Charlotte, NC – City Center

525 North Tryon Street, Suite 1600, Charlotte, United States, 28202

The venue is located in Uptown Charlotte’s City Center district at 525 North Tryon Street, easily reachable by car via I‑77, I‑85, or I‑277, with access to adjacent parking garages connected via pedestrian skywalks. From Charlotte Douglas International Airport (CLT), roughly 10 miles southwest, a taxi or rideshare takes approximately 15–20 minutes via I‑485, I‑77 North, then Trade Street. Public transit is convenient: the Lynx Blue Line light rail stops at 9th Street station two blocks away, and multiple CATS bus routes travel along Tryon and Trade streets—making it accessible for participants without cars.

Cary, NC – Regus at Regency Parkway

2500 Regency Pkwy, Cary, United States, 27518

The venue sits within the Regency Park campus at 2500 Regency Parkway, conveniently accessible by car via US 1/US 64 and I‑40, with free on-site parking and adjacent lots. From Raleigh–Durham International Airport (RDU), about 15 miles northwest, taxis or rideshares typically take 20–25 minutes via US 64 East. Public transit users can use GoCary and GoRaleigh bus routes; Cary Station is about 0.8 miles away, offering access via regional transport followed by a short walk or quick rideshare to the building.

Chapel Hill, NC – Regus at Environ Way

1340 Environ Way, Chapel Hill, United States, 27517

The venue is situated on Environ Way in Chapel Hill’s sustainably designed office park, easily reached by car via US‑15‑501 and NC‑54, with ample on-site and municipal lot parking. From Raleigh–Durham International Airport (RDU), approximately 12 miles northwest, taxis or rideshares typically take 20–25 minutes via I‑40 East and US‑15‑501 South. Public transit is convenient: Chapel Hill Transit buses stop nearby on East Franklin Street, and UNC’s campus is a short walk away—making the location ideal for attendees without cars.

Durham, NC – Regus at Shannon Road

3511 Shannon Road, 3rd Floor, Durham, United States, 27707

The venue is situated in a suburban business park with easy car access via I‑40 and NC‑147, and offers ample on-site parking. From Raleigh-Durham International Airport (RDU), about 7 miles northwest, taxis or rideshares typically take 15–20 minutes via I‑40 East. Public transit options include GoDurham and GoTriangle bus routes that serve nearby Shannon and Cornwallis Roads, with a short walk to the building entrance.

Raleigh, NC – Regus at Raleigh City Plaza

421 Fayetteville Street, Plaza Downtown, Suite 1100, Raleigh, United States, 27601

The venue is located in downtown Raleigh within the One City Plaza tower on Fayetteville Street, easily accessible by car via I‑40 or I‑440, with several public parking garages within a block. From Raleigh‑Durham International Airport (RDU), approximately 17 miles west, taxis or rideshares typically take 20–25 minutes via I‑40 East. For attendees using public transit, GoRaleigh bus routes stop on Fayetteville and Salisbury Streets directly in front of One City Plaza, offering convenient walkable access to the building.

Subcategories (2)

Kubeflow

Kubeflow
MLflow

MLflow

Explore Our Courses

DeepSeek: Advanced Model Optimization and Deployment

 14 Hours

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

 28 Hours

This instructor-led, live training in North Carolina (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 Carolina (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.
Read more...

Kubeflow Fundamentals

 28 Hours

This instructor-led, live training in North Carolina (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.
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MLOps: CI/CD for Machine Learning

 35 Hours

This instructor-led, live training in North Carolina (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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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.
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 for Azure Machine Learning

2025-09-08 9:30 am
14 hours
Chapel Hill, NC – Regus at Environ Way
$ 2703 (Online)
$ 3703 (Classroom)

DeepSeek: Advanced Model Optimization and Deployment

2025-09-22 9:30 am
14 hours
Cary, NC – Regus at Regency Parkway
$ 2703 (Online)
$ 3703 (Classroom)

MLOps: CI/CD for Machine Learning

2025-10-06 9:30 am
35 hours
Chapel Hill, NC – Regus at Environ Way
$ 6757 (Online)
$ 9257 (Classroom)

MLflow

2025-10-20 9:30 am
21 hours
Cary, NC – Regus at Regency Parkway
$ 4054 (Online)
$ 5554 (Classroom)

MLflow

2025-11-03 9:30 am
21 hours
Chapel Hill, NC – Regus at Environ Way
$ 4054 (Online)
$ 5554 (Classroom)

MLflow

2025-11-17 9:30 am
21 hours
Cary, NC – Regus at Regency Parkway
$ 4054 (Online)
$ 5554 (Classroom)

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