MLOps for Production AI
Build, deploy, and manage scalable AI systems in real world production environments. This hands-on course equips engineers and data professionals with the practical skills needed to take machine learning models from experimentation to reliable, production-ready systems, with a strong focus on MLOps for production AI systems
⏱ Duration & Time: 40 hrs (Mo - Fr, 9:30AM - 5:30PM)
📅 Next Course Date: 14th September
👥 Participants: Approx. 15
🌐 Location: Remote (live online sessions with instructors)
🗣 Course Language: English
💳 Course Fee: €1,500
🎓 Completion: Certificate of Completion (DeepStackAI)
💼 Future Job Opportunities: MLOps Engineer, Machine Learning Engineer, AI Platform Engineer, Data Engineer (AI focused), DevOps Engineer (AI/ML)
Expected Salary: €80,000 – €110,000 per year
Course Overview
Building a machine learning model is only the first step deploying and maintaining it in production is where real impact happens. In this course, you’ll learn how to design, deploy, monitor, and scale AI systems using modern MLOps practices.
You will gain hands on experience with real world workflows, including model versioning, CI/CD pipelines, data management, and production monitoring. By the end of the course, you’ll understand how to build robust, scalable, and maintainable AI systems used in industry.
Who This Course Is For?
This course is designed for software engineers who want to work with production AI systems, as well as data scientists looking to deploy their models in real world environments. It is also ideal for machine learning engineers who want to strengthen their MLOps and deployment skills. Anyone interested in building scalable, production ready AI systems will benefit from this course.
What You’ll Learn?
In this course, you will gain a strong understanding of the complete machine learning lifecycle, from data preparation to deployment and monitoring. You will learn how to build automated pipelines for training and deployment, ensuring reproducibility and scalability. The course covers model versioning and experiment tracking, helping you manage and compare different models effectively. You will also explore CI/CD practices for machine learning, enabling faster and more reliable deployments.
Additionally, you will learn how to deploy models using APIs and containerization, monitor model performance in production, and handle challenges such as data drift and model degradation. The course also introduces best practices for maintaining and scaling AI systems in real world environments.
A quick overview of all content
Starting dates: MLOps for Production AI
14th Sept - 18th Sept
30th Nov - 4th Dec
Address
DeepStack AI Berlin, Germany
Contacts
info@deepstackai.de
Company
