Machine-Learning

Implementing Gemini Text Embeddings for Production Applications Note: This guide is based on Google Generative AI API documentation, Gemini embedding model specifications (text-embedding-004 released March 2025), and documented RAG (Retrieval-Augmented Generation) patterns. All code examples use the official google-generativeai Python SDK and follow Google Cloud best practices. Text embeddings transform text into dense vector representations that capture semantic meaning, enabling applications like semantic search, document clustering, and Retrieval-Augmented Generation (RAG). Google’s Gemini embedding models, particularly text-embedding-004 released in March 2025, provide state-of-the-art performance with configurable output dimensions and task-specific optimization. ...

Research Disclaimer This tutorial is based on: Stable-Baselines3 v2.2+ (PyTorch-based RL algorithms) Gymnasium v0.29+ (successor to OpenAI Gym) RLlib v2.9+ (Ray distributed RL) Optuna v3.5+ (hyperparameter optimization) Academic RL papers: PPO (Schulman et al., 2017), DQN (Mnih et al., 2015), A2C (Mnih et al., 2016) TensorBoard v2.15+ and Weights & Biases (monitoring) All code examples are production-ready implementations following documented best practices. Examples tested with Python 3.10+ and work on both CPU and GPU. Stable-Baselines3 is the most actively maintained RL library as of 2025. ...

AI Fairness in Practice: Detecting and Mitigating Bias in Machine Learning Note: This guide is based on fairness research including “Fairness and Machine Learning” by Barocas et al., AI Fairness 360 (IBM Research), Fairlearn (Microsoft), and documented case studies from COMPAS recidivism algorithm analysis. All code examples use established fairness metrics and follow industry best practices for responsible AI. AI bias has real-world consequences: Amazon’s recruiting tool penalized resumes mentioning “women’s” activities, COMPAS criminal risk assessment showed racial disparities, and healthcare algorithms under-allocated resources to Black patients. As ML systems increasingly make high-stakes decisions about loans, jobs, and parole, detecting and mitigating bias is not just ethical—it’s legally required under regulations like GDPR and the EU AI Act. ...

Scalable Serverless AI/ML Pipelines: A Production Guide Research Disclaimer: This guide is based on AWS SDK for Python (boto3) v1.34+, SageMaker Python SDK v2.200+, and AWS Step Functions State Language (Amazon States Language) official documentation. All code examples follow AWS Well-Architected Framework for ML workloads and include production-tested patterns for serverless deployment, monitoring, and cost optimization. Serverless ML pipelines eliminate infrastructure management while providing automatic scaling, pay-per-use pricing, and high availability. This guide covers production-ready patterns for deploying ML models using AWS Lambda, SageMaker, Step Functions, and EventBridge, with complete working examples that you can deploy immediately. ...

As a SOC analyst, one of the most critical tasks is analyzing network flow data to identify potential security threats. In this post, we’ll explore how to combine cloud-based data storage, SQL querying, and AI-powered analysis to streamline this process. Collecting Flow Data in Amazon Athena Amazon Athena provides a serverless query service that makes it easy to analyze data directly in Amazon S3 using standard SQL. Here’s how we set up our flow data collection: ...