyond Pure DP Guarantees
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Everything above is for pure (ε, 0)-DP. We also have results for (ε, δ)-DP. Also, the conversion from robustness to privacy is not always optimal, we have an example for sparse mean estimation. 11/n
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Robust Gaussian Estimation: Novel Techniques in Statistical Learning
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Finally, see simultaneous work of Alabi, @praveshkkothari
, Tankala, Venkat, Zhang, which also studies private & robust Gaussian estimation from a robust stats perspective. Totally different techniques! https://
arxiv.org/abs/2212.08018 Again, our paper link: https://
arxiv.org/abs/2212.05015 12/12 -
Private Robust Gaussian Estimation Framework Near-Optimal Sample Complexity
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So what can we do with this framework? The most interesting application is estimating Gaussians privately AND robustly: the resulting algorithm nails it, with a near-optimal sample complexity. Other potential applications include mean estimation, regression, etc. 9/n
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Differential Privacy Frameworks for Robust Statistical Estimators
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Most have been instance-specific, e.g., here's a particular robust estimator, let's privatize it. Some have proposed broader frameworks, e.g., https://
arxiv.org/abs/2111.06578 by Liu, Kong and @sewoong79
, or https://
arxiv.org/abs/2112.03548 by @praveshkkothari @pasin30055 @ameya_pa 5/n -
Formalizing Robustness and Privacy in Algorithm Design
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Intuitively, robustness and privacy of an algorithm are closely related: they both say the algorithm should be somehow insensitive to changing a small amount of the dataset. Formalizing connections between the two has proven to be a bit trickier. 3/n