I have spent many years as an software engineer who was a total outsider to machine-learning, but with some curiosity and occasional peripheral interactions with it. During this time, a recurring theme for me was horror (and, to be honest, disdain) every time I encountered the widespread usage of Python pickle in the Python ML ecosystem. In addition to their major security issues1, the use of pickle for serialization tends to be very brittle, leading to all kinds of nightmares as you evolve your code and upgrade libraries and Python versions.
Check your grasp of how neural networks make predictions in Python, from dot products and activation functions to gradient descent and backpropagation.
A lot of “capture-the-flag” style ML puzzles give you a black box neural net, and your job is to figure out what it does. When we were thinking of creating our own ML puzzle early last year, we wanted to do something a little different. We thought it’d be neat to give users a complete specification of the neural net, weights and all. They would then be forced to use the tools of mechanistic interpretability to reverse engineer the network—which is a situation we sometimes find ourselves facing in our own research, when trying to interpret features of complex models.
Matching the Tool to the Task A Quick Recap In a previous article, we focused on the strengths of Large Language Models (LLMs), traditional Machine Learning (ML), and statistical methods and recommended 4 key questions to help you choose the right tool for a data solution. Your Data: Is it structured or unstructured? Bounded or unbounded? Your Goal: Do you need prediction, generation, or inference? Your Data Volume: Are you working with massive datasets or limited samples? Your Need for Transparency: Is deep explainability or strict repeatability a requirement? The key takeaway was that LLMs…
Machine learning models increasingly support decisions that carry real consequences. For example, they help fraud analysts identify suspicious transactions, assist doctors in assessing patient risk, and inform hiring decisions that can shape people’s careers.As these models become more complex, explainability has become a cornerstone of trustworthy AI. The idea is straightforward: if people understand why a model reached a particular prediction, they should be able to make better, more informed decisions.But there is a fundamental question that often goes unasked: How do we know whether an…
Enter Deliveroo’s ML Platform For the past three years, we have been building Deliveroo’s Machine Learning Platform, or the ML Platform as we like to call it. The ML Platform boosts our model-building and deployment capabilities by standardising ML workflows, streamlining the end-to-end development process and simplifying model deployment. Besides saving software engineering effort through centralising tooling, the ML Platform also reduces the time that our ML engineers spend on infrastructure tasks. As a result, our ML engineers can now iterate their ML models 2-3x faster than before. What…
A clustering-based approach to create deep learning datasets in a dayIntroductionUnderstanding what’s happening in an image is both an important task, as well as a costly one. In the last few years, the field of computer vision has greatly accelerated due to the advances in neural networks. At Bumble Inc., we see potential value in computer vision for a variety of use cases, such as improving the safety of our platform and providing our members with a better user experience.The most common way to train these neural networks is by showing it many images with the corresponding label.…
Introduction In this article, I’m going to review the Foundations of AI and Machine Learning for Java Developers video course from my fellow Java Champion, Frank Greco. If you are new to AI and ML and want to get a great introduction to these topics, then you should definitely join watch the video lessons created by Frank Greco. And, thanks to LinkedIn Learning’s generosity, until June 20, you can enroll in this video course for free. Video Course Agenda The course provides one hour and thirty-five minutes of video lessons that are... Read More The post Foundations of AI and Machine Learning…
Tilman Drerup, Moe Moazzami, Shih-Ting Lin, Greg Reda (and many more)IntroductionAt Instacart, artificial intelligence is fundamentally changing the way our machine learning engineers operate. In a prior blog post, we used one of our teams as a case study to illustrate how the emergence of agents has reshaped what machine learning engineers spend their time on. The post below goes a few levels deeper and zooms in on the machine learning modeling process itself, an area where recent developments in AI-assisted research have opened up exciting new frontiers that we are now actively exploring.…
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There've been regular viral stories about ML/AI bias with LLMs and generative AI for the past couple years. One thing I find interesting about discussions of bias is how different the reaction is in the LLM and generative AI case when compared to "classical" bugs in cases where there's a clear bug. In particular, if you look at forums or other discussions with lay people, people frequently deny that a model which produces output that's sort of the opposite of what the user asked for is even a bug. For example, a year ago, an Asian MIT grad student asked Playground AI (PAI) to "Give the girl…
By Jean V. Alves and Ferran Pla FernándezMoving beyond binary classification provides novel insights.In the real world, scams rarely present themselves in black and white. Fraudsters exploit nuance, impersonate legitimate brands, and mask malicious intent with seemingly ordinary behavior. That’s why Feedzai has launched ScamAlert (patent pending), a Generative AI-based system innovating on the current paradigm of scam prevention, in response to this growing challenge.Traditional detection systems treat the problem as a binary choice: scam or not a scam, often outputting an estimated “scam…
Why we think LLMs can be useful and why we will not replace all of our models with themContextAt Medium, we have many Machine Learning models that we use to label stories automatically. These affect what stories we recommend to readers.Here’s some examples:a few of our text classification models. All diagrams and charts made by the authorSome Clarifications on our Machine Learning policyBefore we go deep on this project, I just wanted to clarify a few things about how we stand regarding AI in general.Medium has been training internal models with user and post data for a long time now. We…
IntroductionOver the years, we have evolved from using simple, often rule-based algorithms to sophisticated machine learning models. These models are incredibly good at finding patterns in large datasets, but due to their complexity it is frequently challenging for a human to understand why a certain input leads to its respective output. This is especially problematic in areas where high-stakes decisions are being made and where human-AI collaboration is critical.This is why model explainability has gained traction in recent years. The aim of explainability methods is to shed light on what…
* For years, despite functional evidence and scientific hints accumulating, certain AI researchers continued to claim LLMs were stochastic parrots: probabilistic machines that would: 1. NOT have any representation about the meaning of the prompt. 2. NOT have any representation about what they were going to say. In 2025 finally almost everybody stopped saying so. * Chain of thought is now a fundamental way to improve LLM output. But, what is CoT? Why it improves output? I believe it is two things: 1. Sampling in the model representations (that is, a form of internal search). After information…
Authors: Longyu Zhao (Staff Machine Learning Engineer), Gwendolyn Zhao (Staff Machine Learning Engineer), Peng Yan (Senior Machine Learning Engineer), Yuanlu Bai (Senior Machine Learning Engineer), Yuan Wang (Senior Machine Learning Engineer), Yao Cheng (Staff Machine Learning Engineer), Ang Xu (Principal Machine Learning Engineer), Zhaohong Han (Manager II, Ads Lightweight Ranking)IntroductionPreviously¹, we launched the next-generation serving stack for standard ads, which we call Nexus. Nexus decoupled candidate generation from scoring and moved us beyond the classic two-tower-only world,…
LLMs, MCPs, RAG. There are lots of acronyms in the AI space, but what do they all mean? Dear reader, despite being a software engineer who works in the machine learning space, I confess there was a time I wasn’t really sure. Fortunately, with the financial support of Deliveroo’s Women-in-Tech Employee Resource Group, I took the Ardan Labs Building AI-Powered Applications in Go workshop that helped me understand what’s really going on behind the chat interface and where software engineering meets LLM-based applications. We went through a series of modules to incrementally build a RAG…
My colleague and I just wrapped up a live series on Python + AI, a nine-part journey diving deep into how to use generative AI models from Python. I gave the english streams while my colleague Gwen gave the spanish streams (and I hung out in her live chat, working on my technical spanish!). The series introduced multiple types of models, including LLMs, embedding models, and vision models. We dug into popular techniques like RAG, tool calling, and structured outputs. We assessed AI quality and safety using automated evaluations and red-teaming. Finally, we developed AI agents using popular…
My colleague and I just wrapped up a live series on Python + AI, a nine-part journey diving deep into how to use generative AI models from Python. I gave the english streams while my colleague Gwen gave the spanish streams (and I hung out in her live chat, working on my technical spanish!). The series introduced multiple types of models, including LLMs, embedding models, and vision models. We dug into popular techniques like RAG, tool calling, and structured outputs. We assessed AI quality and safety using automated evaluations and red-teaming. Finally, we developed AI agents using popular…
Introduction In an era where data privacy is paramount and artificial intelligence continues to advance at an unprecedented pace, Federated Learning (FL) has emerged as a revolutionary paradigm. This innovative approach allows multiple entities to collaboratively train a shared prediction model without exchanging their raw data. Imagine scenarios where hospitals
Authors : Marsan Ma, Nikhil Lopes, Raj Amrit, Hong Lu, Dipankar Biswas, Trent KyonoLeadership: Iris Wang, Madhu Kurup Recommendation and ranking systems power many of the most important experiences on large internet platforms. Yet the models that run in production are rarely the largest models we can train. They are usually compact, latency-sensitive supervised models […]
Photo by Deng Xiang on UnsplashOne in a Million Ways to Detect Customer Churn — Powered by Pure Metric EngineeringA real-world case study on building a Churn Intelligence Framework using revenue dynamics, structured KPI design, and behavioral transitions.😯 Wow, Churn Prediction sounds impressivePhoto by Ksenia Yakovleva on UnsplashUntil you realize that most ML models struggle in production. — Data fluctuates 🔢 — Features change 💱 — Stakeholders don’t trust black-box outputs ⬛Teams jump into feature engineering and classification algorithms, chasing accuracy scores — while the business…
Explore new AI features and AI tools: support for IBM Granite Time Series models and TimesFM models (EA), enhanced Real-Time Context Engine experience, new Agent Skills, Confluent Copilot
As enterprise generative AI applications move to production, platform engineers face a key challenge: balancing the flexibility of LLM-as-a-judge guardrails with the reliability and portability of traditional classifiers that require custom training data. The recent emergence of "decision models"—highlighted by TypeSafe AI's recent announcement of Jev and “System One” models—promises a flexible middle ground by producing fixed “decisions” given a state and a list of questions rather than generating text. The post Benchmarking AI decision models against traditional guardrails appeared first on…
I didn’t expect DwarfStar 4 (https://github.com/antirez/ds4) to become so popular so fast. It is clear that there was a need for single-model integration focused local AI experience, and that a few things happened together: the release of a quasi-frontier model that is large and fast enough to change the game of local inference, and the fact that it works extremely well with an extremely asymmetric quants recipe of 2/8 bit, so that 96 or 128GB of RAM are enough to run it. And, of course: all the experience produced by the local AI movement in the latest years, that can be leveraged more…
Text classification is often done through fine-tuning of a pretrained foundation model with domain-specific data. In FreeAgent we use transformer based models to automatically classify incoming bank transactions. Specifically we use a DistilBERT model that is fine-tuned on hundreds of millions of bank transactions with customer-labelled accounting categories. The model inputs are currently text-based, built from a combination of bank transaction descriptions and amounts. In this post we describe an approach to fine-tuning the DistilBERT model and training the classifier including the…
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Let me paint you a picture. You have 16 NVIDIA H200 GPUs spread across 2 nodes. That is, conservatively, several 100,000 dollars of silicon sitting in a data center, connected by RDMA/InfiniBand, running Kubernetes, and serving a large language model. You're living the agentic dream (not really, but it's a great start). Except your time to 1st token (TTFT) is thousands of milliseconds. The post How I massively improved my AI inference performance without buying new hardware appeared first on Red Hat Developer.
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