A guide on using Microsoft's Phi-3 Small Language Model with C# and Semantic Kernel for local AI applications.
A summary of a talk on applying Large Language Models (LLMs) to build and deploy recommendation systems at scale, presented at Netflix's PRS workshop.
A developer's experience using ChatGPT 4 as a tool for exploring and learning new technical concepts, from programming to machine learning.
A developer's investigation into unexpected cost spikes from a Vertex AI resource pool, with a cautionary guide on managing cloud ML resources.
Explains kernel ridge regression and scaling RBF kernels using random Fourier features for efficient large-scale machine learning.
Explains data leakage in ML, why it's harmful, and how to prevent it when using pandas and scikit-learn for tasks like missing value imputation.
Explores a closed-form solution for linear metric learning, deriving a transformation matrix to align feature distances with response distances.
A guide to running Python code on serverless GPU instances using Modal.com for faster machine learning inference, demonstrated with a speech-to-text example.
Explores methods for using and finetuning pretrained large language models, including feature-based approaches and parameter updates.
Explores the application of diffusion models to video generation, covering technical challenges, parameterization, and sampling methods.
Explores the pros and cons of discretizing continuous features in machine learning, with a practical guide using scikit-learn's KBinsDiscretizer.
A developer's journey building a TV show recommendation engine using AWS SageMaker, from data collection to model deployment.
A study guide for the Microsoft AI-900 Azure AI Fundamentals exam, covering AI workloads, machine learning, and generative AI.
Announces the addition of 6 new R programming books to the Big Book of R collection, covering statistics, machine learning, and data science.
An analysis of 900 popular open-source AI tools, categorizing them into infrastructure, model development, and application layers.
A monthly tech digest covering Meta's DotSlash tool, AI-powered code reviews, AWS Lambda scaling, observability trends, and Cloudflare's logging pipeline.
Explains why mocking ML models in unit tests is problematic and offers guidelines for effectively testing machine learning code.
Explores the gap between generative AI's perceived quality in open-ended play and its practical effectiveness for specific, goal-oriented tasks.
Explores the importance of high-quality human-annotated data for training AI models, covering task design, rater selection, and the wisdom of the crowd.
Explains key AI model generation parameters like temperature, top-k, and top-p, and how they control output creativity and consistency.
