Ultimate DevOps to MLOps Bootcamp - Build ML CI/CD Pipelines

From Data to Deployment — Learn MLOps by Building a Real-World Machine Learning Project with MLflow, Docker, Kubernetes
⏱️ Length: 11.6 total hours
⭐ 4.58/5 rating
👥 13,890 students
🔄 August 2025 update

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Course Overview
- This bootcamp delivers a transformative learning experience, guiding participants from foundational DevOps principles to the specialized realm of MLOps. It’s meticulously designed to empower aspiring MLOps engineers, data scientists, and machine learning practitioners to construct robust, automated, and scalable machine learning CI/CD pipelines from inception to production. The course deconstructs the complexities of operationalizing machine learning models, emphasizing reliability, efficiency, and maintainability in real-world scenarios.
- Embark on a practical journey that integrates software engineering best practices with the unique demands of machine learning projects. You will gain a holistic understanding of how to bridge the gap between model development in an experimental environment and its deployment as a continuously integrated, production-ready service. The curriculum focuses on building an end-to-end system for a real-world machine learning problem, ensuring every concept is immediately applicable and reinforced through hands-on implementation.
- Move beyond theoretical model building to mastering the entire lifecycle of an ML product. This includes not only the technical intricacies of pipeline automation but also the strategic considerations for managing ML experiments, ensuring model governance, and facilitating seamless model updates. The course underscores the importance of reproducible research and deterministic deployments, preparing you to contribute significantly to the development of production-grade AI solutions.
Requirements / Prerequisites
- A solid understanding of fundamental Python programming concepts, including data structures, functions, and object-oriented programming basics, is essential for engaging with the practical coding exercises.
- Familiarity with core machine learning concepts, such as supervised learning algorithms, model training, evaluation metrics, and basic data preprocessing techniques, will provide a strong foundation for the ML-specific aspects of the course.
- Basic command-line proficiency and an understanding of version control systems, particularly Git, are expected to navigate project setup and collaborate effectively.
- Prior exposure to or a keen interest in cloud computing paradigms and containerization concepts will be beneficial, though not strictly required, as the course will introduce these concepts in a practical context.
- An eagerness to learn about infrastructure automation and the operational aspects of software development will ensure you get the most out of the DevOps transition.
Skills Covered / Tools Used
- Production-Grade ML System Design: Develop the architectural foresight to design scalable, fault-tolerant, and maintainable machine learning systems suitable for production environments. This includes understanding component interplay and service dependencies.
- Containerization Strategies: Master the art of packaging ML models and their dependencies into lightweight, portable, and isolated containers, ensuring consistent execution across different environments.
- Orchestration Techniques for Distributed Systems: Learn to manage and scale containerized ML applications across a cluster of machines, optimizing resource utilization and ensuring high availability for inference services.
- Automated Testing for ML Workflows: Implement strategies for continuous integration that include not just code testing, but also data validation, model integrity checks, and performance regression testing within the ML pipeline.
- Robust Deployment Strategies: Gain proficiency in deploying machine learning models safely and efficiently, incorporating blue/green or canary deployment patterns to minimize downtime and mitigate risks during updates.
- Continuous Delivery for ML Applications: Establish automated release cycles for ML models, enabling frequent and reliable updates to production without manual intervention, fostering agility in model improvement.
- Infrastructure as Code (IaC) Principles: Understand how to define and manage your MLOps infrastructure through code, ensuring reproducibility, versioning, and collaborative development of deployment environments.
- Model Lifecycle Management: Acquire skills in tracking experiments, versioning models, managing model registries, and facilitating seamless transitions of models from development to staging to production.
- Data and Model Observability Foundations: Lay the groundwork for monitoring production ML models for performance degradation, data drift, and concept drift, enabling proactive maintenance and re-training strategies.
- Key Tools Ecosystem: Engage hands-on with a cutting-edge MLOps toolchain comprising systems for experiment tracking and model registry, containerization and orchestration, CI/CD automation, API development, and lightweight UI creation.
Benefits / Outcomes
- Transform Theoretical Knowledge into Practical Expertise: Confidently transition from conceptual understanding of machine learning to the practical implementation of production-ready ML solutions.
- Build Scalable and Reliable ML Products: Acquire the skills to engineer robust, automated, and scalable machine learning applications that can handle real-world data volumes and user traffic.
- Master End-to-End MLOps Workflows: Develop comprehensive expertise in orchestrating the entire ML model lifecycle, from data ingestion and model training to deployment, monitoring, and continuous iteration.
- Enhance Career Prospects: Position yourself as a highly sought-after professional in the burgeoning MLOps domain, opening doors to roles such as MLOps Engineer, ML Platform Engineer, or Senior Data Scientist with deployment expertise.
- Create a Portfolio-Ready Project: Complete a significant, real-world machine learning CI/CD project that serves as tangible proof of your MLOps capabilities, showcasing your ability to deliver production-grade ML.
- Bridge the Data Science-Software Engineering Divide: Gain a unique perspective at the intersection of data science and software engineering, enabling more effective collaboration and communication within cross-functional teams.
- Implement Best Practices for ML Governance: Understand and apply industry best practices for model versioning, experiment tracking, and pipeline automation, leading to more transparent and auditable ML systems.
- Develop a Holistic View of ML Product Lifecycle: Understand not just the mechanics, but also the strategic implications of each stage in the ML product journey, from ideation to sustained operation.
PROS
- Highly Practical and Project-Driven: The emphasis on building a real-world project ensures immediate application of learned concepts and delivers a tangible, portfolio-ready outcome.
- Comprehensive Toolchain Coverage: Provides exposure to a diverse and industry-relevant set of MLOps tools, equipping learners with a versatile skillset.
- Up-to-Date and Relevant Content: The August 2025 update signifies a commitment to keeping the curriculum current with the latest industry trends and tool versions.
- Proven Track Record and Community: A high rating and large student base indicate a well-received course with a potentially active learning community.
- Addresses Critical Industry Skill Gap: Directly tackles the growing demand for professionals capable of operationalizing machine learning models efficiently and reliably.
CONS
- May require significant time commitment and self-discipline to master the diverse toolset and complex concepts covered, especially for those new to infrastructure or advanced DevOps practices.