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 Indiana or in Govtra corporate training centers in Indiana.
Govtra -- Your Local Training Provider for government
Indianapolis, IN - Lockerbie Marketplace
333 N. Alabama Street Suite 350, Indianapolis, United States, 46204
Regus at Lockerbie Marketplace is centrally located in downtown Indianapolis and easily accessible by car, with public parking available along North Alabama Street and in nearby garages. Visitors flying into Indianapolis International Airport (IND) can reach the venue in approximately 20 to 25 minutes via taxi or rideshare, following I‑70 E and exiting onto New York Street toward downtown. For public transit users, IndyGo routes serving the Massachusetts Avenue and Chatham Arch districts stop within a few blocks, making the location convenient for those traveling from other parts of the city.
Fort Wayne, IN - Regus – Power Center
110 E Wayne St floor 12, Fort Wayne, United States, 46802
The venue is conveniently located in downtown Fort Wayne, easily accessible by car via Interstate 69 through either the South Clinton Street or Apple Street exits, which lead directly into the Wayne Street corridor. Visitors will find nearby parking garages as well as metered street parking options. For those arriving by air, the venue is approximately 13 miles northeast of Fort Wayne International Airport (FWA), with a taxi or rideshare ride taking about 20 minutes via I‑69 and Jefferson Boulevard. Public transit is also available: Citilink buses serve downtown with stops just a few blocks away from the venue, near the intersection of Wayne and Clinton Streets.
Indianapolis, IN - Regus – Parkwood Crossing Center
450 E 96th St #500, Indianapolis, United States, 46240
This venue is conveniently accessed by car via the I‑465 beltway, exiting north onto Keystone Avenue before turning onto E 96th Street; ample parking is available in the adjacent surface and garage lots. For those arriving by air, the Indianapolis International Airport (IND) is approximately 17 miles away, with taxis or rideshares taking roughly 25–30 minutes via I‑465 and Keystone Avenue. Public transit is available via IndyGo routes 19 and 120, which serve the 96th Street corridor; the bus stop at Parkwood Crossing is only a short walk from the building.
This instructor-led, live training (offered 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, reduce 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, ensuring alignment with public sector workflows and governance for government.
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 Indiana (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 Indiana (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 Indiana (online or onsite) is designed for engineers who seek to evaluate the available approaches and tools 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 suitable for government use.
Assemble a team with the appropriate skills to construct and support an MLOps system for government operations.
Prepare, validate, and version data for use by machine learning models in a public sector context.
Understand the components of an ML Pipeline and the tools necessary to build one that aligns with public sector workflows.
Experiment with different machine learning frameworks and servers suitable for deployment in government environments.
Operationalize the entire Machine Learning process to ensure it is reproducible and maintainable, meeting governance and accountability standards for government.
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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