Online or onsite, instructor-led live MLOps training courses demonstrate through interactive hands-on practice how to use MLOps tools to automate and optimize the deployment and maintenance of ML systems in production for government.
MLOps training is available as "online live training" or "onsite live training." Online live training (also known as "remote live training") is conducted via an interactive, remote desktop. Onsite live training can be conducted locally on customer premises in Michigan or in Govtra corporate training centers in Michigan.
Govtra -- Your Local Training Provider for government
Detroit, MI - Renaissance Center
400 Renaissance Center, Detroit, United States, 48243
The GM Renaissance Center is conveniently located in downtown Detroit and easily accessed by car via Interstates 75 or 94, with secure underground parking available on site. Travelers flying into Detroit Metropolitan Airport (DTW) can expect a 25–30 minute trip by taxi or rideshare via I‑94. Public transit is efficient: the Detroit People Mover stops directly at the Renaissance Center station, and DDOT routes 3 and 9 serve nearby Jefferson Avenue. Pedestrian skywalks provide safe indoor access from downtown hotels, parking garages, and the riverwalk.
Ann Arbor, MI – Regus - South State Commons I
2723 S State St, Ann Arbor, United States, 48104
Regus South State Commons I is conveniently located off I‑94 via Exit 177 (State Street), with easy access to downtown Ann Arbor and surrounding suburbs. The building offers free on-site surface parking for guests. From Detroit Metropolitan Airport (DTW), the venue can be reached in approximately 20–25 minutes by taxi or rideshare via I‑94 West. Local public transit service (TheRide) operates Route 24 along South State Street, with a stop within a short 2-minute walk of the building.
Grand Rapids, MI - Regus – Calder Plaza
250 Monroe Ave NW, Grand Rapids, United States, 49503
The venue sits centrally at 250 Monroe Avenue NW in downtown Grand Rapids, easily accessed by car via US‑131 or I‑196—with connections via Monroe or Ottawa exits—and offers shared underground and surface parking. From Gerald R. Ford International Airport, take I‑96 East then I‑196 West into the city; the drive is about 20 minutes. Public transit through Rapid bus routes stops near Monroe or Ottawa Avenue, just a short walk from the Regus entrance; the downtown area is pedestrian-friendly.
Lansing, MI - Regus - One Michigan Avenue
120 North Washington Square, Lansing, United States, 48933
The venue is located in the heart of Lansing’s central business district at 120 North Washington Square, easily accessible by car via I‑496 or US‑127 with convenient street parking and a nearby parking ramp. From Capital Region International Airport (LAN), the location is approximately a 12‑minute drive west via I‑96 and US‑127, with taxis and rideshares readily available. Public transit users can take CATA bus routes that stop just a block away on Washington or Grand Avenue, offering seamless access to the venue.
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 Michigan (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 Michigan (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 Michigan (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.
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Testimonials (2)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life TM
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