Kubeflow on AWS Training Course

This instructor-led, live training in Malaysia (online or onsite) targets advanced-level AI engineers and data scientists with intermediate-to-advanced experience who wish to enhance DeepSeek model performance, minimize latency, and deploy AI solutions efficiently using modern MLOps practices.

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

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

This instructor-led, live training in Malaysia (onsite or remote) is designed for engineers who wish to deploy and manage IoT devices on AWS.

By the end of this training, participants will be able to build an IoT platform that includes the deployment and management of a backend, gateway, and devices on top of AWS.

This instructor-led live training in Malaysia (online or onsite) is designed for developers who wish to install, configure, and manage AWS IoT Greengrass capabilities to create applications for various devices.

By the end of this training, participants will be able to use AWS IoT Greengrass to build, deploy, manage, secure, and monitor applications on intelligent devices.

This instructor-led, live training in Malaysia (onsite or remote) is designed for developers who wish to use AWS Lambda to build and deploy services and applications to the cloud, without worrying about provisioning the execution environment (servers, VMs and containers, availability, scalability, storage, etc.).

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

• Configure AWS Lambda to execute a function.
• Comprehend FaaS (Functions as a Service) and the benefits of serverless development.
• Build, upload, and execute AWS Lambda functions.
• Integrate Lambda functions with various event sources.
• Package, deploy, monitor, and troubleshoot Lambda-based applications.

This instructor-led, live training in Malaysia (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of DevOps practices and streamline development processes using AWS Cloud9.

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

• Set up and configure AWS Cloud9 for DevOps workflows.
• Implement continuous integration and continuous delivery (CI/CD) pipelines.
• Automate testing, monitoring, and deployment processes using AWS Cloud9.
• Integrate AWS services such as Lambda, EC2, and S3 into DevOps workflows.
• Utilize source control systems like GitHub or GitLab within AWS Cloud9.

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

This instructor-led training session, available online or on-site, targets intermediate to advanced technical professionals seeking to containerize and operationalise complete ML pipelines using Docker.

Upon completing this training, participants will gain the ability to:

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

Course Format

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

Course Customization Options

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

This instructor-led, live training in Malaysia (online or onsite) is aimed at intermediate-level professionals who wish to learn how to effectively build, deploy, and maintain serverless applications on AWS Cloud9 and AWS Lambda.

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

• Understand the fundamentals of serverless architecture.
• Set up AWS Cloud9 for serverless application development.
• Develop, test, and deploy serverless applications using AWS Lambda.
• Integrate AWS Lambda with other AWS services such as API Gateway and S3.
• Optimize serverless applications for performance and cost efficiency.

Summary:

• Understanding the fundamental principles and architecture of IoT
• Exploring the roles of 'Things' and 'Sensors', their connectivity to the internet, and their alignment with business processes
• An overview of key IoT software layers: hardware, firmware, middleware, cloud infrastructure, and mobile applications
• Core IoT capabilities: fleet management, data visualization, SaaS-based fleet and data visualization tools, alerting systems, sensor and device onboarding, and geofencing
• Foundations of IoT device-to-cloud communication using the MQTT protocol
• Integrating IoT devices with AWS via MQTT and AWS IoT Core
• Linking AWS IoT Core with AWS Lambda for data computation and storage within DynamoDB
• Establishing connections between Raspberry Pi devices and AWS IoT Core for efficient data exchange
• Practical application: Constructing a smart device using Raspberry Pi and AWS IoT Core
• Visualizing sensor data and enabling web interface communication

This instructor-led live training in Malaysia (online or onsite) is designed for developers and data scientists keen on building, deploying, and managing machine learning workflows on Kubernetes.

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

• Install and configure Kubeflow on-premise and in the cloud using AWS EKS (Elastic Kubernetes Service).
• Construct, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run entire machine learning pipelines on diverse architectures and cloud environments.
• Utilise Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

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

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

• Install and configure Kubernetes, Kubeflow, and other necessary software on Azure.
• Use Azure Kubernetes Service (AKS) to simplify the process of initializing a Kubernetes cluster on Azure.
• 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 running in parallel.
• Leverage other AWS managed services to extend an ML application.

This instructor-led, live training (available online or onsite) is tailored for data scientists who want to move beyond simply building ML models and instead optimise the processes of ML model creation, tracking, and deployment.

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

• Install and configure MLflow along with related ML libraries and frameworks.
• Understand the importance of the trackability, reproducibility, and deployability of an ML model.
• Deploy ML models to various public clouds, platforms, or on-premise servers.
• Scale the ML deployment process to support multiple users collaborating on a project.
• Set up a central registry to experiment with, reproduce, and deploy ML models.

This instructor-led live training in Malaysia (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 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.

This instructor-led live training (available online or onsite) is designed for machine learning engineers looking to leverage Azure Machine Learning and Azure DevOps to implement MLOps best practices.

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

• Create reproducible workflows and machine learning models.
• Oversee the entire machine learning lifecycle.
• Track and report on model version history, assets, and other details.
• Deploy production-ready machine learning models to any environment.

MLOps on Kubernetes serves as a framework that automates the training, validation, packaging, and deployment of machine learning models through containerized pipelines and GitOps workflows.

This instructor-led live training, available both online and onsite, is designed for intermediate-level practitioners looking to construct automated, scalable MLOps pipelines on Kubernetes.

Upon completing this training, participants will gain the ability to:

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

Course Format

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

Customization Options

• Organizations can request tailored content that aligns with their internal MLOps tools and infrastructure.