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DevOps to MLOps Bootcamp– Build & Deploy ML Systems

A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.

Master the entire lifecycle of building and deploying machine learning systems in production with this hands-on DevOps to MLOps Bootcamp. You'll learn how MLOps optimizes model development, deployment, and monitoring, gaining skills in tools like Docker, Kubernetes, MLflow, FastAPI, Streamlit, Prometheus, and GitHub Actions. This course bridges the gap between data science and scalable ML infrastructure.

A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.

You'll explore MLOps concepts, trace its evolution through LLMOps and AgenticAIOps, and study real-world case studies. Apply these principles through a regression-based house price prediction project. The course covers CI pipelines with GitHub Actions and advanced production systems with Kubernetes, KEDA, and ArgoCD. The final sections focus on monitoring, autoscaling, and implementing GitOps pipelines for ML/LLM app deployment.

Ideal for data scientists, ML engineers, DevOps pros, and developers, the course requires basic Python, ML knowledge, and container familiarity. By the end, you'll deploy models with containerized APIs and manage scalable systems.

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What's inside

Syllabus

Introduction to MLOps

In this module, you will be introduced to MLOps, its core principles, and its importance in modern machine learning workflows. The evolution from traditional MLOps to emerging paradigms like LLMOps and AgenticAIOps will be covered. You'll also compare DevOps and MLOps, examining their similarities and differences, and explore the growing role of the MLOps Engineer.

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Career center

Learners who complete DevOps to MLOps Bootcamp– Build & Deploy ML Systems will develop knowledge and skills that may be useful to these careers:

MLOps Engineer

An MLOps Engineer bridges machine learning model development and robust production deployment. This bootcamp provides comprehensive, hands-on experience across the entire ML system lifecycle, making it ideal for this specialized role. Learners master optimizing model deployment and monitoring. The course covers setting up MLOps CI workflows with GitHub Actions and building scalable production inference infrastructure using Kubernetes. You will also implement monitoring and autoscaling with Prometheus and GitOps-based deployments for ML/LLM applications via ArgoCD. This curriculum ensures you can manage complex, resilient ML systems efficiently, making it indispensable for any MLOps Engineer.

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Machine Learning Engineer

A Machine Learning Engineer is responsible for building, deploying, and maintaining machine learning systems. This course equips you with the crucial production-readiness skills that go beyond model development. It directly aids in transitioning models from experimentation to scalable production environments. You will learn to package models with FastAPI and Streamlit, containerize applications with Docker, and build scalable inference infrastructure using Kubernetes. The course's focus on CI pipelines with GitHub Actions and monitoring with Prometheus helps bridge the gap between data science and robust ML infrastructure, making you a more versatile and deployable Machine Learning Engineer.

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AI Platform Engineer

An AI Platform Engineer designs, builds, and maintains the underlying infrastructure and tools enabling AI/ML development and deployment at scale. This course directly addresses core competencies for this role. It covers building scalable production inference infrastructure with Kubernetes, configuring pods and services, and generating Kubernetes YAML manifests. Learners will implement system monitoring with Prometheus and Grafana, understand autoscaling with KEDA, and apply GitOps principles through ArgoCD for continuous delivery of ML/LLM applications. This comprehensive approach to building and managing robust ML infrastructure makes the course highly relevant for aspiring AI Platform Engineers.

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Site Reliability Engineer Machine Learning

A Site Reliability Engineer Machine Learning ensures the stability, performance, and uptime of critical ML applications in production. This course provides essential skills for maintaining highly reliable machine learning systems. It focuses heavily on monitoring with Prometheus and Grafana, enabling you to visualize performance metrics. You will learn to automate scaling using KEDA, conduct load testing to evaluate system capacity, and understand robust deployment strategies with Kubernetes and GitOps via ArgoCD. This comprehensive coverage of resilience, performance, and operational excellence directly prepares you for the demanding responsibilities of a Site Reliability Engineer Machine Learning, crucial for production success.

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Cloud Infrastructure Engineer

A Cloud Infrastructure Engineer designs and manages the cloud environments that host applications and services. This course strongly enhances skills for those specializing in machine learning infrastructure on the cloud. It covers containerization with Docker and Docker Compose, orchestration with Kubernetes for deploying scalable ML models, and implementing monitoring with Prometheus. The focus on GitOps principles and tools like ArgoCD for continuous delivery of ML/LLM applications provides expertise in automated, declarative infrastructure management. This knowledge is vital for building and maintaining robust, production-grade cloud infrastructure tailored for machine learning workloads.

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DevOps Engineer

A DevOps Engineer focuses on automating and streamlining the software development lifecycle, emphasizing continuous integration and delivery. This course enhances traditional DevOps skills by applying them specifically to the unique challenges of machine learning systems. You will gain expertise in tools like Docker and Kubernetes for containerization and orchestration. The course deeply covers MLOps CI workflows with GitHub Actions and GitOps-based deployments for ML/LLM apps using ArgoCD. This specialized knowledge allows a DevOps Engineer to extend their capabilities to include scalable ML infrastructure, ensuring efficient and reliable deployment for AI-driven applications.

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Release Engineer

A Release Engineer focuses on automating and streamlining the build, test, and deployment phases of software releases. This course directly enhances skills for managing the release of machine learning applications. It deeply covers MLOps CI workflows with GitHub Actions for automated training and testing. Learners will gain expertise in building scalable production inference infrastructure with Kubernetes and implementing GitOps-based deployments for ML/LLM applications using ArgoCD. This comprehensive understanding of continuous delivery pipelines ensures seamless, automated, and reliable deployment processes for complex ML systems, making you an invaluable asset in any ML-driven release team.

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Solutions Architect

A Solutions Architect designs large-scale technical solutions for complex business problems. This course is highly relevant for architects specializing in machine learning, providing concrete skills in designing scalable and reliable ML systems. It covers model packaging and containerization, continuous integration pipelines with GitHub Actions, and robust production infrastructure using Kubernetes. Additionally, understanding monitoring, autoscaling, and GitOps-based deployments for ML/LLM applications enables you to architect comprehensive, end-to-end MLOps solutions. An advanced degree is often helpful for this role, complementing the practical skills gained here.

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Machine Learning Operations Manager

A Machine Learning Operations Manager oversees the strategic planning, deployment, and lifecycle management of ML models within an organization. While a management role, this course provides the essential technical depth needed to lead MLOps teams effectively. It covers the entire lifecycle from model development ("From Raw Data to Models") to monitoring and autoscaling in production ("Monitoring and Autoscaling an ML Model"). Understanding CI/CD with GitHub Actions and GitOps with ArgoCD equips managers to make informed decisions and guide implementation challenges. An advanced degree is often helpful for this role, complementing this practical foundation.

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Data Scientist

A Data Scientist develops and refines machine learning models, often focusing on analytical insights and predictive power. This course helps data scientists bridge the critical gap to production, ensuring their models are not just effective but also deployable and scalable. While "From Raw Data to Models" covers model experimentation, the course extends to packaging models with FastAPI wrappers and Streamlit user interfaces, then containerizing them with Docker. Understanding MLOps CI workflows with GitHub Actions and scalable deployment with Kubernetes empowers a Data Scientist to significantly contribute to the entire ML lifecycle, from concept to robust production.

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Data Engineer

A Data Engineer is responsible for building and maintaining robust data pipelines that collect, process, and transform data. This course may be helpful by providing a deeper understanding of how machine learning models consume and process data within production environments. The "From Raw Data to Models" module covers essential data engineering and feature engineering techniques. Furthermore, understanding the subsequent steps of packaging, containerizing with Docker, and deploying ML models with Kubernetes and GitOps provides crucial context for delivering clean, production-ready data to scalable ML systems, enhancing a Data Engineer's ability to support ML initiatives.

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Technical Consultant

A Technical Consultant advises organizations on technology strategy and implementation, guiding them through complex transformations. This course may be helpful by providing a comprehensive understanding of MLOps, LLMOps, and AgenticAIOps, along with practical skills in tools like Docker, Kubernetes, MLflow, and GitHub Actions. This knowledge enables consultants to competently guide clients in building, deploying, and managing scalable machine learning systems. The ability to analyze real-world case studies and apply MLOps principles directly impacts a consultant's effectiveness in advising on modern AI/ML initiatives.

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Backend Developer

A Backend Developer creates the server-side logic and APIs that power applications. This course may be useful by providing specialized skills for integrating and deploying machine learning models into backend services. Learners will gain practical experience in packaging models with FastAPI wrappers and creating user interfaces with Streamlit, then containerizing these applications with Docker. This knowledge enables you to develop efficient, scalable backend services that seamlessly incorporate advanced machine learning capabilities, bridging traditional backend development with modern AI integration. The ability to deploy models with containerized APIs is a particularly strong asset.

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Computer Vision Engineer

A Computer Vision Engineer develops and deploys models for analyzing and interpreting visual data. This course may be useful by equipping these engineers with essential skills for bringing their computer vision models from development to production. The course's focus on containerizing applications with Docker, building scalable inference infrastructure with Kubernetes, and implementing continuous deployment workflows applies directly to robustly deploying complex vision systems. Understanding monitoring and autoscaling, especially for resource-intensive computer vision models, makes this bootcamp highly relevant for ensuring the reliability and performance of cutting-edge visual AI solutions.

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Artificial Intelligence Researcher

An Artificial Intelligence Researcher focuses on developing novel algorithms and models, pushing the boundaries of AI capabilities. This course may be useful by providing researchers with critical insights into the practical aspects of deploying their cutting-edge models into production. Understanding MLOps principles, packaging models with FastAPI, and setting up CI workflows can significantly bridge the gap between research prototypes and real-world applications. It helps researchers design models with deployability in mind, recognizing the challenges of scalable and maintainable AI systems. An advanced degree is typically required for this role.

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Introducing MLOps