MLOps Training in Durban

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.

MLOps on Kubernetes provides a framework for automating the training, validation, packaging, and deployment of machine learning models via containerized pipelines and GitOps workflows.

This instructor-led, live training (available online or onsite) is designed for intermediate-level practitioners seeking to build automated, scalable MLOps pipelines on Kubernetes.

Upon completion of this training, participants will be able to:

• Design end-to-end CI/CD pipelines for machine learning.
• Implement GitOps workflows for model deployment and versioning.
• Automate the training, testing, and packaging of ML models.
• Integrate monitoring, alerting, and rollback strategies.

Course Format

• Instructor-guided presentations and technical deep dives.
• Hands-on exercises focused on building real-world CI/CD workflows.
• Live-lab practice for deploying ML workloads to Kubernetes.

Course Customisation Options

• Organisations may request tailored content aligned with their internal MLOps tools and infrastructure.

Kubeflow is an open-source platform engineered to simplify the creation, training, and deployment of machine learning workloads on Kubernetes.

This instructor-led, live training (available online or onsite) targets beginner to intermediate-level professionals aiming to build robust ML workflows utilizing Kubeflow.

Upon completing this training, participants will acquire the skills to:

• Navigate the Kubeflow ecosystem and its core components.
• Construct reproducible workflows using Kubeflow Pipelines.
• Execute scalable training jobs on Kubernetes.
• Deploy machine learning models efficiently via Kubeflow Serving.

Course Format

• Guided presentations alongside collaborative discussions.
• Hands-on labs featuring real Kubeflow components.
• Practical exercises to develop end-to-end ML workflows.

Course Customization Options

• Bespoke versions of this training can be arranged to suit your team’s technology stack and project requirements.

Docker serves as a containerization platform designed to create reproducible, portable, and scalable environments for machine learning systems.

This instructor-led live training (available online or onsite) targets intermediate to advanced technical professionals aiming to containerize and operationalise complete ML pipelines using Docker.

Upon completing this training, participants will be able to:

• Containerise ML training, validation, and inference workloads.
• Design and orchestrate end-to-end ML pipelines using Docker and supporting tools.
• Implement versioning, reproducibility, and CI/CD for ML components.
• Deploy, monitor, and scale ML services within containerised environments.

Course Format

• Interactive lectures complemented by practical demonstrations.
• Hands-on exercises focused on constructing real ML pipeline components.
• Live-lab implementation for end-to-end containerised workflows.

Course Customisation Options

• For customised training tailored to specific ML infrastructure requirements, please contact us to discuss options.

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.