LangGraph Applications in Finance Training Course

LangGraph is a framework designed for constructing stateful, multi-actor LLM applications using composable graphs that maintain persistent state and offer control over execution.

This instructor-led, live training (available both online and onsite) is targeted at advanced-level AI platform engineers, DevOps for AI, and ML architects who aim to optimize, debug, monitor, and manage production-grade LangGraph systems.

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

• Design and optimize complex LangGraph topologies for improved speed, cost efficiency, and scalability.
• Ensure reliability through techniques such as retries, timeouts, idempotency, and checkpoint-based recovery.
• Effectively debug and trace graph executions, inspect state, and systematically reproduce issues encountered in production environments.
• Instrument graphs with logs, metrics, and traces, deploy them to production, and monitor SLAs and costs.

Format of the Course

• Interactive lectures and discussions.
• Ample exercises and hands-on practice.
• Practical implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at intermediate-level financial professionals, risk analysts, and AI engineers who wish to develop and deploy AI-driven solutions for financial automation and fraud detection.

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

• Understand the role of AI in financial automation and fraud detection.
• Build AI models for detecting fraudulent transactions.
• Leverage machine learning for real-time risk assessment.
• Deploy AI-powered financial monitoring systems.

AI in Financial Services is a strategic tool that enhances risk reduction, customer experience, and operational efficiency.

This instructor-led, live training (available online or on-site) is designed for financial services executives, fintech managers, and compliance officers who have limited prior exposure to artificial intelligence. The goal is to help them understand how to implement AI solutions responsibly and effectively within their institutions.

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

• Comprehend the strategic importance of AI in financial services.
• Identify and manage ethical risks associated with AI models.
• Navigate the regulatory environment for AI in finance.
• Develop responsible AI governance and implementation frameworks.

Format of the Course

• Interactive lectures and discussions.
• Case study analysis and group exercises.
• Application of ethical frameworks to practical financial scenarios.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Artificial Intelligence is a robust set of techniques used to create intelligent trading systems that analyze market data, make predictions, and execute strategies autonomously.

This instructor-led, live training (available online or onsite) is designed for intermediate-level finance professionals who want to apply AI techniques in trading and asset management. The focus will be on signal generation, portfolio optimization, and algorithmic strategies.

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

• Understand the role of AI in contemporary financial markets.
• Use Python to develop and backtest algorithmic trading strategies.
• Apply both supervised and unsupervised learning models to financial data.
• Optimize portfolios using AI-driven methods.

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at finance professionals who wish to use ChatGPT to streamline their workflows and improve their data analysis and reporting capabilities.

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

• Understand the basics of ChatGPT and how it works.
• Use ChatGPT to automate financial tasks such as data entry and report generation.
• Analyze financial data with ChatGPT to gain insights and make informed decisions.
• Develop custom ChatGPT models for specific financial use cases.

This comprehensive five-day training program is designed to equip participants with a deep understanding of how artificial intelligence (AI) is revolutionizing financial systems. Through a blend of theoretical knowledge and practical applications, participants will explore cutting-edge AI techniques tailored specifically for the financial sector. By the end of the course, participants will have gained the skills and insights necessary to leverage AI technologies effectively in financial decision-making processes, risk management, fraud detection, algorithmic trading, customer service, and other critical areas within financial institutions. This course aims to empower professionals with the expertise needed to navigate the evolving landscape of AI-driven finance and stay ahead in an increasingly competitive industry.

Generative AI refers to a set of artificial intelligence methods designed to create new content or predictions from existing data, encompassing technologies like Large Language Models (LLMs) and Generative Adversarial Networks (GANs).

This training, led by an instructor, can be conducted either online or on-site. It is tailored for finance professionals at beginner to intermediate levels who aim to leverage generative AI for tasks such as forecasting, anomaly detection, and ensuring compliance within financial services.

By the conclusion of this training, participants will have the ability to:

• Grasp the fundamental principles underlying generative AI models.
• Utilize LLMs and GANs for applications like fraud detection and synthetic data creation.
• Develop effective prompts to support financial forecasting and reporting.
• Assess the ethical and regulatory implications of generative AI in practical scenarios.

Format of the Course

• Engaging lectures and discussions.
• Numerous exercises and hands-on practice.
• Practical implementation in a live-lab setting.

Course Customization Options

• For customizing this training to better suit your needs, please contact us to arrange.

LangGraph is a framework designed for creating graph-structured LLM applications that support planning, branching, tool integration, memory management, and controlled execution.

This instructor-led, live training (available online or on-site) is tailored for beginner-level developers, prompt engineers, and data professionals who aim to design and build reliable, multi-step LLM workflows using LangGraph.

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

• Understand key LangGraph concepts such as nodes, edges, and state, and know when to apply them.
• Create prompt chains that can branch out, call external tools, and maintain a memory state.
• Incorporate retrieval mechanisms and external APIs into graph-based workflows.
• Test, debug, and assess LangGraph applications for reliability and safety.

Format of the Course

• Interactive lectures and facilitated discussions.
• Guided labs and code walkthroughs in a sandbox environment.
• Scenario-based exercises focusing on design, testing, and evaluation.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

LangGraph facilitates stateful, multi-actor workflows driven by LLMs, offering precise control over execution paths and state persistence. In the healthcare sector, these features are essential for compliance, interoperability, and developing decision-support systems that align with medical processes.

This instructor-led, live training (conducted online or on-site) is designed for intermediate to advanced professionals who aim 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:

• Create healthcare-specific LangGraph workflows with a focus on compliance and auditability.
• Integrate LangGraph applications with medical ontologies and standards such as FHIR, SNOMED CT, and ICD.
• Implement 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 lectures and discussions.
• Practical exercises with real-world case studies.
• Hands-on implementation practice in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

LangGraph is a framework designed for constructing stateful, multi-actor LLM applications through composable graphs that maintain persistent state and offer precise control over execution.

This instructor-led, live training (conducted online or on-site) targets intermediate to advanced professionals who aim to design, implement, and manage 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 ensure auditability and compliance.
• Integrate legal ontologies and document standards into the graph state and processing.
• Implement safeguards, 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 lectures and discussions.
• Numerous exercises and practice sessions.
• 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 is a framework designed to create graph-structured workflows for LLMs, enabling branching, tool usage, memory management, and controllable execution.

This instructor-led, live training (available online or on-site) is targeted at intermediate-level engineers and product teams who want to integrate LangGraph’s graph logic with LLM agent loops to develop 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 effectively coordinate LLM agents, tools, and memory.
• Implement conditional routing, retries, and fallback mechanisms for robust execution.
• Integrate retrieval processes, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and enhance the reliability and safety of agent behavior.

Format of the Course

• Interactive lectures and facilitated discussions.
• 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 is a graph-based orchestration framework that facilitates conditional, multi-step workflows involving LLMs and tools, making it ideal for automating and personalizing content pipelines.

This instructor-led, live training (available online or on-site) is designed for intermediate-level marketers, content strategists, and automation developers who want 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 incorporating conditional logic.
• Integrate LLMs, APIs, and data sources for automated personalization.
• Manage state, memory, and context throughout multi-step campaigns.
• Evaluate, monitor, and optimize the performance and delivery outcomes of workflows.

Format of the Course

• Interactive lectures and group discussions.
• Hands-on labs for implementing email workflows and content pipelines.
• Scenario-based exercises focusing on personalization, segmentation, and branching logic.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Machine Learning is a branch of Artificial Intelligence that focuses on developing systems capable of learning from data and making predictions or decisions without explicit programming.

This instructor-led, live training (available online or onsite) is designed for intermediate-level finance professionals who aim to apply machine learning and AI techniques to practical challenges such as fraud detection, credit scoring, and risk modeling.

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

• Grasp the fundamental machine learning concepts pertinent to finance.
• Implement both supervised and unsupervised learning algorithms on financial datasets.
• Construct and assess predictive models for credit risk, fraud detection, and market analysis.
• Utilize Python and scikit-learn to build machine learning pipelines.

Format of the Course

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at intermediate-level finance professionals, data analysts, risk managers, and AI engineers who wish to leverage multimodal AI for risk analysis and fraud detection.

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

• Understand how multimodal AI is applied in financial risk management.
• Analyze structured and unstructured financial data for fraud detection.
• Implement AI models to identify anomalies and suspicious activities.
• Leverage NLP and computer vision for financial document analysis.
• Deploy AI-driven fraud detection models in real-world financial systems.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at intermediate-level finance professionals and fintech developers who wish to use AI-powered prompt engineering techniques to enhance financial analysis, risk management, and decision-making.

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

• Understand the fundamentals of prompt engineering in financial applications.
• Leverage AI models for financial forecasting and market sentiment analysis.
• Automate financial reporting and data extraction using AI prompts.
• Develop AI-driven risk assessment models through optimized prompts.
• Ensure compliance and ethical considerations when using AI in finance.