How model validation standards are changing for LLM-based systems: what breaks, what carries over, and how to test output quality
The post The Model Validation Playbook for GenAI: Lessons from Banking appeared first on Towards Data Science.
I saved my day with a mere $52 bill. But you don't have to repeat my mistake.
The post I Vibe-Coded an App in Just Two Hours (And Regretted It the Next Day) appeared first on Towards Data Science.
From scalar observations to self-attention, and how positional information restores sequence order
The post Why Transformers Need Positional Encoding For Time Series: A Visual Guide appeared first on Towards Data Science.
Why the default 50/50 split is the wrong move when your treatment is more expensive than your control, and how cost-based sampling weights fix it
The post Optimal Traffic Allocation Under Heterogeneous Variant Cost appeared first on Towards Data Science.
A visual guide to how graph neural networks work under the hood
The post Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply appeared first on Towards Data Science.
The hidden assumptions behind the data we observe.
The post What We Miss About Missing Values appeared first on Towards Data Science.
A framework for building RAG pipelines that introduces complexity in response to observed failure modes, from lexical and hybrid search to reranking and agentic information seeking
The post Why RAG Complexity Should Be Earned appeared first on Towards Data Science.
Quick and simple tips to help you write better agent instructions
The post 8 Tips for Writing Effective Agent Instructions appeared first on Towards Data Science.
How to apply the latest context engineering guidelines to your day-to-day data science work
The post Context Engineering Is Changing. Here’s What It Means for Data Scientists appeared first on Towards Data Science.
As AI handles more of the execution, what work should belong to agents vs humans and why does that distinction matter?
The post Agentic AI Is Rewriting The Analytics Stack But There's One Skill It Still Can't Touch appeared first on Towards Data Science.
A practical guide to getting better code, not just more code
The post How to Work with AI Coding Agents appeared first on Towards Data Science.
Why most agents are just flowcharts in disguise, and what to build instead.
The post Is Agentic AI Just Automation? appeared first on Towards Data Science.
Watermarks act at the model’s moments of doubt, and so do the safety checks that catch AI mistakes
The post Hallucinations, Watermarks, Removers, and a Squeezed Balloon appeared first on Towards Data Science.
How DFlash trades spare compute for saved memory bandwidth, and why its gains shrink as concurrency rises
The post Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash appeared first on Towards Data Science.
LoRA fine-tuning solved our under-labeling problem. Whether it makes sense for you depends on three questions.
The post Why We Fine-Tuned SigLip (And Why That’s Not Always the Right Call) appeared first on Towards Data Science.
A walkthrough of and the maths behind using low-capacity networks to acquire fine-grained scoring when only categorical labelling is available for training
The post Estimating from No Data: Deriving a Continuous Score from Categories appeared first on Towards Data Science.
Watermarks act at the model’s moments of doubt, and so do the safety checks that catch AI mistakes
The post Hallucinations, Watermarks, Removers, and a Squeezed Balloon appeared first on Towards Data Science.
Speculative decoding can turn underused CPU compute into faster token generation, without changing the model's output. In our vLLM tests, DFlash delivered 3.92x the autoregressive throughput with Qwen3.5-9B on Intel Xeon 6 at concurrency 1. We break down where the speedup comes from, explain the acceptance metrics, and show what determines whether speculation pays off.
The post Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash appeared first on Towards Data Science.
LoRA fine-tuning solved our under-labeling problem. Whether it makes sense for you depends on three questions.
The post Why We Fine-Tuned SigLip (And Why That’s Not Always the Right Call) appeared first on Towards Data Science.
A walkthrough of and the maths behind using low-capacity networks to acquire fine-grained scoring when only categorical labelling is available for training
The post Estimating from No Data: Deriving a Continuous Score from Categories appeared first on Towards Data Science.
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