Text Summarization with Python Training Course

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

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

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

• Design and optimize complex LangGraph topologies to enhance speed, reduce costs, and ensure scalability.
• Engineer reliability by implementing retries, timeouts, idempotency, and checkpoint-based recovery mechanisms.
• Debug and trace graph executions, inspect state data, and systematically reproduce production issues.
• Instrument graphs with logs, metrics, and traces; deploy to production environments; and monitor SLAs and costs.

Course Format

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

Customization Options

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

Devstral is an open-source framework engineered to facilitate the creation and execution of coding agents capable of interacting with codebases, developer utilities, and APIs to boost engineering productivity.

This instructor-led, live training (available online or onsite) targets intermediate to advanced ML engineers, developer-tooling teams, and SREs looking to design, implement, and optimize coding agents using Devstral.

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

• Set up and configure Devstral for coding agent development.
• Design agentic workflows for codebase exploration and modification.
• Integrate coding agents with developer tools and APIs.
• Implement best practices for secure and efficient agent deployment.

Course Format

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

Customization Options

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

This instructor-led, live training in India (online or onsite) is designed for data scientists and software engineers who want to leverage Dask within the Python ecosystem to build, scale, and analyze large datasets.

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

• Set up the necessary environment to begin building big data processing workflows with Dask and Python.
• Explore the features, libraries, tools, and APIs available in Dask.
• Understand how Dask accelerates parallel computing in Python.
• Learn how to scale the Python ecosystem (NumPy, SciPy, and Pandas) using Dask.
• Optimize the Dask environment to maintain high performance when handling large datasets.

This instructor-led, live training in India (online or onsite) is aimed at intermediate-level Python developers and data analysts who wish to enhance their skills in data analysis and manipulation using Pandas and NumPy.

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

• Set up a development environment that includes Python, Pandas, and NumPy.
• Create a data analysis application using Pandas and NumPy.
• Perform advanced data wrangling, sorting, and filtering operations.
• Conduct aggregate operations and analyze time series data.
• Visualize data using Matplotlib and other visualization libraries.
• Debug and optimize their data analysis code.

Devstral and Mistral models are open-source AI technologies designed for flexible deployment, fine-tuning, and scalable integration.

This instructor-led live training (available online or onsite) targets intermediate to advanced ML engineers, platform teams, and research engineers looking to self-host, fine-tune, and govern Mistral and Devstral models in production environments.

Upon completing this training, participants will be able to:

• Set up and configure self-hosted environments for Mistral and Devstral models.
• Apply fine-tuning techniques to optimize domain-specific performance.
• Implement versioning, monitoring, and lifecycle governance.
• Ensure security, compliance, and responsible usage of open-source models.

Course Format

• Interactive lectures and discussions.
• Hands-on exercises focused on self-hosting and fine-tuning.
• Live-lab sessions for implementing governance and monitoring pipelines.

Course Customization Options

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

This instructor-led live training, offered online or on-site, is designed for developers who wish to utilize the FARM (FastAPI, React, and MongoDB) stack to create dynamic, high-performance, and scalable web applications.

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

• Establish the necessary development environment integrating FastAPI, React, and MongoDB.
• Comprehend the essential concepts, features, and benefits associated with the FARM stack.
• Master the process of building REST APIs with FastAPI.
• Learn techniques for designing interactive applications using React.
• Develop, test, and deploy full-stack applications (frontend and backend) leveraging the FARM stack.

This instructor-led live training in India (online or onsite) is designed for developers who want to utilize FastAPI with Python to build, test, and deploy RESTful APIs with greater ease and speed.

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

• Set up the essential development environment for building APIs with Python and FastAPI.
• Produce APIs faster and more efficiently using the FastAPI library.
• Learn to create data models and schemas based on Pydantic and OpenAPI.
• Link APIs to a database using SQLAlchemy.
• Apply security and authentication features to APIs using FastAPI utilities.
• Construct container images and deploy web APIs onto a cloud server.

LangGraph serves as a framework for constructing stateful, multi-agent LLM applications through composable graphs, enabling persistent state management and precise execution control.

This instructor-led live training, available online or onsite, targets intermediate to advanced professionals seeking to design, implement, and manage finance solutions based on LangGraph, ensuring proper governance, observability, and compliance.

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

• Develop finance-specific LangGraph workflows that align with regulatory and audit requirements.
• Integrate financial data standards and ontologies into graph states and tooling.
• Implement reliability, safety, and human-in-the-loop controls for critical operations.
• Deploy, monitor, and optimize LangGraph systems to enhance performance, manage costs, and meet SLAs.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical practice.
• Hands-on implementation within a live laboratory environment.

Customization Options

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

LangGraph is a framework designed for constructing graph-structured LLM applications, supporting planning, branching, tool use, memory, and controllable execution.

This instructor-led, live training (available 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 facilitates stateful, multi-actor workflows driven by LLMs, offering precise control over execution paths and state persistence. In the healthcare sector, these features are vital for ensuring compliance, enabling interoperability, and developing decision-support systems that align with medical workflows.

This instructor-led, live training (available online or onsite) is designed for intermediate to advanced professionals aiming to design, implement, and manage LangGraph-based healthcare solutions while addressing regulatory, ethical, and operational challenges.

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

• Design healthcare-specific LangGraph workflows with a focus on compliance and auditability.
• 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 serves as a framework for constructing stateful, multi-actor LLM applications through composable graphs, enabling persistent state management and precise control over execution flow.

This instructor-led live training, available online or on-site, targets intermediate to advanced professionals aiming to design, implement, and manage LangGraph-based legal solutions, ensuring necessary compliance, traceability, and governance controls.

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

• Design LangGraph workflows tailored for legal requirements that maintain auditability and compliance.
• Integrate legal ontologies and document standards into graph states and processing logic.
• Implement guardrails, human-in-the-loop approval mechanisms, and traceable decision paths.
• Deploy, monitor, and maintain LangGraph services in production environments with robust observability and cost management.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical sessions.
• Hands-on implementation within a live laboratory environment.

Customization Options

• For customized training arrangements, please reach out to us to coordinate.

LangGraph serves as a framework designed for composing graph-structured workflows involving Large Language Models (LLMs), enabling capabilities such as branching logic, tool utilization, memory management, and controllable execution.

This instructor-led live training, available both online and onsite, targets intermediate-level engineers and product teams aiming to merge LangGraph’s graph-based logic with LLM agent loops. The objective is to develop dynamic, context-aware applications, including customer support agents, decision trees, and information retrieval systems.

Upon completing this training, participants will be equipped to:

• Design graph-based workflows that effectively coordinate LLM agents, tools, and memory.
• Implement conditional routing, retries, and fallback mechanisms to ensure robust execution.
• Integrate retrieval processes, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and secure agent behavior to guarantee reliability and safety.

Course Format

• Interactive lectures coupled with facilitated discussions.
• Guided labs and code walkthroughs conducted within a sandbox environment.
• Scenario-based design exercises and peer review sessions.

Customization Options for the Course

• For requests regarding customized training for this course, please reach out to us to make arrangements.

LangGraph serves as a graph-based orchestration framework that facilitates conditional, multi-step workflows involving Large Language Models (LLMs) and tools, making it ideally suited for automating and personalizing content pipelines.

This instructor-led live training, available either online or at the participant's site, targets intermediate-level marketers, content strategists, and automation developers looking to build dynamic, branching email campaigns and content generation pipelines using LangGraph.

Upon completing this training, participants will be capable of:

• Designing graph-structured workflows for email and content that incorporate conditional logic.
• Integrating LLMs, APIs, and various data sources to achieve automated personalization.
• Managing state, memory, and context across complex, multi-step marketing campaigns.
• Evaluating, monitoring, and optimizing the performance and delivery outcomes of these workflows.

Course Format

• Interactive lectures paired with group discussions.
• Practical, hands-on labs focused on implementing email workflows and content pipelines.
• Scenario-based exercises covering personalization, segmentation, and branching logic.

Customization Options

• For those interested in tailored training for this course, please get in touch with us to arrange a customized schedule.

Le Chat Enterprise offers a private ChatOps solution, delivering secure, customizable, and governed conversational AI capabilities tailored for organizational needs. It supports Role-Based Access Control (RBAC), Single Sign-On (SSO), connectors, and seamless integration with enterprise applications.

This instructor-led live training, available online or onsite, targets intermediate-level product managers, IT leads, solution engineers, and security/compliance teams who aim to deploy, configure, and govern Le Chat Enterprise within enterprise settings.

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

• Set up and configure Le Chat Enterprise for secure deployments.
• Enable RBAC, SSO, and compliance-driven controls.
• Integrate Le Chat with enterprise applications and data stores.
• Design and implement governance and admin playbooks for ChatOps.

Format of the Course

• Interactive lecture and discussion.
• Extensive 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.

This instructor-led, live training in India (online or onsite) is designed for data scientists and developers who wish to utilize Modin to build and implement parallel computations with Pandas for faster data analysis.

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

• Set up the necessary environment to start developing Pandas workflows at scale with Modin.
• Understand the features, architecture, and advantages of Modin.
• Know the differences between Modin, Dask, and Ray.
• Perform Pandas operations faster with Modin.
• Implement the entire Pandas API and functions.