"This MLOps course was a game-changer. I learned not just how to build models, but how to deploy and manage them reliably. The hands-on exercises with Docker, CI/CD, and monitoring gave me skills I could apply immediately at work. Highly practical and engaging."
This course provides a comprehensive, practical introduction to MLOps and AI deployment, teaching learners how to manage the full lifecycle of machine learning models in production. Participants gain hands-on experience in model versioning, CI/CD pipelines, monitoring, scaling, and cloud deployment. The course emphasises real-world workflows, ensuring that learners can develop, deploy, and maintain robust AI systems that perform reliably in business and industrial environments. By the end, learners will understand best practices for operationalising AI, improving collaboration between data scientists and engineers, and delivering production-ready machine learning solutions.
Module 1: MLOps Fundamentals & Workflow
Learners begin by exploring the principles of MLOps, including the AI/ML lifecycle, version control, experiment tracking, and reproducible workflows. Hands-on exercises demonstrate how to structure projects for scalability and maintainability, bridging the gap between model development and production deployment.
Module 2: Deployment & Continuous Integration
This module covers practical deployment techniques, including creating APIs for ML models, containerisation with Docker, orchestration with Kubernetes, and CI/CD pipelines. Learners gain experience automating model deployment, testing, and updating, ensuring AI systems are robust, repeatable, and easy to manage.
Module 3: Monitoring, Scaling & Production Best Practices
Learners focus on monitoring model performance, detecting drift, handling data updates, and scaling AI systems efficiently. The module also addresses operational challenges such as latency, reliability, and fault tolerance. By applying best practices, participants learn how to keep production AI systems performant and maintainable over time.
This course is ideal for data scientists, AI engineers, software developers, and technical professionals who want to take their machine learning models into production. It is also suitable for teams responsible for operationalising AI workflows or maintaining enterprise-grade AI solutions.
We guide organisations in adopting AI to automate tasks, streamline processes, and free up valuable time so your team can focus on what really matters.
Our hands-on training helps staff build confidence in using AI tools, from automation to data analysis, improving both efficiency and decision-making.
We design and support AI systems tailored to your organisation, ensuring smooth integration with existing workflows and long-term efficiency gains.
Attending this course equips learners with the practical skills to deploy, monitor, and maintain machine learning systems effectively. The hands-on approach ensures participants can operationalise AI workflows, collaborate with development teams, and deliver production-ready solutions that meet industry standards.
Our inhouse programmes are easy to arrange and can be tailored to meet more specific learning needs. Training can be run from your own premises, or we will help you select a suitable venue offsite. Our existing IT courses can be customised to meet your organisation requirements. With all our courses there are no hidden cost, the price you see is the price you pay.
With Infinity AI's inhouse training, your team can gain the essential IT skills they need without the inconvenience of off-site sessions. Our expert-led courses are designed to fit your business, maximising participation and providing customised solutions that boost productivity and performance. Let us bring the training to you, ensuring your entire team is equipped for success.
Inhouse Training
Infinity AI Learning delivers expert-led, industry-focused training that combines theory with real-world application. Our MLOps course emphasises hands-on learning, professional workflows, and mentorship, giving learners the confidence to deploy and manage AI systems in production environments across diverse sectors.
