Shelton, CT – Regus at One Reservoir Corporate Centre

4 Research Drive, Suite 402, Shelton, United States, 06484

The venue is located within the One Reservoir Corporate Centre business park on Research Drive, easily accessible by car via Route 8 or the Wilbur Cross Parkway, with ample free on-site and adjacent parking. From Tweed New Haven Airport (HVN), approximately 19 miles south, a taxi or rideshare typically takes 25–30 minutes via Route 34 and I‑95 North. Public transit is available via local bus stops on Waterview Drive and Constitution Boulevard South, within a few minutes’ walk to the building entrance, offering convenient options for attendees without a car.

Bridgeport, CT – Regus at Downtown Bridgeport

1000 Lafayette Blvd Downtown, Suite 1100, Bridgeport, United States, 06604

The venue is centrally located in Bridgeport’s downtown core at 1000 Lafayette Boulevard, easily accessible by car via I‑95 and US‑1, with several parking garages nearby and on-site amenities. From Bradley International Airport (BDL), approximately 50 miles north, a taxi or rideshare typically takes 60–70 minutes via I‑84 and I‑95 South. For those using public transit, Metro-North Rail’s Bridgeport Station is about a 5-minute walk from the building, and local Greater Bridgeport Transit (GBT) buses serve stops along Broad and Fairfield Avenues, placing the venue within a short walk for commuters.

Explore Our Courses

Kubeflow

 35 Hours

This instructor-led, live training in Connecticut (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 Fundamentals

 28 Hours

This instructor-led, live training in Connecticut (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...

Kubeflow on AWS

 28 Hours

This instructor-led, live training in Connecticut (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 Connecticut (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...

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Testimonials (1)

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

Kubeflow on AWS

2025-08-18 9:30 am
28 hours
Shelton, CT – Regus at One Reservoir Corporate Centre
$ 6371 (Online)
$ 6371 (Classroom)

Kubeflow Fundamentals

2025-09-01 9:30 am
28 hours
Shelton, CT – Regus at One Reservoir Corporate Centre
$ 5405 (Online)
$ 5405 (Classroom)

Kubeflow on Azure

2025-09-15 9:30 am
28 hours
Shelton, CT – Regus at One Reservoir Corporate Centre
$ 5405 (Online)
$ 5405 (Classroom)

Kubeflow Fundamentals

2025-09-29 9:30 am
28 hours
Shelton, CT – Regus at One Reservoir Corporate Centre
$ 5405 (Online)
$ 5405 (Classroom)

Kubeflow

2025-10-13 9:30 am
35 hours
Shelton, CT – Regus at One Reservoir Corporate Centre
$ 6757 (Online)
$ 6757 (Classroom)

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