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Suzhou, Jinghope Plaza

11/F, 11/F, Tower 2, 88 Hua Chi Street, SIP, Suzhou, China, 215021

Situated right in the core of the Suzhou industry zone, The Regus Centre is on the 11th floor of Jinghope Plaza. Jinghope Plaza is a mixed use complex comprising a high-end shopping mall, 5 star hotel and international Grade A office building. It is one of the most prestigious landmarks in the financial center of Suzhou. Advanced facilities and central location attract many Fortune 500 companies such as Nokia, Philips and 3M. Jinghope Plaza has easy access to excellent facilities and amenities in Suzhou. Metro Line 1 Times Square station is just in the basement of the complex. The high-speed train station which provides easy transportation to Shanghai and Nanjing is only a 5 minutes drive from the centre.

Jinhe Plaza

11th Floor, No. 2, 88 Huachi Street, Industrial Park, Suzhou, China, 215021

The center of Jinhe Plaza is located at the core of Suzhou Industrial Zone, on the east bank of Jinji Lake. Jinhe Plaza is a comprehensive building, including high-end shopping malls, five-star hotels, international Grade A office buildings, and Regus is located on the eleventh floor of the building. As the most famous landmark in Suzhou Financial Center, with its advanced facilities and central location, it has attracted many Fortune 500 companies such as Nokia, Philips and 3M. Jinhe Plaza provides easy access to all major core areas of Suzhou. Metro Line 1 Times Square Station is located on the basement of the building, while the High Speed ​​Rail Station to Shanghai and Nanjing is a 5-minute drive away.

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Kubeflow

Kubeflow

Explore Our Courses

DeepSeek: Advanced Model Optimization and Deployment

 14 Hours

This instructor-led, live training in SuZhou (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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MLOps on Kubernetes: CI/CD Pipelines for Machine Learning

 14 Hours

MLOps on Kubernetes is a framework for automating the training, validation, packaging, and deployment of machine learning models using containerized pipelines and GitOps workflows.

This instructor-led, live training (online or onsite) is aimed at intermediate-level practitioners who wish to build automated, scalable MLOps pipelines on Kubernetes.

After completing this training, participants will be equipped to:

  • Design end-to-end CI/CD pipelines for machine learning.
  • Implement GitOps workflows for model deployment and versioning.
  • Automate training, testing, and packaging of ML models.
  • Integrate monitoring, alerting, and rollback strategies.

Format of the Course

  • Instructor-guided presentations and technical deep dives.
  • Hands-on exercises that build real-world CI/CD workflows.
  • Live-lab practice deploying ML workloads to Kubernetes.

Course Customization Options

  • Organizations may request tailored content aligned with their internal MLOps tools and infrastructure.
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Kubeflow Essentials: Build, Train & Serve with Kubernetes

 14 Hours

Kubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.

Upon completion of this training, attendees will gain the skills to:

  • Navigate the Kubeflow ecosystem and core components.
  • Build reproducible workflows with Kubeflow Pipelines.
  • Run scalable training jobs on Kubernetes.
  • Serve machine learning models efficiently using Kubeflow Serving.

Format of the Course

  • Guided presentations and collaborative discussions.
  • Hands-on labs with real Kubeflow components.
  • Practical exercises to build end-to-end ML workflows.

Course Customization Options

  • Customized versions of this training can be arranged to align with your team’s technology stack and project requirements.
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Docker for MLOps: End-to-End Pipeline Containerization

 21 Hours

Docker is a containerization platform used to build reproducible, portable, and scalable environments for ML systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level technical professionals who wish to containerize and operationalize complete ML pipelines using Docker.

Upon completion of this training, participants will be able to:

  • Containerize ML training, validation, and inference workloads.
  • Design and orchestrate end-to-end ML pipelines using Docker and supporting tools.
  • Implement versioning, reproducibility, and CI/CD for ML components.
  • Deploy, monitor, and scale ML services in containerized environments.

Format of the Course

  • Interactive lectures supported by practical demonstrations.
  • Hands-on exercises focused on building real ML pipeline components.
  • Live-lab implementation for end-to-end containerized workflows.

Course Customization Options

  • For customized training aligned with specific ML infrastructure needs, please contact us to discuss options.
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MLOps: CI/CD for Machine Learning

 35 Hours

This instructor-led, live training in SuZhou (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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Upcoming Courses

MLOps on Kubernetes: CI/CD Pipelines for Machine Learning

2026-08-27 09:30
14 hours
Jinhe Plaza

Docker for MLOps: End-to-End Pipeline Containerization

2026-09-10 09:30
21 hours
Suzhou, Jinghope Plaza

MLOps on Kubernetes: CI/CD Pipelines for Machine Learning

2026-09-24 09:30
14 hours
Jinhe Plaza

MLOps: CI/CD for Machine Learning

2026-10-08 09:30
35 hours
Suzhou, Jinghope Plaza

Kubeflow Essentials: Build, Train & Serve with Kubernetes

2026-10-22 09:30
14 hours
Jinhe Plaza

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