Online MLOps Training in US

This instructor-led, live training (online or onsite) is designed for advanced-level AI engineers and data scientists with intermediate to advanced experience. The goal is to enhance DeepSeek model performance, minimize 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. - Implement best practices for MLOps and model versioning. - Deploy DeepSeek models on cloud and on-premise infrastructure. - Monitor, maintain, and scale AI solutions effectively.

MLOps on Kubernetes is a framework designed to automate the training, validation, packaging, and deployment of machine learning models using containerized pipelines and GitOps workflows. This instructor-led, live training (available online or onsite) is targeted at intermediate-level practitioners who wish to develop automated, scalable MLOps pipelines on Kubernetes for government use. Upon completion of 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. **Format of the Course** - 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 for government operations.

Kubeflow is an open-source platform designed to streamline the development, training, and deployment of machine learning workloads on Kubernetes. This instructor-led, live training (online or onsite) is aimed at professionals at the beginner to intermediate levels who wish to build reliable ML 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 ML workflows. **Course Customization Options** - Customized versions of this training can be arranged to align with your team’s technology stack and project requirements for government use.

Docker is a containerization platform designed to create reproducible, portable, and scalable environments for machine learning (ML) systems. This instructor-led, live training (available online or onsite) is targeted at intermediate to advanced technical professionals who aim to containerize and operationalize comprehensive ML pipelines using Docker. Upon completion of this training, participants will be able to: - Containerize ML training, validation, and inference workloads. - Design and orchestrate end-to-end ML pipelines using Docker and complementary tools. - Implement versioning, reproducibility, and continuous integration/continuous deployment (CI/CD) for ML components. - Deploy, monitor, and scale ML services in containerized environments. **Format of the Course** - Interactive lectures supported by practical demonstrations. - Hands-on exercises focused on constructing real ML pipeline components. - Live-lab implementation for end-to-end containerized workflows. **Course Customization Options** - For customized training aligned with specific ML infrastructure needs, please contact us to discuss options tailored for government and other public sector entities.

This instructor-led, live training in US (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 both on-premise and in the cloud using AWS EKS (Elastic Kubernetes Service). - Build, deploy, and manage ML workflows based on Docker containers and Kubernetes. - Run comprehensive machine learning pipelines across various architectures and cloud environments. - Utilize Kubeflow to spawn and manage Jupyter notebooks. - Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in US (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an AWS EC2 server for government. By the end of this training, participants will be able to: - Install and configure Kubernetes, Kubeflow, and other necessary software on AWS. - Utilize EKS (Elastic Kubernetes Service) to simplify the initialization of a Kubernetes cluster on AWS. - Develop and deploy a Kubernetes pipeline for automating and managing ML models in production environments. - Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel. - Leverage other AWS managed services to enhance an ML application.

This instructor-led, live training in [location] (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to the Azure cloud for government. By the end 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 streamline the initialization of a Kubernetes cluster on Azure. - Create and deploy a Kubernetes pipeline for automating and managing ML models in production environments. - Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel. - Leverage other Azure managed services to extend an ML application, ensuring alignment with public sector workflows and governance.

This instructor-led, live training in [location] (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes for government use. By the end of this training, participants will be able to: - Install and configure Kubeflow both on-premise and in the cloud. - Build, deploy, and manage ML workflows using Docker containers and Kubernetes. - Run comprehensive machine learning pipelines across various architectures and cloud environments. - Utilize Kubeflow to create and manage Jupyter notebooks. - Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training (available online or onsite) is designed for data scientists who aim to enhance their machine learning (ML) model creation, tracking, and deployment processes beyond the initial development phase. By the end of this training, participants will be able to: - Install and configure MLflow along with related ML libraries and frameworks. - Understand the significance of trackability, reproducibility, and deployability in the context of an ML model for government applications. - 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 efficiently.

This instructor-led, live training in US (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today 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 necessary 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 production environments.
• Operationalize the entire machine learning process to ensure it is reproducible and maintainable.

This instructor-led, live training (available online or onsite) is designed for government machine learning engineers who wish to utilize Azure Machine Learning and Azure DevOps to implement MLOps practices. By the end of this training, participants will be able to: - Construct reproducible workflows and machine learning models. - Manage the entire machine learning lifecycle. - Track and report on model version history, assets, and other relevant data. - Deploy production-ready machine learning models in any environment for government use.