MLOps for Generative AI
Focus on the operational challenges of deploying, monitoring, and maintaining large-scale generative models. Learn about model versioning, performance tracking, cost management, and building robust production pipelines.
59 courses
Connect AI models to real-world data and external tools by building custom servers and agent integrations using the Model Context Protocol.
Learn to transition machine learning models from development to production by building REST APIs, deploying serverless functions, and tracking experiments.
Learn to build and automate production-ready LLM deployment pipelines using Jenkins, Docker, Kubernetes, and cloud-native monitoring tools.
Develop the skills to manage the lifecycle of Generative AI applications, from initial prompt design to production deployment and monitoring.
Learn modern quality engineering principles, integrate automated testing into DevOps pipelines, and leverage generative AI to accelerate your software testing career.
Learn how to deploy, optimize, and scale large language models using MLflow, Ray, and modern quantization techniques to build production-ready AI applications.
Connect AI models to external tools, databases, and APIs by building custom MCP servers and integrations using Python, Claude Desktop, and Cursor.
Learn to build connected AI systems by mastering the Model Context Protocol to bridge language models with external data and real-world tools.
Learn to build test frames, design automated test cases, and write Python assessments to verify Simulink and Targetlink models using Time Partition Testing.
Learn to design, deploy, and monitor robust machine learning models in production, moving from experimental code to scalable, real-world systems.
Build efficient data pipelines and deploy machine learning models to browsers, mobile devices, and cloud servers using TensorFlow.js, TensorFlow Lite, and TensorFlow Serving.
Learn to architect, deploy, and scale robust machine learning pipelines on GCP using modern MLOps practices, distributed training, and efficient inference strategies.
Learn to track experiments, package reproducible code, and deploy models systematically using MLflow to streamline your data science workflow.
Move machine learning models from development to production by mastering TensorFlow Serving, model repositories, and foundational MLOps workflows.
Learn to build, automate, and monitor production-grade machine learning workflows for scalable AI applications.
Learn how to detect model drift, prevent silent failures, and maintain high-performing machine learning systems in production using modern MLOps observability principles.
Learn to track performance, detect data drift, and maintain production models using Python and NannyML.
Learn to deploy, scale, and maintain large language models in production environments through structured written lessons designed for developers and AI enthusiasts.
Build and deploy sophisticated machine learning models using advanced ensemble methods, modern MLOps workflows, and vector databases for real-world applications.
Learn how to apply artificial intelligence and machine learning to automate IT operations, improve observability, and resolve system issues faster.
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