#machine-learning (85)
- Scaling Social Discovery on Facebook Reels with Friend Bubbles
Learn how Meta engineered Friend Bubbles, addressing architectural challenges and machine learning evolution to scale social discovery on Facebook Reels.
- Integrate a Tiny Local LLM for Edge Device Language Understanding
Integrate a tiny, quantized LLM directly onto an edge device to enable real-time, privacy-preserving natural language understanding without cloud dependency.
- Understanding Tiny LLM and On-Device AI Agent Internals
You will understand the architectural decisions, optimization techniques, and inference mechanisms that power tiny LLMs and on-device AI agents.
- Apple, Meta, OpenAI Multimodal Embedding Models Compared
Learn to objectively compare multimodal embedding models from Apple, Meta, and OpenAI to make an informed choice for your AI applications.
- Data Science Platforms and Tools: Complete Comparison 2026
Comprehensive comparison of 18 leading Data Science platforms and tools - features, performance, pros & cons, and when to use each.
- Actor-Verifier Reasoning: Trustworthy AI for Safety-Critical Systems
Implement Actor-Verifier AI to build highly reliable and interpretable systems for safety-critical domains, ensuring robust, evidence-based decision-making.
- Unlocking Enterprise Innovation with Open-Source AI in 2026
Explore the transformative impact of open-source AI on enterprise innovation in 2026, covering key trends, benefits, challenges, and strategic considerations for developers.
- Designing Scalable AI Systems: An Architectural Guide
Learn to design robust, scalable, and production-ready AI-powered applications, covering pipelines, orchestration, microservices, distributed architectures, and modern AI trends.
- AI-Powered Monitoring, Observability, and Alerting
Explore how AI transforms monitoring and observability in DevOps, enabling predictive analytics, anomaly detection, and intelligent alerting for more resilient systems.
- MLOps Essentials: Bridging Machine Learning and DevOps
Understand the core principles and lifecycle of MLOps, bridging machine learning development with robust DevOps practices for reliable AI systems.
- Build an AI Anomaly Detector for Production Metrics
You will learn to build an AI-driven anomaly detector for production metrics using Python and scikit-learn to identify unusual patterns.
- AI in DevOps Workflows Guide
Unlock the power of AI in DevOps. Learn to integrate AI into CI/CD, automate code reviews, validate deployments, enhance monitoring, and streamline infrastructure automation effectively.
- Introduction to AI System Design: Principles & Foundations
Dive into the core principles of AI system design, understanding what makes AI applications unique and how to lay a solid foundation for scalable, reliable, and observable AI solutions.
- Designing Scalable AI Systems
Learn to design scalable AI applications covering pipelines, orchestration, microservices, and distributed architectures with real-world examples.
- Personalization & Recommendations: The Brain Behind Your Feed
Explore the complex architecture behind Netflix's personalization and recommendation systems, including data flows, model ensembles, and design tradeoffs for delivering a unique user experience.
- 13. AI-Powered Services with Void Cloud
Discover how to integrate and deploy AI-powered services on Void Cloud, from serverless functions to edge inference, with practical examples and best practices.
- Face Detection and Alignment: The First Steps
Dive into the fundamental processes of face detection and alignment using the conceptual UniFace toolkit. Learn how to locate faces and standardize their appearance for robust biometric analysis.
- Build Your First Face Recognition Model in Python
Develop your first face recognition model in Python, understanding face detection, feature extraction, and comparison through hands-on coding.
- AI-Powered Systems: Debugging Models & Data Pipelines
Master debugging techniques for AI models and data pipelines, covering data quality, model performance, prompt engineering, and observability in modern AI systems.
- Integrating AI & Agentic Features
Explore integrating on-device AI with Core ML, leveraging cloud-based AI APIs, and designing agentic features for intelligent user interactions in iOS apps using modern Swift 6 and Xcode 16+.
- Containerize an ML Workflow Natively on macOS with Apple Tools
Master containerizing a full machine learning workflow on macOS using Apple's native tools, ensuring reproducible data preparation, model training, and persistence.
- Vector Distance Metrics and Their Impact
Explore vector distance metrics like Euclidean, Cosine, and Dot Product, understanding their role in USearch and ScyllaDB for accurate similarity search.
- Scaling ScyllaDB Vector Search for Billions of Vectors
Unlock the power of ScyllaDB and USearch to build highly scalable vector search solutions capable of handling billions of vectors with low latency and high throughput.
- Advanced USearch Features: Quantization & Compression
Dive into advanced USearch features: quantization and compression. Optimize vector search for memory, speed, and scale, balancing accuracy with performance in your AI applications.
- Building a Movie Recommendation System
Learn to build a real-time movie recommendation system using USearch for efficient vector similarity search and ScyllaDB for scalable, low-latency vector storage.
- Fraud Detection with Vector Similarity
Learn to apply USearch and ScyllaDB for real-time fraud detection using vector similarity, building on prior knowledge of embeddings and scalable databases.
- Understanding AI Agents for Customer Service Transformation
Readers will learn the fundamental concepts of AI agents, their core components, and how they revolutionize customer service beyond traditional chatbots.
- OpenAI Agents for Customer Service: Build & Deploy
Learn to design, develop, and deploy sophisticated AI agents powered by OpenAI, transforming customer service operations with intelligent, automated solutions.
- AI & Agentic AI in React & React Native Frontend
Learn to integrate AI and agentic AI into React and React Native frontend applications for smarter, more engaging user experiences.
- Mastering Tunix: JAX-Native LLM Post-Training Library
Learn to effectively use Tunix, a JAX-native library, for LLM post-training, covering its setup, core features, advanced applications, and best practices.
- Advanced Data Governance & Security
Learn about advanced data governance and security measures to protect sensitive datasets in machine learning projects.
- Master Meta AI's Open-Source ML Library for Dataset Management
Gain expertise in Meta AI's open-source machine learning library for dataset management, from foundational setup to advanced workflow integration.
- Data Parsing and Structure Extraction with OpenZL
Learn how to use OpenZL for efficient data parsing and structure extraction, unlocking better compression ratios.
- Kiro's Four-Layer Architecture Explained
Explains the four-layer architecture of AWS Kiro, a powerful AI-driven development tool.
- Performance Tuning and Optimization for Kiro
Learn how to optimize AWS Kiro for better performance, cost-effectiveness, and smarter AI solutions.
- Learn AI & Machine Learning Concepts Without Code
Explore core AI and Machine Learning concepts from the ground up, gaining intuitive understanding and real-world knowledge without needing any prior coding experience.
- AI & ML Unplugged: What's the Big Idea?
An introduction to Artificial Intelligence and Machine Learning, explaining their core ideas without requiring prior coding experience.
- Building Brains: The Concept of a Model
An in-depth explanation of what an AI or Machine Learning model is, its role in decision-making, and how it learns from data.
- How Machines Learn: Training and Prediction Explained
Explains the fundamental concepts of training and prediction in machine learning, using everyday examples.
- Is Our Model Good? Introduction to Evaluation Metrics
Learn how to evaluate machine learning models using metrics like Accuracy, Precision, and Recall with Python.
- Learn Neural Networks & Deep Learning Core Concepts
Grasp the fundamental concepts of neural networks and deep learning, understanding how individual neurons and layered structures function, with a conceptual Python example.
- AI in Action: Real-World Use Cases and Impact
An overview of real-world applications and impacts of Artificial Intelligence, connecting theoretical concepts to practical scenarios.
- Ethical AI: Responsibility and Fairness
An introduction to ethical considerations in AI, focusing on bias, fairness, transparency, and accountability.
- The Road Ahead: Future of AI & Career Paths
Explore the future of AI, ethical considerations, and diverse career paths for those interested in this rapidly evolving field.
- Your Next Steps: Continuing the Learning Journey
Explore the next steps in your AI and Machine Learning journey with tips on continuous learning, technical skills, and career growth.
- AI All Around Us: Real-World Stories
Explore how AI and ML are already part of our daily lives through real-world examples.
- Understanding How AI Predicts Outcomes from Data
Learn how artificial intelligence makes informed predictions by recognizing patterns within data, empowering better decisions and new insights in daily applications.
- The Future of AI & Your Place in It
An exploration of the future of AI and how beginners can contribute to this transformative technology.
- Building a Simple Predictor (Conceptually)
Learn the fundamental concept of prediction in AI and Machine Learning through a simple example.
- Data: The Fuel for AI's Brain
Understanding the crucial role of data in AI and ML, including types of data and why it's essential.
- Experiment with AI: No-Code Tools and Interactive Playgrounds
Readers will learn to interact with and train simple AI models using no-code tools and interactive playgrounds, solidifying their understanding of core AI concepts.
- Your First Steps in Programming for AI
This lesson teaches core programming concepts, enabling you to write your first Python code and instruct computers, which is essential for future AI development.
- Welcome to the World of AI & ML
An introduction to Artificial Intelligence and Machine Learning, explained simply for beginners.
- Your First AI Project: No Code Magic!
Learn how to build your first AI project without writing a single line of code using Teachable Machine.
- Models: AI's Rulebook or Mental Map
Explains the concept of AI models as rulebooks or mental maps, using everyday examples and analogies.
- AI Ethics: Thinking About What's Right
An introduction to AI ethics, exploring fairness, safety, transparency, and respect for human values in AI development and usage.
- Supervised vs. Unsupervised Learning: Two Ways AI Learns
Explains the fundamental concepts of supervised and unsupervised learning in AI, using simple analogies.
- Training an AI: Practice Makes Perfect
Learn how to train an AI model using simple steps and a visual tool, making it smarter with each iteration.
- What is AI, Really? (Beyond Sci-Fi)
Explains the basics of Artificial Intelligence and Machine Learning in a simple, easy-to-understand way.
- The AI/ML Landscape & Foundational Math
Learn the basics of AI/ML and foundational math with practical examples in Python.
- Python for AI/ML: A Deep Dive
Learn the basics of Python for AI/ML, setting up an environment and understanding core concepts.
- Data Science Toolkit: NumPy, Pandas, Matplotlib
Learn the essential libraries for data science: NumPy, Pandas, and Matplotlib. Understand their core functionalities and why they are indispensable.
- Introduction to Classical Machine Learning
Learn the basics of classical machine learning with Python and scikit-learn, including regression, classification, and model evaluation.
- Deep Learning Fundamentals & Neural Networks
Learn the basics of deep learning and neural networks through a step-by-step tutorial.
- Research Literacy & Staying Current in AI
Learn how to navigate the fast-paced AI landscape through research literacy and staying current with new paradigms.
- Building a Semantic Search Engine Using Embeddings
Readers will learn to build a semantic search engine from scratch, preparing data, generating embeddings, and performing intelligent searches for relevant results.
- Professional Development & Career Guidance
Strategic insights for transitioning from AI/ML learner to a successful professional in 2026 and beyond.
- Foundations of Applied AI: Python & System Thinking
Learn the fundamentals of Applied AI with Python, setting up your environment, and cultivating system thinking.
- Designing AI-Driven Workflows & Complex Agent Patterns
Learn how to design and orchestrate complex AI-driven workflows using multi-agent systems.
- Multi-Pass Extraction and Refinement
Learn how to use LangExtract for multi-pass extraction and refinement of complex documents.
- Deploying LangExtract for Production
Learn how to deploy LangExtract in a production environment for reliable, efficient, and scalable data extraction.
- Routing
Explains the concept of routing in AI agents, enabling dynamic decision-making based on input and context.
- Conclusion
Comprehensive overview of agentic AI patterns and their role in building intelligent systems.
- Planning
Explains the Planning pattern in AI, its adaptability, and practical applications.
- What makes an AI system an \"agent\"?
Explains the concept of AI agents, their functions, and their growing importance in technology and business.
- Build a Real-time Supply Chain Platform with Databricks Lakehouse
Develop a real-time supply chain intelligence platform using Databricks Lakehouse to gain visibility, predict delays, and analyze tariff impacts.
- Databricks: From Zero to Production-Ready Solutions
Learn to build robust, scalable data solutions using Databricks from zero to production-ready.
- JSON and TOON: Data Formats for AI and LLM Development
Understand the syntax and structure of JSON and TOON, allowing you to design and implement efficient data communication for AI and Large Language Models.
- Bonus Section: Further Learning and Resources
Expand your knowledge with resources for JSON and TOON in AI, including documentation, courses, and blogs.
- Core Concepts of Semantic Caching
Explains the principles and implementation of semantic caching using Redis LangCache, focusing on embeddings and vector similarity.
- Learn Transformers.js: Revolutionizing AI in the Browser
Learn how to use Transformers.js to integrate powerful AI capabilities into web applications directly in the browser.
- Introduction to Transformers.js
Learn how to use Transformers.js, a JavaScript library for running state-of-the-art machine learning models in the browser.
- Sentiment Analysis with Transformers.js and JavaScript
Learn to build a practical sentiment analyzer using Transformers.js and JavaScript, enabling you to classify text and interpret emotional tone accurately.
- Mastering Advanced Agentic AI for Production UI & Backend
Master the intricacies of building, deploying, and managing advanced agentic AI systems for production UI and backend applications.
- Web Developer's Guide to Pandas Data Analysis in Python
Web developers will learn to effectively use Python's Pandas library to load, clean, transform, and analyze tabular data with confidence.