Houston, TX - Regus – Downtown at Pennzoil Place

700 Milam Street, Suite 1300, Houston, United States, 77002

The venue is centrally located near major highways, including I‑45, I‑69, and US‑59, with convenient parking available in the building’s garage as well as nearby garages. For those arriving by air, both George Bush Intercontinental Airport (IAH) and William P. Hobby Airport (HOU) are just 7 to 10 miles away. Public transit is easily accessible via METRO bus routes, and the venue also offers tunnel access to downtown amenities.

Dallas, TX - Regus - Downtown Republic Center

325 N. St. Paul Street, Dallas, United States, 75201

The venue is conveniently located in downtown Dallas and easily accessible by car via I‑35E, US‑75, and I‑30, with several parking garages and surface lots nearby. Public transit access is excellent, just steps from St. Paul Station on the DART Blue and Red lines and connected to the downtown tunnel network. For those arriving by air, Dallas Love Field is approximately 7 miles away, while DFW International Airport is about 20 miles from the venue. Valet service is available at the building, along with multiple nearby parking options.

Austin, TX - Regus - Lamar Central

3800 N Lamar Blvd, Suite 200, Austin, United States, 78756

Easily reachable by car via Mopac (Loop 1) and US‑183. Plenty of nearby public parking is available. For air travelers, Austin-Bergstrom International Airport (AUS) is approximately 10 miles away. Local transit access is provided via bus routes serving Lamar Blvd.

Lubbock, TX

1610 Broadway, Lubbock, United States, 79401

The venue is conveniently located just off I‑27, offering easy access by car with on-site self-parking and nearby garages. For those arriving by air, Fort Smith Regional Airport is approximately 6 miles away, providing quick and convenient access. Public transit is also available, with downtown bus routes serving the area and the venue within walking distance of central Lubbock locations.

Austin, TX - Regus - Littlefield Congress

106 E 6th St, Austin, United States, 78701

The venue is conveniently situated near I‑35 and US‑183, making it easily accessible by car, with both public and garage parking options—such as the Littlefield Garage at 508 Brazos—located within walking distance. For those using public transportation, the venue is just a short walk from the Downtown Station (CapMetro) and is served by several bus routes. Travelers arriving by air will find that Austin-Bergstrom International Airport is approximately 8 miles away, providing straightforward access to and from the venue.

San Antonio, TX - Regus - One Riverwalk Place

700 North Saint Mary's Street, One Riverwalk Place, Ste 1400, San Antonio, United States, 78205

The venue is easily accessible by car via major highways including I‑35, I‑37, and I‑10, with the convenience of an on-site parking garage and nearby public lots. For those traveling by air, San Antonio International Airport (SAT) is located approximately 8 miles away. Public transit options are also readily available, with several bus lines nearby and the downtown VIA Transit hub just a short walk from the building.

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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 Texas (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 Texas (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 Texas (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 Texas (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 Texas (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 Texas (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.
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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
Austin, TX - Regus - Lamar Central
$ 6757 (Online)
$ 7757 (Classroom)

Kubeflow

2025-08-18 9:30 am
35 hours
Lubbock, TX
$ 6757 (Online)
$ 6757 (Classroom)

Kubeflow on AWS

2025-09-01 9:30 am
28 hours
Austin, TX - Regus - Lamar Central
$ 6371 (Online)
$ 7171 (Classroom)

DeepSeek: Advanced Model Optimization and Deployment

2025-09-11 9:30 am
14 hours
Lubbock, TX
$ 2703 (Online)
$ 2703 (Classroom)

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