DeepSeek: Advanced Model Optimization and Deployment Training Course

This instructor-led, live training in Hong Kong (online or onsite) is aimed at intermediate-level developers, data engineers, and software teams who wish to implement DeepSeek Coder for AI-assisted software development, automation, and optimization.

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

• Implement AI-assisted code generation and refactoring in large-scale projects.
• Leverage AI-powered debugging to enhance software reliability.
• Integrate DeepSeek Coder into DevOps and CI/CD pipelines.
• Use AI for intelligent automation in software engineering workflows.

This instructor-led, live training in Hong Kong (online or onsite) is aimed at advanced-level AI engineers, developers, and data analysts who wish to master prompt engineering strategies to maximize the effectiveness of DeepSeek LLM in real-world applications.

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

• Craft advanced prompts to optimize AI responses.
• Control and refine AI-generated text for accuracy and consistency.
• Leverage prompt chaining and context management techniques.
• Mitigate biases and enhance ethical AI usage in prompt engineering.

This instructor-led, live training in Hong Kong (online or onsite) is aimed at intermediate-level to advanced-level architects, urban planners, and design professionals who wish to integrate AI into their design process, from conceptualization to final deliverables.

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

• Generate design iterations using AI-powered text-to-video and image generation tools.
• Use AI to create floor plans, sections, elevations, and material selections.
• Ensure compliance with regulations using AI-driven design validation.
• Integrate AI workflows into Revit and other rendering tools.

This instructor-led, live training in Hong Kong (online or onsite) is aimed at intermediate-level developers, software engineers, and data scientists who wish to leverage DeepSeek APIs for building AI-powered applications.

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

• Understand the capabilities of DeepSeek APIs.
• Integrate DeepSeek APIs into applications.
• Implement AI-powered automation and chatbots.
• Optimize API performance and manage API calls effectively.

This instructor-led, live training in Hong Kong (online or onsite) is aimed at advanced-level AI architects, enterprise developers, and CTOs who wish to deploy, optimize, and scale DeepSeek models within business environments while ensuring security, compliance, and ethical AI practices.

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

• Deploy DeepSeek models in enterprise environments.
• Optimize AI models for performance and scalability.
• Ensure data security and compliance in AI applications.
• Implement ethical AI practices in business solutions.

This instructor-led, live training in Hong Kong (online or onsite) is aimed at advanced-level AI engineers, robotics developers, and automation specialists who wish to leverage DeepSeek for building intelligent AI agents and autonomous systems.

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

• Understand the architecture and capabilities of DeepSeek AI models.
• Integrate DeepSeek into AI agents for decision-making and automation.
• Apply reinforcement learning techniques for training autonomous systems.
• Deploy AI-driven autonomous agents in real-world environments.

This instructor-led, live training in Hong Kong (online or onsite) is aimed at intermediate-level sustainability professionals, researchers, and AI developers who wish to use DeepSeek to apply AI-driven analysis for sustainability, predictive modeling for climate action, and responsible AI applications for social good.

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

• Use DeepSeek models for sustainability-related data analysis.
• Apply AI for climate change modeling, resource optimization, and biodiversity monitoring.
• Develop AI-driven solutions for social impact and sustainable development goals (SDGs).
• Ensure responsible AI practices in sustainability applications.

This instructor-led, live training in Hong Kong (online or onsite) is aimed at intermediate-level content creators, marketers, and media professionals who wish to leverage DeepSeek for AI-assisted writing, automated media generation, and content production workflows.

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

• Generate high-quality text content using DeepSeek models.
• Automate content creation workflows for blogs, social media, and marketing campaigns.
• Integrate AI tools into existing content management systems.
• Enhance creativity and efficiency with AI-driven ideation and structuring.

This instructor-led, live training in Hong Kong (online or onsite) is aimed at intermediate-level business analysts, managers, and decision-makers who wish to leverage DeepSeek for predictive modeling, data visualization, and strategic decision-making.

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

• Use DeepSeek to analyze business data and generate insights.
• Apply predictive modeling for business forecasting.
• Automate reporting and business intelligence workflows.
• Enhance decision-making with AI-powered analytics.

This instructor-led, live training in Hong Kong (online or onsite) is aimed at beginner-level non-technical professionals and entrepreneurs who wish to leverage DeepSeek's open-source models for content creation, automation, and business intelligence.

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

• Understand the fundamentals of no-code AI and its applications in business.
• Use DeepSeek models for content generation and automation.
• Integrate AI tools into existing workflows using platforms like Zapier, Make and Notion.
• Analyze business data and generate actionable insights using AI.
• Develop AI-driven strategies to improve productivity and decision-making.

This instructor-led, live training in Hong Kong (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.

This instructor-led, live training in Hong Kong (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.

This instructor-led, live training in Hong Kong (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.

This instructor-led, live training in Hong Kong (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.

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.