NobleProg Training Place, City Regus - Downtown Provo

Regus - Downtown Provo

180 North University Avenue, Provo, United States, 84601

Host your next corporate or technical training session with NobleProg at Regus in the Zions Bank Financial Center, situated in downtown Provo’s central business district. This modern coworking venue offers fully equipped meeting rooms with high-speed Wi‑Fi, state-of-the-art audiovisual systems, and on-site support staff ready to assist. Whether for certification prep, hands-on workshops, or executive training, the environment provides a professional and seamless experience for small to mid-sized groups. How to get there: By car: Easily accessible via I‑15, with nearby public parking garages and street parking. By air: Provo Airport is approximately 15 minutes away. By public transit: Located steps from multiple local bus routes.
NobleProg Training Place, City Regus - Salt Lake City - 222 Main

Regus - Salt Lake City - 222 Main

222 South Main Street Downtown, Salt Lake City, United States, 84101

Host your next corporate or technical training session with NobleProg at Regus, located in the heart of downtown Salt Lake City. This modern coworking centre offers professional meeting rooms equipped with high-speed Wi‑Fi, modern audiovisual systems, flexible layouts, and onsite support staff—ideal for instructor-led training, certification prep, and workshops for small to mid-sized teams. How to get there: By car: Easily accessible via I‑15 and I‑80, with multiple public parking garages and street parking nearby. By air: Salt Lake City International Airport (SLC) is approximately 10 minutes away by car. By public transit: Located steps from main bus routes and within walking distance of the TRAX light rail.

Salt Lake Marriott Downtown at City Creek

75 S West Temple, Salt Lake City, United States, 84101

Host your next corporate or technical training session with NobleProg at the Salt Lake Marriott Downtown at City Creek. This premier downtown venue offers professional meeting rooms equipped with high-speed Wi‑Fi, modern audiovisual systems, and full on-site event support. Ideal for workshops, certification prep, and instructor-led sessions, the hotel provides a polished, distraction-free environment for small to mid-size teams, backed by expert support staff to ensure seamless delivery. How to Get There By car: Convenient access via I‑15 and I‑80, with on-site (fee-based) parking and nearby garages. By air: Salt Lake City International Airport (SLC) is approximately 6 miles away, with direct transportation options. By public transit: Walking distance from the City Center TRAX station and main bus lines, connecting to key downtown hubs.

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 Utah (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 Utah (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 Utah (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.
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Kubeflow on AWS

 28 Hours

This instructor-led, live training in Utah (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 Utah (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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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 Utah (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

Kubeflow

2025-08-04 9:30 am
35 hours
Regus - Salt Lake City - 222 Main
$ 6757 (Online)
$ 6757 (Classroom)

Kubeflow

2025-08-18 9:30 am
35 hours
Regus - Salt Lake City - 222 Main
$ 6757 (Online)
$ 6757 (Classroom)

Kubeflow on AWS

2025-09-01 9:30 am
28 hours
Regus - Salt Lake City - 222 Main
$ 6371 (Online)
$ 6371 (Classroom)

DeepSeek: Advanced Model Optimization and Deployment

2025-09-11 9:30 am
14 hours
Regus - Salt Lake City - 222 Main
$ 2703 (Online)
$ 2703 (Classroom)

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Other Countries

These courses are also available in other countries

Consulting

MLOps Consulting
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