Online MLOps Training in US Empire

This instructor-led, live training in US Empire (online or onsite) is aimed at advanced-level AI engineers and data scientists with intermediate-to-advanced experience who wish to enhance DeepSeek model performance, reduce latency, and deploy AI solutions efficiently using modern MLOps practices for government.

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

• Optimize DeepSeek models for efficiency, accuracy, and scalability in alignment with public sector workflows.
• Implement best practices for MLOps and model versioning to ensure governance and accountability.
• Deploy DeepSeek models on cloud and on-premise infrastructure to support government operations.
• Monitor, maintain, and scale AI solutions effectively to meet the needs of government agencies.

Kubeflow is an open-source platform designed to streamline the building, training, and deployment of 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 machine learning workflows using Kubeflow for government applications.

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

• Navigate the Kubeflow ecosystem and its 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 machine learning workflows for government use cases.

Course Customization Options

• Customized versions of this training can be arranged to align with your team’s technology stack and project requirements, ensuring they meet the specific needs for government operations.

This instructor-led, live training in US Empire (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes for government.

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

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

This instructor-led, live training in US Empire (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an AWS EC2 server for government use.

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

• Install and configure Kubernetes, Kubeflow, and other necessary software on AWS for government applications.
• Use EKS (Elastic Kubernetes Service) to streamline the initialization of a Kubernetes cluster on AWS for government operations.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production environments for government.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel for government projects.
• Leverage other AWS managed services to enhance an ML application for government use cases.

This instructor-led, live training in US Empire (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to the Azure cloud for government use.

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

• Install and configure Kubernetes, Kubeflow, and other necessary software on Azure.
• Utilize Azure Kubernetes Service (AKS) to streamline the process of initializing a Kubernetes cluster on Azure for government operations.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production environments for government applications.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel, enhancing efficiency for government projects.
• Leverage other AWS managed services to extend an ML application's capabilities for government use cases.

This instructor-led, live training in US Empire (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes for government.

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

• Install and configure Kubeflow on-premises 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 for government use.
• Use Kubeflow to spawn and manage Jupyter notebooks for government projects.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms for government applications.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to go beyond building machine learning (ML) models and optimize the entire ML model creation, tracking, and deployment process for government use.

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

• Install and configure MLflow and related ML libraries and frameworks for government.
• Understand the importance of trackability, reproducibility, and deployability of an ML model in a public sector context.
• Deploy ML models to different public clouds, platforms, or on-premise servers suitable for government operations.
• Scale the ML deployment process to accommodate multiple users collaborating on a project within a government environment.
• Set up a central registry to experiment with, reproduce, and deploy ML models in alignment with government workflows and governance requirements.

This instructor-led, live training in US Empire (online or onsite) is designed for engineers who wish to evaluate the current approaches and tools available to make an informed decision on adopting MLOps within their organization.

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

• Install and configure various MLOps frameworks and tools for government use.
• Assemble a team with the appropriate skills for constructing and supporting an MLOps system.
• Prepare, validate, and version data for use by machine learning models.
• Understand the components of an ML Pipeline and the tools required to build one.
• Experiment with different machine learning frameworks and servers for deployment in a production environment.
• Operationalize the entire Machine Learning process to ensure it is reproducible and maintainable.

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

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

• Construct reproducible workflows and machine learning models.
• Manage the entire machine learning lifecycle effectively.
• Track and report on model version history, assets, and other relevant data.
• Deploy production-ready machine learning models in any environment.