Scaling Vector Search: Comparing Quantization and Matryoshka Embeddings for 80% Cost Reduction
Navigating the performance cliff: How pairing MRL with int8 and binary quantization balances infrastructure costs with retrieval accuracy.
The post Scaling Vector Search: Comparing Quantization and Matryoshka Embeddings for 80% Cost Reduction appeared first on Towards Data Science.
I Finally Built My First AI App (And It Wasn’t What I Expected)
A beginner-friendly walkthrough of API calls, environment variables, and real-world AI infrastructure
The post I Finally Built My First AI App (And It Wasn’t What I Expected) appeared first on Towards Data Science.
How the Fourier Transform Converts Sound Into Frequencies
A visual, intuition-first guide to understanding what the math is really doing — from winding machines to spectrograms
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Hybrid Neuro-Symbolic Fraud Detection: Guiding Neural Networks with Domain Rules
I really thought I was onto something big: add a couple of simple domain rules to the loss function, and watch fraud detection just skyrocket on super-imbalanced data. The first run looked amazing… until I fixed a sneaky threshold bug and ran the whole thing across five different random seeds. Sudde...
Building a Like-for-Like solution for Stores in Power BI
Like-for-Like (L4L) solutions are essential for comparing elements. It's about comparing only comparable elements, in this case, comparing stores over time. Let's see a solution built in a Semantic model.
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How to design and implement agent skills for custom agents outside the Claude ecosystem
The post What Are Agent Skills Beyond Claude? appeared first on Towards Data Science.
When Data Lies: Finding Optimal Strategies for Penalty Kicks with Game Theory
A data-driven introduction to game theory, Nash equilibrium, and strategic decision-making
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Machine Learning at Scale: Managing More Than One Model in Production
From one model to managing a massive portfolio: What 10 years in the industry taught me
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LatentVLA: Latent Reasoning Models for Autonomous Driving
What if natural language is not the best abstraction for driving?
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Five classical data science skills are becoming the scarcest resource in tech. A 90-day roadmap to build them while everyone else chases AI hype.
The post The AI Bubble Has a Data Science Escape Hatch appeared first on Towards Data Science.
Stop Tuning Hyperparameters. Start Tuning Your Problem.
80% of ML projects fail from bad problem framing, not bad models. A 5-step protocol to define the right problem before you write training code.
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I Quit My $130,000 ML Engineer Job After Learning 4 Lessons
What they don't tell you about "dream tech jobs"
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Code Less, Ship Faster: Building APIs with FastAPI
Master path operations, Pydantic models, dependency injection, and automatic documentation.
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Zero-Waste Agentic RAG: Designing Caching Architectures to Minimize Latency and LLM Costs at Scale
Reducing LLM costs by 30% with validation-aware, multi-tier caching
The post Zero-Waste Agentic RAG: Designing Caching Architectures to Minimize Latency and LLM Costs at Scale appeared first on Towards Data Science.
If you have both unique domain expertise and know how to make it usable to your AI systems, you’ll be hard to beat.
The post Context Engineering as Your Competitive Edge appeared first on Towards Data Science.
Claude Skills and Subagents: Escaping the Prompt Engineering Hamster Wheel
How reusable, lazy-loaded instructions solve the context bloat problem in AI-assisted development.
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Scaling ML Inference on Databricks: Liquid or Partitioned? Salted or Not?
A case study on techniques to maximize your clusters
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Designing Data and AI Systems That Hold Up in Production
A system-level perspective on architecture, agents, and responsible scale
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A practical guide to identifying, restoring, and transforming elements within your images
The post Detecting and Editing Visual Objects with Gemini appeared first on Towards Data Science.
Scaling Feature Engineering Pipelines with Feast and Ray
Utilizing feature stores like Feast and distributed compute frameworks like Ray in production machine learning systems
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Breaking the Host Memory Bottleneck: How Peer Direct Transformed Gaudi’s Cloud Performance
Engineering RDMA-like performance over cloud host NICs using libfabric, DMA-BUF, and HCCL to restore distributed training scalability
The post Breaking the Host Memory Bottleneck: How Peer Direct Transformed Gaudi’s Cloud Performance appeared first on Towards Data Science.
Aliasing in Audio, Easily Explained: From Wagon Wheels to Waveforms
Understanding the foundational distortion of digital audio from first principles, with worked examples and visual intuition
The post Aliasing in Audio, Easily Explained: From Wagon Wheels to Waveforms appeared first on Towards Data Science.