#mlops (80)
- Implement Evals Frameworks for AI Agent Reliability
Design and implement robust evaluation frameworks to systematically measure and improve AI agent reliability, performance, and overall dependability.
- Implement a 12-Metric Evaluation Harness for Production AI Agents
Learn to build a robust 12-metric evaluation harness for production AI agents, ensuring consistent performance and reliability at scale.
- The Build vs. Buy Decision for AI Model Evaluation
Evaluate the hidden costs of custom AI model evaluation tools against the value of investing in a specialized commercial platform.
- Systems Thinking for Resilient AI & Agentic Architectures
Readers will learn to apply systems thinking and navigate architectural tradeoffs to design resilient, scalable, and maintainable AI and agentic workflows.
- Deployment, Maintainability, and Expanding Edge AI Agent Concepts
Learn production-grade deployment strategies, maintainability best practices, and advanced concepts for evolving on-device AI agents and tiny LLM systems.
- Edge AI Agent & Tiny LLM Projects for On-Device Apps
Build intelligent, autonomous AI agent and tiny LLM applications directly on edge hardware using modern edge AI tooling and frameworks.
- Build and Deploy Robust AI Systems for Production
Learn to build, deploy, and maintain robust, scalable AI systems, covering MLOps, LLMOps, and best practices for production-ready applications.
- Integrating AI into DevOps Workflows: An Essential Guide
Learn how to integrate Artificial Intelligence into DevOps practices, enhancing CI/CD, code review, deployment, monitoring, and infrastructure automation.
- AI Observability: A Practical Guide to Monitoring AI Systems
Learn to implement robust AI observability for production systems, covering logging, tracing, metrics, cost monitoring, and debugging of AI models and LLMs.
- Ensuring AI Reliability: Evaluation and Guardrails
Learn to test, validate, and implement robust guardrails for AI systems, covering prompt testing, hallucination detection, and production-grade safety strategies.
- 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.
- Deploy and Manage Large Language Models in Production
Learn to deploy, manage, and optimize Large Language Models in production, covering inference, scaling, monitoring, and cost-efficient LLMOps practices.
- AI for Automated Code Review and Quality Gates
Explore how AI transforms automated code review and quality gates within DevOps workflows, enhancing code quality, security, and developer efficiency with practical insights and examples.
- AI-Enhanced Deployment Validation and Rollouts
Learn how AI can enhance deployment validation and automate intelligent rollouts, covering anomaly detection, canary analysis, and predictive monitoring for robust software delivery.
- How AI Transforms Software Delivery and Operations
Learn how Artificial Intelligence fundamentally transforms DevOps workflows, enabling smarter, faster, and more resilient software delivery processes.
- AIOps in Action: Automating Infrastructure with Intelligence
Dive into AIOps, learning how to leverage AI for predictive infrastructure monitoring, automated incident response, and self-healing systems in cloud environments.
- The Future Horizon: Emerging Trends and Challenges in AI DevOps
Explore the cutting-edge trends, emerging challenges, and critical considerations for the future of AI in DevOps, focusing on responsible innovation.
- 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.
- Model Governance and Data Management for MLOps Maturity
Learn the critical concepts of Model Governance and Data Management to achieve MLOps Maturity, ensuring reliable, ethical, and reproducible AI systems in your DevOps workflows.
- Responsible AI in DevOps: Ethics, Bias, and Explainability
Explore Responsible AI in DevOps, covering ethical considerations, bias mitigation, and the importance of explainability for AI-driven automation in CI/CD, monitoring, and operations.
- Setting Up Your AI-Powered DevOps Workbench
Prepare your development environment for integrating AI into DevOps workflows. Learn to set up Python, virtual environments, essential AI/ML libraries, and cloud tooling.
- Accelerate CI with AI-Driven Testing and Build Optimization
Implement AI in CI workflows to intelligently prioritize tests, predict build failures, and optimize processes for improved efficiency and reliability.
- OpenTelemetry Tracing for AI Observability in Python
Readers will learn to instrument Python AI applications with OpenTelemetry to collect traces, enabling deep insights into system performance and behavior.
- Debugging AI: Pinpointing Issues in Prompts, Models, and Data
Learn how to effectively debug AI systems in production by pinpointing issues in prompts, model behavior, and data, using practical observability techniques and OpenTelemetry.
- Hands-On Project: End-to-End AI Observability Implementation
Build a practical AI observability system from scratch! Learn to instrument an LLM application with OpenTelemetry for tracing, metrics, and logs, then visualize everything in SigNoz and Grafana.
- Structured Logging for AI: Capture Key Interaction Data
Learn to implement structured logging in AI applications, capturing crucial interaction data to enhance monitoring and debugging capabilities.
- Monitor AI Model Performance and System Health with KPIs
Learn to define, collect, and interpret key metrics for AI model performance, cost, and operational health using practical Python examples.
- Real-time Insights: Dashboards, Alerting, and Anomaly Detection
Learn how to build real-time dashboards, set up proactive alerts, and implement anomaly detection for AI systems using tools like Prometheus and Grafana, focusing on AI-specific metrics.
- AI Observability: Why It Matters and How It Works
Discover the essential principles of AI observability, its unique challenges, and how to apply them for reliable, high-performing AI applications.
- Implement AI Observability for Production AI Systems
Implement robust AI observability by tracking prompts, responses, and performance to ensure your AI models operate reliably in production.
- Regression Testing for AI: Preventing Unintended Consequences
Discover how to implement robust regression testing strategies for AI systems to prevent unintended consequences, maintain performance, and ensure reliability in production.
- The Imperative of AI Reliability: Evaluation & Guardrails
Discover why AI reliability, through robust evaluation and proactive guardrails, is essential for building safe, trustworthy, and effective AI systems in production.
- Continuous Monitoring & MLOps for AI Reliability in Production
Learn how to establish robust continuous monitoring and MLOps practices to ensure the ongoing reliability, safety, and performance of AI systems in production environments.
- Foundations of AI System Evaluation: Metrics & Benchmarking
Explore the foundational concepts of AI system evaluation, including critical metrics for various AI tasks and robust benchmarking strategies to ensure reliability and performance.
- AI System Evaluation and Guardrails Guide
Ensure AI system reliability with this guide on testing, validation, and guardrail design. Learn prompt testing, hallucination detection, output validation, and real-world production strategies.
- Building AI/ML Pipelines: From Data to Deployment
Explore the foundational concepts of AI/ML pipelines, from data ingestion and preparation to model training, deployment, and continuous monitoring, crucial for scalable AI applications.
- Case Study: Architecting a Real-time Recommendation Engine
Learn to design a scalable, real-time recommendation engine using microservices, event-driven architecture, and distributed AI principles with practical examples.
- Building Reliable AI with Data Quality and Model Trustworthiness
Learn to design and deploy AI systems that are robust, fair, and transparent by mastering data quality, model trustworthiness, and responsible AI principles.
- Distributed AI: Scaling Training and Inference Across Resources
Explore Distributed AI architectures for scaling model training and inference. Learn about data and model parallelism, horizontal scaling, and fault tolerance in production AI systems.
- Observability for AI Systems: Monitoring, Logging & Tracing
Master observability for AI systems: understand monitoring, structured logging, distributed tracing, and ML-specific metrics to build robust, scalable, and reliable AI applications.
- 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 Secure, Private, and Responsible AI Systems
Learn to design AI systems that protect sensitive data, respect user privacy, resist attacks, and adhere to ethical principles for trustworthy production applications.
- Production-Ready Context: Best Practices & LLMOps
Master production-ready context management for LLMs. Learn best practices for designing, structuring, and optimizing context within LLMOps workflows to enhance AI reliability and performance.
- Breaking Down Information: Smart Chunking Strategies
Master smart chunking strategies to effectively break down large documents for LLMs, improving context management, relevance, and RAG system performance.
- Essential AI Infrastructure for LLM Serving
Explore the foundational AI infrastructure required for robust, scalable, and cost-efficient LLM serving, covering hardware, software, and architectural patterns.
- Smart Caching Strategies for Cost-Efficient LLM Inference
Explore smart caching strategies like KV cache, prompt cache, and semantic cache to significantly reduce costs and improve performance for LLM inference in production systems.
- Dynamic Model Routing and A/B Testing for LLMs
Master dynamic model routing and A/B testing strategies for LLMs to optimize performance, cost, and user experience in production environments.
- Build & Optimize LLM Inference Pipelines for Production
You will learn to build, optimize, and scale robust LLM inference pipelines, mastering GPU optimization and effective scaling strategies for production.
- Build an End-to-End Production RAG System with LLMOps
Build a robust, scalable, and cost-efficient Retrieval Augmented Generation system using LLMOps best practices for real-world production.
- Optimize GPUs for Faster, More Efficient LLM Inference
Learn to optimize GPU performance for Large Language Models, enabling faster, more efficient, and cost-effective inference using key techniques.
- LLM Inference: Core Mechanics, Optimization, and Caching
Learn LLM inference mechanics, GPU optimization, and caching strategies to deploy robust, scalable, and cost-efficient production systems.
- Understanding the Unique Challenges of LLMOps for LLMs
Understand the distinct challenges and specialized methods required for deploying and managing Large Language Models in production environments.
- Mastering Cost Optimization for LLM Inference
Master techniques to identify LLM inference cost drivers and implement GPU optimization, smart caching, and dynamic scaling for cost-efficient production.
- Scale LLM Deployments from Single Instances to Clusters
Learn to scale Large Language Model deployments from single instances to robust, high-throughput clusters using Kubernetes and auto-scaling.
- Monitoring and Observability for Production LLM Systems
Master LLM monitoring and observability to track performance, manage costs, detect model drift, and ensure your production systems run reliably.
- Implement Security and Governance for LLM Deployments
Implement robust security and governance strategies for LLM deployments, covering data privacy, access control, compliance, and responsible AI principles.
- AI Infrastructure and LLMOps Guide
A guide to AI infrastructure and LLMOps. Learn to deploy and manage AI systems in production, covering model routing, inference, caching, GPU usage, scaling, and monitoring.
- 10 Open-Source AI Alternatives for Solo Developers
Discover 10 open-source AI tools, comparing their features, performance, and use cases to confidently choose alternatives for solo projects.
- 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.
- Data Artifacts & Metadata Management
Learn about managing data artifacts and metadata for reproducible machine learning projects with MetaMLFlow.
- Versioning Datasets with MetaDataFlow
Learn how to version datasets using MetaDataFlow for better reproducibility and auditability in machine learning workflows.
- Monitoring & Observability for Data Pipelines
Learn how to monitor and observe data pipelines for high-quality, reliable data in machine learning projects.
- Project: Developing a Feature Store with MetaDataFlow
Learn how to build a feature store using MetaDataFlow, a powerful open-source library for managing machine learning datasets.
- Project: Deploying a Production-Ready Data Workflow
Learn how to deploy a production-ready data workflow using MetaDataHub, Docker, and Apache Airflow.
- Comparing with Alternatives & Future Trends
Analyze and compare Meta's open-source dataset management library with alternatives, exploring future trends in data management for AI.
- AI/ML Engineering: A Step-by-Step Learning Path
Acquire foundational knowledge and advanced practical skills to build a thriving career in AI/ML engineering, covering core concepts and advanced applications.
- Data Preparation & Feature Engineering for Production
Learn how to prepare data and engineer features for production-ready machine learning models.
- Inference Optimization & Model Deployment
Learn how to optimize and deploy machine learning models for real-world applications, focusing on latency, throughput, cost, edge deployment, and energy efficiency.
- Distributed Training & Scaling Deep Learning
Learn how to scale deep learning models using distributed training with PyTorch.
- Evaluation, Observability & Debugging AI Agents
Learn how to evaluate, observe, and debug AI agents for better performance and reliability.
- Implement ML Experiment Tracking with Trackio and Hugging Face
Learn to effectively track machine learning experiments using Trackio, a lightweight, local-first Python library with seamless Hugging Face integration.
- The World of Experiment Tracking & Trackio Fundamentals
Learn how to track your machine learning experiments with Trackio, a lightweight local-first library.
- Visualizing Experiments with the Local Gradio Dashboard
Learn how to visualize experiments with Trackio's local Gradio dashboard, logging metrics and parameters.
- Advanced Logging: Artifacts, Models, and Custom Data
Learn advanced logging techniques with Trackio, including how to log artifacts like models and datasets for reproducible machine learning experiments.
- Trackio CLI: Manage ML Experiments, Dashboards, and Sync
You will use Trackio's command line interface to efficiently manage machine learning experiments, launch dashboards, and integrate with cloud platforms.
- Database Management, Backups, and Data Integrity
Learn how to manage, backup, and ensure data integrity in your machine learning experiments with Trackio.
- Real-World Scenario: Hyperparameter Tuning with Trackio
Learn how to use Trackio for efficient hyperparameter tuning experiments in machine learning.
- Troubleshooting Common Issues and Debugging Tips
Learn systematic troubleshooting and debugging techniques for Trackio, a tool for machine learning and experiment tracking.
- Best Practices for Production-Ready Experiment Tracking
Learn best practices for production-ready experiment tracking with Trackio and Hugging Face Spaces.