# Data & ML Published articles for Data & ML. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Fine-tuning a DistilBERT classifier with numerical and text inputs DevFeed: [Fine-tuning a DistilBERT classifier with numerical and text inputs]() Original publisher: [Read original article]() Author: Paloma Jol Published: 2026-04-10T10:04:33Z Content type: tutorial Language: en Sources: [FreeAgent]() Topics: [Fine-tuning](), [Transformer](), [foundation-models](), [Training AI Models](), [datasets](), [Kaggle](), [scikit-learn]() Tags: [ai](), [aws](), [data](), [data-ml](), [data-science](), [dataset](), [encoding](), [fine-tuning](), [hugging-face](), [kaggle](), [llms](), [machine-learning](), [model](), [models](), [nlp](), [performance](), [text-classification](), [training-data](), [validation]() ### AI overview This tutorial explains how to fine-tune a DistilBERT text classifier while incorporating a numerical feature into the same network. It uses the Kaggle wine reviews dataset, combining review descriptions with price and preprocessing the data for training, validation, and testing. ### Source excerpt 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 numerical amount feature as a single network. ## Structured outputs with Pydantic AI DevFeed: [Structured outputs with Pydantic AI]() Original publisher: [Read original article]() Author: Ed Berry Published: 2026-03-24T10:34:08Z Content type: tutorial Language: en Sources: [FreeAgent]() Topics: [Large Language Model](), [JSON Schema](), [Prompt Engineering](), [Pydantic](), [Python](), [Structured-data]() Tags: [ai](), [data](), [data-ml](), [data-science](), [data-validation](), [genai](), [json-schema](), [llms](), [prompt-engineering](), [python](), [schema](), [structured-output]() ### AI overview This article explains structured outputs for LLMs using Pydantic AI. It introduces the relationship between Python type hints, static analysis, Pydantic, and runtime data validation, while noting that Pydantic AI is model-agnostic. ### Source excerpt One of the challenges of working with LLMs is getting them to respond with a consistent format, such as a given JSON schema. Anyone who has tried to solve this issue with prompt engineering knows how frustrating it can be. You add a 'MUST' here and an 'always return JSON' there, but still the output [...] ## How we Use Dagster Automations in our Data Pipeline DevFeed: [How we Use Dagster Automations in our Data Pipeline]() Original publisher: [Read original article]() Author: Delphine Rabiller Published: 2025-12-10T11:32:42Z Content type: article Language: en Sources: [FreeAgent]() Topics: [Automation](), [data](), [migration]() Tags: [analytics-engineering](), [automation](), [dagster](), [data-ml](), [data-pipeline](), [data-pipelines](), [declarative](), [migration](), [pipeline](), [scheduled](), [sensors](), [tooling](), [triggers]() ### AI overview This engineering post explains how FreeAgent is migrating data pipelines to Dagster and re-architecting its automation logic. It describes three approaches to automating asset materialization: schedules, declarative automation, and asset sensors, including when schedules are appropriate and how declarative automation uses asset dependencies and materialization status. ### Source excerpt Introduction The heart of a reliable data platform are robust and automated data pipelines. As our team migrates our data pipelines to Dagster, re-architecting our automation logic is a crucial task. Dagster offers condition-based approaches to creating or updating a data asset (table or file), moving us toward a modern, asset-centric view of data. This [...] ## How to Choose a Data or Machine Learning Project for Your Portfolio DevFeed: [How to Choose a Data or Machine Learning Project for Your Portfolio]() Original publisher: [Read original article]() Author: Meri Nova Published: 2024-04-16T17:27:02Z Content type: tutorial Language: en Sources: [Meri Nova]() Topics: [Data Science](), [data](), [Machine learning](), [datasets](), [A/B Testing]() Tags: [data-ml](), [data-science](), [datasets](), [how-to](), [pitfalls](), [project]() ### AI overview This tutorial presents a six-step framework for choosing a data or machine learning project and building a first data portfolio. It emphasizes aligning projects with target roles, selecting work that sustains motivation, and using messy, realistic datasets instead of only popular clean datasets. ### Source excerpt Follow the 6-step framework to save 20+ hours and gain clarity in your approach. ## Algo Hour - Large Scale Data & ML Monitoring with whylogs | Alessya Visnjic DevFeed: [Algo Hour - Large Scale Data & ML Monitoring with whylogs | Alessya Visnjic]() Original publisher: [Read original article]() Published: 2022-09-29T09:00:00Z Content type: article Language: en Sources: [Stitch Fix]() Topics: [Data Quality](), [data observability](), [Monitoring](), [Machine learning](), [ai observability]() Tags: [data](), [data-ml](), [data-pipelines](), [data-quality](), [monitoring](), [observability](), [open-source]() ### AI overview This talk explains how the open-source whylogs library supports end-to-end data quality and monitoring across machine learning pipelines. It covers whylogs' lightweight statistical data collection, language- and platform-agnostic approach, architecture, and application to existing data and ML pipelines. ### Source excerpt Title: Large Scale Data & ML Monitoring with whylogs Talk Abstract: In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. An open source library called whylogs is built to address these challenges. It is a lightweight data profiling library that enables end-to-end data monitoring across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It's been deployed at massive-scale data environments, on structured and unstructured data modalities, and across a range of points in the ML lifecycle. In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and we will show how users can apply this library to existing data and ML pipelines. Date and Time: The talk will be held on Tuesday, October 11th at 1:00PM PDT. Recording Info: This talk was recorded live and is viewable below: Speaker Info: Alessya Visnjic is the CEO of WhyLabs, the AI Observability company building tools that power robust and responsible AI deployment. Prior to WhyLabs, Alessya was a CTO-in-residence at the Allen Institute for AI, where she evaluated commercial potential for the latest AI research. Earlier, Alessya spent 9 years at Amazon leading ML initiatives, including forecasting and data science platforms. Alessya is also the founder of Rsqrd AI, a global community of 1,000+ AI practitioners who are committed to making enterprise AI technology responsible.