Kansas City, MO - Regus – Two Pershing Square (Crown Center)

2300 Main St, Kansas City, United States, 64108

Regus in Two Pershing Square is located just steps from Union Station and is easily accessible by car using Interstate 70, with secure underground parking and skywalk access direct to the building. Travelers from Kansas City International Airport (MCI) can reach the venue in approximately 20 to 25 minutes by taxi or rideshare via I‑70. Public transit users can take the KCATA bus or the free downtown streetcar to Union Station, then walk through the skywalk before taking the elevator to the 9th floor.
NobleProg Training Place, City Jefferson City, MO - Jefferson and High

Jefferson City, MO - Jefferson and High

227 Jefferson Street, Jefferson City, United States,  65101

Jefferson and High is centrally located on Jefferson Street, within easy reach by car, with public parking garages and metered street parking nearby. Guests arriving via Jefferson City Memorial Airport (BCY) can reach the venue in approximately 10 minutes by taxi or rideshare via Highway 54/63 into downtown. Public transit users will find JeffTran bus routes stopping a short walk away on Capitol Avenue, offering seamless access for attendees throughout the city
NobleProg Training Place, City Louis, MO - Regus – Central West End

Louis, MO - Regus – Central West End

4625 Lindell Blvd, St. Louis, United States, 63108

Located on Lindell Boulevard in the Central West End, this Regus center is easily accessible by car using Interstates 64 or 44, with secure underground and surface parking available nearby. Visitors arriving via St. Louis Lambert International Airport (STL) typically reach the venue in 15–20 minutes by taxi or rideshare via I‑70 and Forest Park Expressway. Public transit users can take MetroLink to the Central West End station, followed by a short walk on Lindell to reach the facility.

Kansas City, MO - Plexpod Crossroads

1712 Main Street, Kansas City, United States, 64108

Plexpod Crossroads is situated in downtown Kansas City's Crossroads Arts District and is easily reached by car, with free three-hour street parking and paid daily lots nearby. Visitors arriving from Kansas City International Airport (MCI) can expect a 20–25 minute taxi or rideshare trip via I‑70 into downtown. Public transit and streetcar access are seamless, with lines stopping just one block away—then it’s a short walk into the building.

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 Missouri (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 Missouri (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 Missouri (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 Missouri (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 Missouri (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 Missouri (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
Kansas City, MO - Plexpod Crossroads
$ 6757 (Online)
$ 8757 (Classroom)

MLOps: CI/CD for Machine Learning

2025-09-08 9:30 am
35 hours
Kansas City, MO - Regus – Two Pershing Square (Crown Center)
$ 6757 (Online)
$ 6757 (Classroom)

Kubeflow

2025-09-22 9:30 am
35 hours
Kansas City, MO - Plexpod Crossroads
$ 6757 (Online)
$ 8757 (Classroom)

DeepSeek: Advanced Model Optimization and Deployment

2025-10-06 9:30 am
14 hours
Kansas City, MO - Regus – Two Pershing Square (Crown Center)
$ 2703 (Online)
$ 2703 (Classroom)

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