Whether delivered online or in person, these instructor-led live MLOps training courses guide you through interactive, hands-on exercises to master the use of MLOps tools for automating and optimizing the deployment and maintenance of ML systems within production environments.
You can select MLOps training in the form of "online live training" or "onsite live training". Online live training, also known as "remote live training", is conducted via an interactive remote desktop session. Onsite live training can be facilitated locally at customer premises in Durban or at NobleProg corporate training centres located in Durban.
NobleProg -- Your Local Training Provider
Garden Court South Beach
Durban, South Africa
Way to get there
From Durban city centre: follow the beachfront/Marine Parade south toward South Beach.
By car/taxi: give the driver the address 73 O.R. Tambo Parade, South Beach, Durban 4001.
From King Shaka International Airport: the hotel is roughly 30 km ;from the airport.
This instructor-led, live training in Durban (online or on-site) is designed for advanced AI engineers and data scientists with intermediate-to-advanced experience who wish to enhance DeepSeek model performance, reduce latency, and efficiently deploy AI solutions using modern MLOps practices.
By the end of this training, participants will be able to:
Optimise DeepSeek models for efficiency, accuracy, and scalability.
Implement best practices for MLOps and model versioning.
Deploy DeepSeek models on both cloud and on-premise infrastructure.
Monitor, maintain, and scale AI solutions effectively.
This live training in Durban empowers intermediate practitioners to construct automated MLOps pipelines on Kubernetes. Participants will learn to design CI/CD workflows, implement GitOps strategies, and deploy ML models using containerized infrastructure to achieve scalable and reproducible machine learning operations.
This hands-on training in Durban provides you with the necessary skills to design, train, and serve machine learning models on Kubernetes using Kubeflow. You will learn to navigate the ecosystem, author scalable pipelines, and manage production-ready workloads by adhering to best practices.
This live, instructor-led course in Durban walks professionals through the process of containerizing complete ML pipelines with Docker. Learners will gain proficiency in creating reproducible environments, managing training and inference workloads, and establishing CI/CD frameworks for scalable MLOps deployments.
This instructor-led, live training in Durban (available online or on-site) is designed for developers and data scientists looking to build, deploy, and manage machine learning workflows on Kubernetes.
By the end of this training, participants will be able to:
Install and configure Kubeflow on premise and in the cloud.
Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
Run entire machine learning pipelines on diverse architectures and cloud environments.
Using Kubeflow to spawn and manage Jupyter notebooks.
Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
This instructor-led live training in Durban (online or at your premises) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization.
By the end of this training, participants will be able to:
Install and configure various MLOps frameworks and tools.
Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
Prepare, validate and version data for use by ML models.
Understand the components of an ML Pipeline and the tools needed to build one.
Experiment with different machine learning frameworks and servers for deploying to production.
Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.
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