Seeing Images Through the Eyes of Decision Trees
In this article, you’ll learn to: • Turn unstructured, raw image data into structured, informative features.
7 Pandas Tricks to Improve Your Machine Learning Model Development
If you’re reading this, it’s likely that you are already aware that the performance of a machine learning model is not just a function of the chosen algorithm.
A Practical Guide to Handling Out-of-Memory Data in Python
These days, it is not uncommon to come across datasets that are too large to fit into random access memory (RAM), especially when working on advanced data analysis projects at scale, managing streaming data generated at high velocity, or building large machine learning models.
The “Super Weight:” How Even a Single Parameter can Determine a Large Language Model’s Behavior
A recent paper from Apple researchers, “The Super Weight in Large Language Models,” reveals that an extremely small subset of parameters in LLMs (in some cases, a single parameter) can exert a disproportionate influence on an LLM’s overall functionality (see Figure 1). This work highlights the critical role of these “super weights” and their corresponding …
About Palantir
Answers to Frequently Asked Questions About Palantir We have received many questions about Palantir. Given the high interest in our company, we collected the questions we receive most often and use this blog post to answer them. What does Palantir do? Palantir Technologies is a software company that provides data operations and AI infrastructure platforms as well as …
From Facts & Metrics to Media Machine Learning: Evolving the Data Engineering Function at Netflix
By Dao Mi, Pablo Delgado, Ryan Berti, Amanuel Kahsay, Obi-Ike Nwoke, Christopher Thrailkill, and Patricio Garza At Netflix, data engineering has always been a critical function to enable the business’s ability to understand content, power recommendations, and drive business decisions. Traditionally, the function centered on building robust tables and pipelines to capture facts, derive metrics, and …
Fine-tune OpenAI GPT-OSS models using Amazon SageMaker HyperPod recipes
This post is the second part of the GPT-OSS series focusing on model customization with Amazon SageMaker AI. In Part 1, we demonstrated fine-tuning GPT-OSS models using open source Hugging Face libraries with SageMaker training jobs, which supports distributed multi-GPU and multi-node configurations, so you can spin up high-performance clusters on demand. In this post, …
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Intelligent code conversion: Databricks Spark SQL to BigQuery SQL via Gemini
As data platforms evolve and businesses diversify their cloud ecosystems, the need to migrate SQL workloads between engines is becoming increasingly common. Recently, I had the opportunity to work on translating a set of Databricks SQL queries to BigQuery SQL — a task that is deceptively complex due to differences in syntax, functions, and execution …
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Inside Walmart’s AI security stack: How a startup mentality is hardening enterprise-scale defense
Walmart CISO Jerry Geisler on securing agentic AI, modernizing identity, and Zero Trust for enterprise-scale cybersecurity resilience.Read More
Qwen-Image-Edit LoRA training is here + we just dropped our first trained model
Hey everyone! 👋 We just shipped something we’ve been cooking up for a while – full LoRA training support for Qwen-Image-Edit, plus our first trained model is now live on Hugging Face! What’s new: ✅ Complete training pipeline for Qwen-Image-Edit LoRA adapters ✅ Open-source trainer with easy YAML configs ✅ First trained model: Inscene LoRA …
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