Online MLOps Training in Botswana

This instructor-led, live training in Botswana (online or onsite) targets advanced AI engineers and data scientists with intermediate to advanced proficiency who aim to improve DeepSeek model performance, reduce latency, and implement AI solutions efficiently through contemporary MLOps practices.

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

• Optimize DeepSeek models for enhanced efficiency, accuracy, and scalability.
• Apply best practices for MLOps and model versioning.
• Deploy DeepSeek models across cloud and on-premise infrastructures.
• Monitor, maintain, and scale AI solutions effectively.

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

This instructor-led live training (available online or onsite) is designed for intermediate-level practitioners who aim 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 that build real-world CI/CD workflows.
• Live-lab practice deploying ML workloads to Kubernetes.

Course Customization Options

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

Kubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.

Upon completion of this training, attendees will gain the skills to:

• Navigate the Kubeflow ecosystem and core components.
• Build reproducible workflows with Kubeflow Pipelines.
• Run scalable training jobs on Kubernetes.
• Serve machine learning models efficiently using Kubeflow Serving.

Format of the Course

• Guided presentations and collaborative discussions.
• Hands-on labs with real Kubeflow components.
• Practical exercises to build end-to-end ML workflows.

Course Customization Options

• Customized versions of this training can be arranged to align with your team’s technology stack and project requirements.

Docker serves as a containerisation 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 seeking to containerise and operationalise complete machine learning pipelines using Docker.

Upon completing this training, participants will be equipped to:

• Containerise machine learning training, validation, and inference workloads.
• Design and orchestrate end-to-end machine learning pipelines using Docker and complementary tools.
• Implement versioning, reproducibility, and continuous integration and delivery (CI/CD) for machine learning components.
• Deploy, monitor, and scale machine learning services within containerised environments.

Format of the Course

• Interactive lectures supported by practical demonstrations.
• Hands-on exercises focused on building real machine learning pipeline components.
• Live-lab implementation for end-to-end containerised workflows.

Course Customisation Options

• For customised training aligned with specific machine learning infrastructure needs, please contact us to discuss options.

This instructor-led live training in Botswana (online or onsite) is designed for engineers who wish to evaluate the approaches and tools currently available, helping them make informed decisions on the path forward for adopting MLOps within their organisation.

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.