MLOps Training in Christchurch

This instructor-led, live training in Christchurch (online or on-site) is designed for advanced-level AI engineers and data scientists with intermediate to advanced experience who aim to enhance DeepSeek model performance, minimise latency, and deploy AI solutions efficiently 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 cloud and on-premises infrastructure.
• Monitor, maintain, and scale AI solutions effectively.

MLOps on Kubernetes is a framework for automating the training, validation, packaging, and deployment of machine learning models using containerised pipelines and GitOps workflows.

This instructor-led, live training (online or on-site) is aimed at intermediate-level practitioners who wish to build automated, scalable MLOps pipelines on Kubernetes.

Upon completing this training, participants will be equipped 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-led presentations and technical deep dives.
• Hands-on exercises that build real-world CI/CD workflows.
• Live-lab practice 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 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.

Course Format

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

Course Customisation Options

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

Docker is a containerisation platform used to build reproducible, portable, and scalable environments for ML systems.

This instructor-led, live training (online or on-site) is designed for intermediate to advanced-level technical professionals who wish to containerise and operationalise complete ML pipelines using Docker.

Upon completion of 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 in containerised environments.

Course Format

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

Course Customisation Options

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

This instructor-led, live training in Christchurch (available online or onsite) is tailored for developers and data scientists who wish 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 both on-premises and in the cloud using AWS EKS (Elastic Kubernetes Service).
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Execute complete machine learning pipelines across diverse architectures and cloud environments.
• Use Kubeflow to spawn and manage Jupyter notebooks.
• Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in Christchurch (online or on-site) is tailored for engineers who wish to deploy Machine Learning workloads to an AWS EC2 server.

By the end of this training, participants will be able to:

• Install and configure Kubernetes, Kubeflow, and other required software on AWS.
• Utilise EKS (Elastic Kubernetes Service) to simplify the process of initialising a Kubernetes cluster on AWS.
• Create and deploy a Kubernetes pipeline to automate and manage ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines operating in parallel.
• Leverage other AWS managed services to extend an ML application.

This instructor-led, live training in Christchurch (online or on-site) is designed for engineers who wish to deploy Machine Learning workloads to the Azure cloud.

By the end of this training, participants will be able to:

• Install and configure Kubernetes, Kubeflow, and other required software on Azure.
• Use Azure Kubernetes Service (AKS) to simplify the process of initialising a Kubernetes cluster on Azure.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
• Leverage other AWS managed services to extend an ML application.

This instructor-led, live training in Christchurch (online or on-site) is aimed at developers and data scientists who wish 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 both on-premises and in the cloud.
• Build, deploy, and manage ML workflows using Docker containers and Kubernetes.
• Execute complete machine learning pipelines across diverse architectures and cloud environments.
• Use Kubeflow to launch and manage Jupyter notebooks.
• Construct ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training (available online or on-site) is aimed at data scientists who wish to move beyond building machine learning models and instead optimise the entire model creation, tracking, and deployment process.

By the end of this training, participants will be able to:

• Install and configure MLflow along with related machine learning libraries and frameworks.
• Appreciate the importance of trackability, reproducibility, and deployability in machine learning models.
• Deploy machine learning models across different public clouds, platforms, or on-premise servers.
• Scale the machine learning deployment process to support multiple users collaborating on a project.
• Establish a central registry to experiment with, reproduce, and deploy machine learning models.

This instructor-led, live training in Christchurch (online or onsite) 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 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.
• Operationalise the entire Machine Learning process so that it's reproducible and maintainable.

This instructor-led, live training (online or onsite) is aimed at machine learning engineers who wish to use Azure Machine Learning and Azure DevOps to facilitate MLOps practices.

By the end of this training, participants will be able to:

• Build reproducible workflows and machine learning models.
• Manage the machine learning lifecycle.
• Track and report model version history, assets, and more.
• Deploy production-ready machine learning models anywhere.