NobleProg Training Place, City Bismarck, ND - Juniper Workantile

Bismarck, ND - Juniper Workantile

122 North 4th St., Bismarck, United States, 58501

The venue is located in historic downtown Bismarck at 122 North 4th Street, easily accessible by car via I‑94 or US‑83 with street and nearby garage parking available. From Bismarck Municipal Airport (BIS), take Highway 83 South into town—a taxi or rideshare typically takes about 10 minutes. Public transit users can take Bis‑Man Transit bus routes stopping within a block on 4th Street, followed by a short walk to the venue.
NobleProg Training Place, City Fargo, ND - Regus - Brandt Office Park

Fargo, ND - Regus - Brandt Office Park

3523 45th Street South, Suite 100, Fargo, United States, 58104

The venue is located just off I‑29 in Fargo’s well-connected Brandt Office Park, easily accessible by car via exit 65 on I‑29; free on-site parking is available for all attendees. From Hector International Airport (FAR), take I‑29 South, exit at 45th Street, and drive approximately 5 minutes—taxi or rideshare options are plentiful. Public transit is also convenient with local MATBUS routes stopping near 45th Street South, followed by a short walk into the business park.
NobleProg Training Place, City Fargo, ND - Railyard Offices NobleProg Training Place, City Fargo, ND - Railyard Offices

Fargo, ND - Railyard Offices

1630 1st Avenue North, Suite B, Fargo, United States,  58102

The venue is situated in the midst of downtown Fargo at 1630 1st Avenue North, conveniently accessible by car via I‑94 or I‑29 with exits onto 1st Avenue North; public parking is available nearby. From Hector International Airport, take I‑94 East to exit toward downtown, then follow signs to 1st Avenue North—approximately a 15‑minute taxi or rideshare ride. Public transit riders can use MATBUS routes that stop within walking distance, and the venue is located in a pedestrian-friendly area.

Subcategories (2)

Category for Kubeflow

Kubeflow
Category for MLflow

MLflow

Explore Our Courses

DeepSeek: Advanced Model Optimization and Deployment

 14 Hours

This instructor-led, live training in North Dakota (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, 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.
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Kubeflow

 35 Hours

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

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.
  • Run entire machine learning pipelines on diverse architectures and cloud environments.
  • Using Kubeflow to spawn and manage Jupyter notebooks.
  • Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
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MLOps for Azure Machine Learning

 14 Hours

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

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

  • Build reproducible workflows and machine learning models.
  • Manage the machine learning lifecycle.
  • Track and report model version history, assets, and more.
  • Deploy production ready machine learning models anywhere.
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MLOps: CI/CD for Machine Learning

 35 Hours

This instructor-led, live training in North Dakota (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.
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Kubeflow on AWS

 28 Hours

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

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

  • Install and configure Kubernetes, Kubeflow and other needed software on AWS.
  • Use EKS (Elastic Kubernetes Service) to simplify the work of initializing a Kubernetes cluster on AWS.
  • Create and deploy a Kubernetes pipeline for automating and managing 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.
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Kubeflow on Azure

 28 Hours

This instructor-led, live training in North Dakota (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to Azure cloud.

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

  • Install and configure Kubernetes, Kubeflow and other needed software on Azure.
  • Use Azure Kubernetes Service (AKS) to simplify the work of initializing a Kubernetes cluster on Azure.
  • Create and deploy a Kubernetes pipeline for automating and managing 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.
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Kubeflow Fundamentals

 28 Hours

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

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

  • Install and configure Kubeflow on premise 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.
  • Using Kubeflow to spawn and manage Jupyter notebooks.
  • Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
Read more...

MLflow

 21 Hours

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

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

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

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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

Course - Kubeflow

Upcoming Courses

MLOps: CI/CD for Machine Learning

2025-08-25 9:30 am
35 hours
Fargo, ND - Regus - Brandt Office Park
$ 6757 (Online)
$ 6757 (Classroom)

MLOps: CI/CD for Machine Learning

2025-09-08 9:30 am
35 hours
Fargo, ND - Regus - Brandt Office Park
$ 6757 (Online)
$ 6757 (Classroom)

Kubeflow

2025-09-22 9:30 am
35 hours
Fargo, ND - Regus - Brandt Office Park
$ 6757 (Online)
$ 6757 (Classroom)

DeepSeek: Advanced Model Optimization and Deployment

2025-10-06 9:30 am
14 hours
Fargo, ND - Regus - Brandt Office Park
$ 2703 (Online)
$ 2703 (Classroom)

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