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.

Houston, TX – Regus at Capital One Building

5444 Westheimer Rd Uptown, Suite 1000, Houston, United States, 77056

The venue is located in the Capital One Building off Westheimer Road in the Galleria/Uptown area, easily accessed by car via I‑610, US‑59, or Westheimer, with ample attached covered parking and visitor spaces on-site. From Houston’s George Bush Intercontinental Airport (IAH), a taxi or rideshare takes approximately 25–30 minutes via I‑69 South and I‑610 West. From William P. Hobby Airport (HOU), the ride takes about 35 minutes via I‑45 North and I‑610 West. Public transit is served by Metro bus routes along Westheimer Drive with stops close to the building, enabling easy access for attendees without cars.

Explore Our Courses

Federated Learning and Edge AI

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This instructor-led, live training in Houston (online or onsite) is aimed at advanced-level AI researchers, data scientists, and security specialists who wish to implement federated learning techniques for training AI models across multiple edge devices while preserving data privacy.

By the end of this training, participants will be able to:

  • Understand the principles and benefits of federated learning in Edge AI.
  • Implement federated learning models using TensorFlow Federated and PyTorch.
  • Optimize AI training across distributed edge devices.
  • Address data privacy and security challenges in federated learning.
  • Deploy and monitor federated learning systems in real-world applications.
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Federated Learning for Secure AI Collaboration

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This instructor-led, live training in Houston (online or onsite) is aimed at intermediate-level AI and data professionals who wish to understand and implement federated learning techniques for privacy-preserving machine learning and collaborative AI solutions across distributed data sources.

By the end of this training, participants will be able to:

  • Understand the core concepts and benefits of federated learning.
  • Implement distributed training strategies for AI models.
  • Apply federated learning techniques to secure data-sensitive collaborations.
  • Explore case studies and practical examples of federated learning in healthcare and finance.
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Federated Learning in IoT and Edge Computing

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This instructor-led, live training in Houston (online or onsite) is aimed at intermediate-level professionals who wish to apply Federated Learning to optimize IoT and edge computing solutions.

By the end of this training, participants will be able to:

  • Understand the principles and benefits of Federated Learning in IoT and edge computing.
  • Implement Federated Learning models on IoT devices for decentralized AI processing.
  • Reduce latency and improve real-time decision-making in edge computing environments.
  • Address challenges related to data privacy and network constraints in IoT systems.
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Federated Learning for Finance

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This instructor-led, live training in Houston (online or onsite) is aimed at intermediate-level professionals who wish to apply Federated Learning techniques to enhance data privacy and collaborative AI in the financial industry.

By the end of this training, participants will be able to:

  • Understand the principles and benefits of Federated Learning in finance.
  • Implement Federated Learning models for privacy-preserving financial applications.
  • Analyze financial data collaboratively without compromising privacy.
  • Apply Federated Learning to real-world financial scenarios, such as fraud detection and risk management.
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Federated Learning for Healthcare

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This instructor-led, live training in Houston (online or onsite) is aimed at intermediate-level professionals who wish to apply Federated Learning to healthcare scenarios, ensuring data privacy and effective collaboration across institutions.

By the end of this training, participants will be able to:

  • Understand the role of Federated Learning in healthcare.
  • Implement Federated Learning models while ensuring patient data privacy.
  • Collaborate on AI model training across multiple healthcare institutions.
  • Apply Federated Learning to real-world healthcare case studies.
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Advanced Federated Learning Techniques

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This instructor-led, live training in Houston (online or onsite) is aimed at advanced-level professionals who wish to master cutting-edge Federated Learning techniques and apply them to large-scale AI projects.

By the end of this training, participants will be able to:

  • Optimize Federated Learning algorithms for improved performance.
  • Handle non-IID data distributions in Federated Learning.
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Federated Learning for Privacy-Preserving AI

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This instructor-led, live training in Houston (online or onsite) is aimed at intermediate-level professionals who wish to understand and apply Federated Learning to ensure data privacy in AI development.

By the end of this training, participants will be able to:

  • Understand the principles and benefits of Federated Learning.
  • Implement privacy-preserving machine learning models using Federated Learning techniques.
  • Address the challenges of data privacy in decentralized AI training.
  • Apply Federated Learning in real-world scenarios across various industries.
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Introduction to Federated Learning

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This instructor-led, live training in Houston (online or onsite) is aimed at beginner-level professionals who wish to learn the fundamentals of Federated Learning and its practical applications.

By the end of this training, participants will be able to:

  • Understand the principles of Federated Learning.
  • Implement basic Federated Learning algorithms.
  • Address data privacy concerns using Federated Learning.
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Upcoming Courses

Federated Learning for Finance

2025-08-13 9:30 am
14 hours
Houston, TX – Regus at Capital One Building
$ 2703 (Online)
$ 2703 (Classroom)

Federated Learning for Finance

2025-08-27 9:30 am
14 hours
Houston, TX - Regus – Downtown at Pennzoil Place
$ 2703 (Online)
$ 3703 (Classroom)

Advanced Federated Learning Techniques

2025-09-10 9:30 am
21 hours
Houston, TX – Regus at Capital One Building
$ 4054 (Online)
$ 4054 (Classroom)

Federated Learning in IoT and Edge Computing

2025-09-24 9:30 am
14 hours
Houston, TX - Regus – Downtown at Pennzoil Place
$ 2703 (Online)
$ 3703 (Classroom)

Advanced Federated Learning Techniques

2025-10-08 9:30 am
21 hours
Houston, TX – Regus at Capital One Building
$ 4054 (Online)
$ 4054 (Classroom)

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