Artificial Intelligence (AI) for City Planning Training Course
What will cities look like in the future? How can Artificial Intelligence (AI) be used to improve city planning? How can AI be used to make cities more efficient, livable, safer and environmentally friendly?
In this instructor-led, live training (onsite or remote), we examine the various technologies that make up AI, as well as the skill sets and mental framework required to put them to use for city planning. We also cover tools and approaches for gathering and organizing relevant data for use in AI, including data mining.
Audience
- City planners
- Architects
- Developers
- Transportation officials
Format of the Course
- Part lecture, part discussion, and a series of interactive exercises.
Note
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction
- AI for city planning
Uses and Opportunities for City Service Providers
- Architecture, transportation, public safety, land use, environment, etc.
Applications for AI
- Computer Vision, Natural Language Procession (NLP), Voice Recognition, etc.
The Data Behind AI
- Data as the enabler of AI
- Gaining access to the data
The Computation behind AI
- Probability and Statistics as the Core
- How Algorithms Enable Intelligence
The Logic Behind AI
- Programming Language used in AI
- Needed skillsets
Teaching Machines How to Learn
- Understanding machine learning
- Applying machine learning libraries to develop intelligent systems
Advanced Approaches to Machine Learning
- Deep Learning
Case Study
- Predicting traffic bottlenecks with machine learning
The Tooling behind AI
- Different databases for different purposes
- Data processing engines
- Building the infrastructure on premise or in the cloud
Analyzing the Data
- Handling large volumes of data
- Aggregating data across agencies
- Data preparation, staging, analysis and reporting
- Data mining approaches
Case Study
- Collecting, filtering and analyzing demographic data by neighborhood
The Interplay of AI and IoT
- Cameras, sensors, actuators, etc.
- Assessing the city's network infrastructure
Autonomous Decision Making and Execution
- Using rules engines and expert systems to make decisions
- Programming machines to take actions on their own
Case Study
- Responding to emergencies based on real-time data
Automating Human Processes
- The interplay of humans and machine
- Optimizing processes in municipal departments
Bringing it All Together
- The low-hanging fruit for city planners
- Constructing a city wide digital platform
Planning and Communicating an AI Strategy
- Needs assessment and return on investment
- Bringing together city leaders, agencies, businesses and universities
Summary and Conclusion
Requirements
- An understanding of city planning
- A basic understanding of programming concepts
Open Training Courses require 5+ participants.
Artificial Intelligence (AI) for City Planning Training Course - Booking
Artificial Intelligence (AI) for City Planning Training Course - Enquiry
Artificial Intelligence (AI) for City Planning - Consultancy Enquiry
Consultancy Enquiry
Upcoming Courses
Related Courses
Advanced LangGraph: Optimization, Debugging, and Monitoring Complex Graphs
35 HoursLangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and control over execution.
This instructor-led, live training (online or onsite) is aimed at advanced-level AI platform engineers, DevOps for AI, and ML architects who wish to optimize, debug, monitor, and operate production-grade LangGraph systems.
By the end of this training, participants will be able to:
- Design and optimize complex LangGraph topologies for speed, cost, and scalability.
- Engineer reliability with retries, timeouts, idempotency, and checkpoint-based recovery.
- Debug and trace graph executions, inspect state, and systematically reproduce production issues.
- Instrument graphs with logs, metrics, and traces, deploy to production, and monitor SLAs and costs.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Advanced Ollama Model Debugging & Evaluation
35 HoursAdvanced Ollama Model Debugging & Evaluation is an in-depth course focused on diagnosing, testing, and measuring model behavior when running local or private Ollama deployments.
This instructor-led, live training (online or onsite) is aimed at advanced-level AI engineers, ML Ops professionals, and QA practitioners who wish to ensure reliability, fidelity, and operational readiness of Ollama-based models in production.
By the end of this training, participants will be able to:
- Perform systematic debugging of Ollama-hosted models and reproduce failure modes reliably.
- Design and execute robust evaluation pipelines with quantitative and qualitative metrics.
- Implement observability (logs, traces, metrics) to monitor model health and drift.
- Automate testing, validation, and regression checks integrated into CI/CD pipelines.
Format of the Course
- Interactive lecture and discussion.
- Hands-on labs and debugging exercises using Ollama deployments.
- Case studies, group troubleshooting sessions, and automation workshops.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Building Private AI Workflows with Ollama
14 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at advanced-level professionals who wish to implement secure and efficient AI-driven workflows using Ollama.
By the end of this training, participants will be able to:
- Deploy and configure Ollama for private AI processing.
- Integrate AI models into secure enterprise workflows.
- Optimize AI performance while maintaining data privacy.
- Automate business processes with on-premise AI capabilities.
- Ensure compliance with enterprise security and governance policies.
Claude AI for Workflow Automation and Productivity
14 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at beginner-level professionals who wish to integrate Claude AI into their daily workflows to improve efficiency and automation.
By the end of this training, participants will be able to:
- Use Claude AI for automating repetitive tasks and streamlining workflows.
- Enhance personal and team productivity using AI-powered automation.
- Integrate Claude AI with existing business tools and platforms.
- Optimize AI-driven decision-making and task management.
Deploying and Optimizing LLMs with Ollama
14 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at intermediate-level professionals who wish to deploy, optimize, and integrate LLMs using Ollama.
By the end of this training, participants will be able to:
- Set up and deploy LLMs using Ollama.
- Optimize AI models for performance and efficiency.
- Leverage GPU acceleration for improved inference speeds.
- Integrate Ollama into workflows and applications.
- Monitor and maintain AI model performance over time.
Fiji: Image Processing for Biotechnology and Toxicology
14 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at beginner-level to intermediate-level researchers and laboratory professionals who wish to process and analyze images related to histological tissues, blood cells, algae, and other biological samples.
By the end of this training, participants will be able to:
- Navigate the Fiji interface and utilize ImageJ’s core functions.
- Preprocess and enhance scientific images for better analysis.
- Analyze images quantitatively, including cell counting and area measurement.
- Automate repetitive tasks using macros and plugins.
- Customize workflows for specific image analysis needs in biological research.
Fine-Tuning and Customizing AI Models on Ollama
14 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at advanced-level professionals who wish to fine-tune and customize AI models on Ollama for enhanced performance and domain-specific applications.
By the end of this training, participants will be able to:
- Set up an efficient environment for fine-tuning AI models on Ollama.
- Prepare datasets for supervised fine-tuning and reinforcement learning.
- Optimize AI models for performance, accuracy, and efficiency.
- Deploy customized models in production environments.
- Evaluate model improvements and ensure robustness.
Introduction to Claude AI: Conversational AI and Business Applications
14 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at beginner-level business professionals, customer support teams, and tech enthusiasts who wish to understand the fundamentals of Claude AI and leverage it for business applications.
By the end of this training, participants will be able to:
- Understand Claude AI’s capabilities and use cases.
- Set up and interact with Claude AI effectively.
- Automate business workflows with conversational AI.
- Enhance customer engagement and support using AI-driven solutions.
LangGraph Applications in Finance
35 HoursLangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and control over execution.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to design, implement, and operate LangGraph-based finance solutions with proper governance, observability, and compliance.
By the end of this training, participants will be able to:
- Design finance-specific LangGraph workflows aligned to regulatory and audit requirements.
- Integrate financial data standards and ontologies into graph state and tooling.
- Implement reliability, safety, and human-in-the-loop controls for critical processes.
- Deploy, monitor, and optimize LangGraph systems for performance, cost, and SLAs.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
LangGraph Foundations: Graph-Based LLM Prompting and Chaining
14 HoursLangGraph is a framework for building graph-structured LLM applications that support planning, branching, tool use, memory, and controllable execution.
This instructor-led, live training (online or onsite) is aimed at beginner-level developers, prompt engineers, and data practitioners who wish to design and build reliable, multi-step LLM workflows using LangGraph.
By the end of this training, participants will be able to:
- Explain core LangGraph concepts (nodes, edges, state) and when to use them.
- Build prompt chains that branch, call tools, and maintain memory.
- Integrate retrieval and external APIs into graph workflows.
- Test, debug, and evaluate LangGraph apps for reliability and safety.
Format of the Course
- Interactive lecture and facilitated discussion.
- Guided labs and code walkthroughs in a sandbox environment.
- Scenario-based exercises on design, testing, and evaluation.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
LangGraph in Healthcare: Workflow Orchestration for Regulated Environments
35 HoursLangGraph enables stateful, multi-actor workflows powered by LLMs with precise control over execution paths and state persistence. In healthcare, these capabilities are crucial for compliance, interoperability, and building decision-support systems that align with medical workflows.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to design, implement, and manage LangGraph-based healthcare solutions while addressing regulatory, ethical, and operational challenges.
By the end of this training, participants will be able to:
- Design healthcare-specific LangGraph workflows with compliance and auditability in mind.
- Integrate LangGraph applications with medical ontologies and standards (FHIR, SNOMED CT, ICD).
- Apply best practices for reliability, traceability, and explainability in sensitive environments.
- Deploy, monitor, and validate LangGraph applications in healthcare production settings.
Format of the Course
- Interactive lecture and discussion.
- Hands-on exercises with real-world case studies.
- Implementation practice in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
LangGraph for Legal Applications
35 HoursLangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and precise control over execution.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to design, implement, and operate LangGraph-based legal solutions with the necessary compliance, traceability, and governance controls.
By the end of this training, participants will be able to:
- Design legal-specific LangGraph workflows that preserve auditability and compliance.
- Integrate legal ontologies and document standards into graph state and processing.
- Implement guardrails, human-in-the-loop approvals, and traceable decision paths.
- Deploy, monitor, and maintain LangGraph services in production with observability and cost controls.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Building Dynamic Workflows with LangGraph and LLM Agents
14 HoursLangGraph is a framework for composing graph-structured LLM workflows that support branching, tool use, memory, and controllable execution.
This instructor-led, live training (online or onsite) is aimed at intermediate-level engineers and product teams who wish to combine LangGraph’s graph logic with LLM agent loops to build dynamic, context-aware applications such as customer support agents, decision trees, and information retrieval systems.
By the end of this training, participants will be able to:
- Design graph-based workflows that coordinate LLM agents, tools, and memory.
- Implement conditional routing, retries, and fallbacks for robust execution.
- Integrate retrieval, APIs, and structured outputs into agent loops.
- Evaluate, monitor, and harden agent behavior for reliability and safety.
Format of the Course
- Interactive lecture and facilitated discussion.
- Guided labs and code walkthroughs in a sandbox environment.
- Scenario-based design exercises and peer reviews.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
LangGraph for Marketing Automation
14 HoursLangGraph is a graph-based orchestration framework that enables conditional, multi-step LLM and tool workflows, ideal for automating and personalizing content pipelines.
This instructor-led, live training (online or onsite) is aimed at intermediate-level marketers, content strategists, and automation developers who wish to implement dynamic, branching email campaigns and content generation pipelines using LangGraph.
By the end of this training, participants will be able to:
- Design graph-structured content and email workflows with conditional logic.
- Integrate LLMs, APIs, and data sources for automated personalization.
- Manage state, memory, and context across multi-step campaigns.
- Evaluate, monitor, and optimize workflow performance and delivery outcomes.
Format of the Course
- Interactive lectures and group discussions.
- Hands-on labs implementing email workflows and content pipelines.
- Scenario-based exercises on personalization, segmentation, and branching logic.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Getting Started with Ollama: Running Local AI Models
7 HoursThis instructor-led, live training in Chile (online or onsite) is aimed at beginner-level professionals who wish to install, configure, and use Ollama for running AI models on their local machines.
By the end of this training, participants will be able to:
- Understand the fundamentals of Ollama and its capabilities.
- Set up Ollama for running local AI models.
- Deploy and interact with LLMs using Ollama.
- Optimize performance and resource usage for AI workloads.
- Explore use cases for local AI deployment in various industries.