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Towards Data Science
Jul 23, 2026
Lessons Learned After 8.5 Years of ML
Patience, Optimism, Discipline, Projects, Teams
The post Lessons Learned After 8.5 Years of ML appeared first on Towards…
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ML Mastery
Jul 14, 2026
LLM Evaluation Frameworks Compared: How to Actually Measure What Your Model Does
In this article, you will learn how to evaluate LLM applications using the three dominant open-source frameworks — RAGAS…
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Towards Data Science
Jul 21, 2026
I Tried Fine-Tuning a Robot AI Model on Colab. Here Is What Worked
A reproducible 100-step LoRA fine-tuning run for OpenVLA, with dataset checks, Colab setup, training metrics, and W&B ev…
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ML Mastery
Jul 10, 2026
Choosing the Right AI Agent Memory Strategy: A Decision-Tree Approach
In this article, you will learn how to choose the right memory strategy for an AI agent by working through a simple deci…
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ML Mastery
Jul 16, 2026
Run a Local AI Model with Ollama in 15 Minutes
In this article, you will learn how to get a small language model running locally on your own machine in under 15 minute…
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Towards Data Science
Jul 21, 2026
How Much of a Data Science Workflow Can Run on a GPU Today? Part 1: Accelerating Data Preparation
Exploring GPU acceleration with cuDF, cudf.pandas, and the Polars GPU Engine
The post How Much of a Data Science Workflo…
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Towards Data Science
Jul 25, 2026
How to Optimize Vector Search When RAM Gets Too Expensive: On-Disk vs. In-Memory ANN Indexes
Architecting cost-effective infrastructure by navigating the latency and storage trade-offs of HNSW, SPANN, and DiskANN
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Towards Data Science
Jul 24, 2026
Tabular LLMs: An Introduction to the Foundation Models That Predict Your Spreadsheet
Tabular foundation models predict the missing column of any spreadsheet zero-shot, the way an LLM completes text — and o…
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