Natural Language Processing (NLP) Training in Faro

This instructor-led, live training in Faro (online or onsite) is aimed at intermediate-level software developers and technical writers who wish to leverage LLMs to streamline their coding workflow and create detailed, comprehensive documentation.

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

• Understand the role of LLMs in automating code generation and software documentation.
• Utilize LLMs to create accurate and efficient code snippets and documentation.
• Integrate LLMs into their software development lifecycle for enhanced productivity.
• Maintain high-quality documentation standards using automated tools.
• Address ethical considerations and best practices for using AI in software development.

Ollama is a platform that allows users to run large language and multimodal models locally.

This instructor-led live training (available online or onsite) is designed for intermediate practitioners seeking to master prompt engineering techniques to optimise Ollama outputs.

Upon completion, participants will be able to:

• Design effective prompts for a variety of use cases.
• Apply techniques such as priming and chain-of-thought structuring.
• Implement prompt templates and context management strategies.
• Build multi-stage prompting pipelines for complex workflows.

Course Format

• Interactive lectures and discussions.
• Hands-on exercises focused on prompt design.
• Practical implementation within a live lab environment.

Customisation Options

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

LangGraph is a framework designed 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.

This instructor-led, live training (available online or onsite) targets advanced engineers, AI specialists, and localization leads who aim to implement large language model (LLM) systems for automated translation, quality evaluation, and enterprise governance.

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

• Construct enterprise-grade LLM localization pipelines that integrate both open-source and proprietary models.
• Implement automated QA workflows and define quality metrics to ensure translation consistency.
• Establish governance and approval frameworks for the production of multilingual content.
• Deploy scalable, auditable LLM-based localization systems within secure environments.

The CANN SDK (Compute Architecture for Neural Networks) offers robust deployment and optimization utilities for real-time AI applications in computer vision and NLP, particularly on Huawei Ascend hardware.

This instructor-led live training (available online or onsite) is designed for intermediate-level AI professionals seeking to build, deploy, and optimize vision and language models using the CANN SDK for production-grade solutions.

Upon completing this training, participants will be capable of:

• Deploying and optimizing CV and NLP models utilizing CANN and AscendCL.
• Leveraging CANN utilities to convert models and integrate them into active pipelines.
• Enhancing inference performance for tasks such as detection, classification, and sentiment analysis.
• Constructing real-time CV/NLP pipelines tailored for edge or cloud-based deployment environments.

Course Format

• Interactive lectures and demonstrations.
• Practical labs focused on model deployment and performance profiling.
• Designing live pipelines using real-world CV and NLP scenarios.

Customization Options

• For those requiring customized training for this course, please contact us to arrange.

Mistral models are open-source AI technologies that have expanded into multimodal workflows, supporting both language and vision tasks for enterprise and research applications.

This instructor-led, live training (available online or onsite) targets intermediate-level ML researchers, applied engineers, and product teams looking to build multimodal applications using Mistral models, including OCR and document comprehension pipelines.

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

• Set up and configure Mistral models for multimodal tasks.
• Implement OCR workflows and integrate them with NLP pipelines.
• Design document comprehension applications for enterprise use cases.
• Develop vision-text search and assistive UI functionalities.

Format of the Course

• Interactive lecture and discussion.
• Hands-on coding exercises.
• Live-lab implementation of multimodal pipelines.

Course Customization Options

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

This instructor-led, live training in Faro (online or onsite) is designed for intermediate-level data scientists and developers wishing to apply NLP techniques using Python in Google Colab.

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

• Understand the core concepts of natural language processing.
• Preprocess and clean text data for NLP tasks.
• Perform sentiment analysis using NLTK and SpaCy libraries.
• Work with text data using Google Colab for scalable and collaborative development.

Large language models (LLMs) and autonomous agent frameworks, such as AutoGen and CrewAI, are transforming how DevOps teams automate processes like change tracking, test generation, and alert triage by emulating human-like collaboration and decision-making.

This instructor-led live training, available online or onsite, is tailored for advanced engineers looking to design and implement DevOps automation workflows driven by LLMs and multi-agent systems.

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

• Integrate LLM-based agents into CI/CD workflows for intelligent automation.
• Automate test generation, commit analysis, and change summaries using agents.
• Orchestrate multiple agents to triage alerts, generate responses, and provide DevOps recommendations.
• Construct secure and maintainable agent-powered workflows using open-source frameworks.

Course Format

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

Course Customization Options

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

This instructor-led, live training in Faro (online or onsite) is designed for advanced-level machine learning engineers and AI researchers aiming to create energy-efficient AI solutions using small language models that are both powerful and environmentally sustainable.

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

• Comprehend the impact of AI on energy consumption and the environment.
• Apply model compression and optimization techniques to decrease the size and energy footprint of AI models.
• Leverage energy-efficient hardware and software frameworks for AI deployment.
• Implement best practices for sustainable AI development.
• Advocate for and contribute to sustainable practices within the AI industry.

This instructor-led, live training in Faro (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.

Le Chat Enterprise is a private ChatOps solution that provides secure, customizable, and governed conversational AI capabilities for organizations, with support for RBAC, SSO, connectors, and enterprise app integrations.

This instructor-led, live training (online or onsite) is aimed at intermediate-level product managers, IT leads, solution engineers, and security/compliance teams who wish to deploy, configure, and govern Le Chat Enterprise in enterprise environments.

By the end 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.
• 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.

This instructor-led, live training in Faro (online or onsite) targets intermediate-level data scientists and AI researchers aiming to create engaging and efficient AI-powered conversational experiences using small language models.

Upon completion of this training, participants will be capable of:

• Grasping the core principles of conversational AI and understanding the specific role of SLMs.
• Designing and implementing user-centric AI interactions.
• Developing and training SLMs for interactive applications.
• Assessing and enhancing the effectiveness of human-AI communication through suitable metrics.
• Deploying scalable and ethical AI-driven conversational interfaces in real-world contexts.

This instructor-led, live training in Faro (online or onsite) is aimed at intermediate-level professionals who wish to enhance their skills in generating high-quality, human-like text using advanced NLG methods.

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

• Understand advanced techniques for generating natural language text.
• Implement and fine-tune transformer-based models for NLG.
• Optimize NLG outputs for fluency, coherence, and relevance.
• Evaluate the quality of generated text using both automated and human metrics.

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

This instructor-led live training, available either online or onsite, is designed for intermediate-level engineers and product teams seeking to integrate LangGraph’s graph logic with LLM agent loops 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, retry mechanisms, and fallback protocols to ensure robust execution.
• Integrate retrieval processes, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and fortify agent behavior to enhance reliability and safety.

Course Format

• Interactive lectures accompanied by facilitated discussions.
• Guided laboratory sessions and code walkthroughs within a sandbox environment.
• Scenario-based design exercises and peer review sessions.

Customization Options

• To request a customized version of this course, please contact us to make arrangements.

This instructor-led, live training in Faro (online or onsite) is aimed at intermediate-level data scientists, AI developers, and AI enthusiasts who wish to use LLMs to perform various NLP tasks and create novel and diverse content for different purposes.

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

• Establish a development environment with LLMs and essential tools.
• Expertly perform NLU and NLI tasks with LLMs.
• Extract, infer, and utilize knowledge graphs effectively.
• Generate and manage dialogues using LLMs for conversational applications.
• Evaluate content quality and diversity generated by LLMs and generative AI.
• Apply ethical principles, ensuring fairness and responsible use of LLMs.

This instructor-led, live training in Faro (online or onsite) is aimed at intermediate-level professionals who wish to enhance their NLP projects through the effective fine-tuning of pre-trained language models.

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

• Understand the fundamentals of fine-tuning for NLP tasks.
• Fine-tune pre-trained models such as GPT, BERT, and T5 for specific NLP applications.
• Optimize hyperparameters for improved model performance.
• Evaluate and deploy fine-tuned models in real-world scenarios.

This instructor-led live training (available online or on-site) is designed for data scientists, machine learning engineers, NLP researchers, and AI enthusiasts who aim to grasp the inner workings of GPT models, investigate the capabilities of GPT-3 and GPT-4, and learn how to effectively utilise these models for their NLP tasks.

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

• Grasp the core concepts and principles underlying Generative Pre-trained Transformers.
• Understand the architecture and training process of GPT models.
• Employ GPT-3 for tasks such as text generation, completion, and translation.
• Investigate the latest advancements in GPT-4 and its potential applications.
• Apply GPT models to their own NLP projects and tasks.

LangGraph is a framework designed for constructing graph-structured LLM applications that facilitate planning, branching, tool usage, memory management, and controllable execution.

This instructor-led live training, available both online and onsite, targets beginner-level developers, prompt engineers, and data practitioners eager to design and build reliable, multi-step LLM workflows using LangGraph.

Upon completion 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.

Mistral Medium 3 is a high-performance, multimodal large language model engineered for robust, production-grade deployment within enterprise settings.

This instructor-led training, available either online or on-site, is tailored for AI/ML engineers, platform architects, and MLOps teams at intermediate to advanced levels. The course focuses on deploying, optimizing, and securing Mistral Medium 3 for specific enterprise applications.

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

• Deploy Mistral Medium 3 via API or self-hosted solutions.
• Enhance inference performance while managing costs.
• Develop and implement multimodal use cases using Mistral Medium 3.
• Apply security and compliance best practices suitable for enterprise environments.

Course Format

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

Course Customization Options

• For customized training arrangements, please contact us directly.

Unstructured data is estimated to constitute over 90% of all data, with a significant portion appearing as text. This volume continues to expand rapidly through blog posts, tweets, social media activity, and other digital publications.

This instructor-led, live course focuses on extracting valuable insights and meaning from this vast amount of information. By leveraging the R programming language and Natural Language Processing (NLP) libraries, we integrate concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically interpret the meaning within text data. Data samples are provided in various languages to meet specific customer needs.

Upon completion of this training, participants will be capable of preparing datasets (both large and small) from diverse sources and applying appropriate algorithms to analyze and report on their significance.

Format of the Course

• A blend of lectures and discussions, with a strong emphasis on hands-on practice and occasional assessments to evaluate understanding

This course is designed for managers, solutions architects, innovation officers, CTOs, software architects, and anyone seeking an overview of applied artificial intelligence and its near-term development forecast.

Artificial Intelligence (AI) encompasses a range of technologies designed to create intelligent systems that can interpret data and context to make "intelligent decisions". For telecom providers, leveraging AI in the development of applications and services can significantly enhance operational efficiency and service delivery, particularly in areas such as maintenance and network optimization.

This course explores the various technologies that constitute AI and the skill sets necessary to implement them effectively. Throughout the program, we delve into specific AI applications within the telecom sector.

Audience

• Network engineers
• Network operations staff
• Telecom technical managers

Course Format

• A blend of lectures, discussions, and hands-on exercises

This course is designed for developers and data scientists who wish to understand and implement artificial intelligence in their applications. It specifically focuses on data analysis, distributed AI, and natural language processing.

Chatbots are automated programs that mimic human conversation through chat interfaces. They enable organizations to optimize operational efficiency by offering faster and more streamlined user interaction channels.

During this instructor-led live training, participants will acquire the skills necessary to construct chatbots using Python.

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

• Grasp the fundamental principles of chatbot development
• Construct, evaluate, deploy, and resolve issues with various types of chatbots using Python

Audience

• Developers

Course Format

• A blend of lectures, discussions, practical exercises, and intensive hands-on practice

Note

• To arrange customized training for this course, please get in touch with us.

In this instructor-led, live training in Faro, participants will learn to utilize Python libraries for NLP by building an application that processes a set of images and generates captions.

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

• Design and implement DL for NLP using Python libraries.
• Write Python code that reads a large volume of images and generates keywords.
• Develop Python code that generates captions based on the detected keywords.

This instructor-led live training in Faro (online or on-site) is aimed at intermediate developers, architects, and platform engineers who wish to use MCP to build reliable servers and clients for enterprise deployment and operations.

By the end of this training, participants will be able to: explain practical MCP architecture, build production-ready integrations, deploy and observe MCP services, and apply patterns for versioning, resilience, and support.

This classroom-based training session will delve into NLP techniques alongside the application of AI and Robotics in business. Delegates will undertake computer-based examples and case study solving exercises using Python

This instructor-led, live training (available online or onsite) is designed for developers and data scientists who wish to use spaCy to process very large volumes of text to find patterns and gain insights.

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

• Install and configure spaCy.
• Understand spaCy's approach to Natural Language Processing (NLP).
• Extract patterns and obtain business insights from large-scale data sources.
• Integrate the spaCy library with existing web and legacy applications.
• Deploy spaCy to live production environments to predict human behavior.
• Use spaCy to pre-process text for Deep Learning

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.
• To learn more about spaCy, please visit: https://spacy.io/

In the realm of Python Machine Learning, the Text Summarization feature allows users to ingest input text and generate a concise summary. This functionality is accessible via the command line or as a Python API/Library. A compelling use case is the rapid production of executive summaries, which is highly beneficial for organizations that must review substantial volumes of text data prior to creating reports and presentations.

During this instructor-led live training, participants will learn how to utilize Python to develop a straightforward application that automatically generates summaries from input text.

Upon completing this training, participants will be able to:

• Utilize a command-line tool designed for text summarization.
• Design and build Text Summarization code using Python libraries.
• Evaluate three Python summarization libraries: sumy 0.7.0, pysummarization 1.0.4, and readless 1.0.17.

Audience

• Developers
• Data Scientists

Format of the course

• A blend of lectures, discussions, exercises, and extensive hands-on practice.

TensorFlow™ is an open-source software library designed for numerical computation via data flow graphs.

SyntaxNet serves as a neural network-based Natural Language Processing framework built for TensorFlow.

Word2Vec facilitates the learning of vector representations for words, known as "word embeddings". It offers a highly computationally efficient predictive model for deriving word embeddings from raw text. This technology is available in two variations: the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (refer to Chapters 3.1 and 3.2 in Mikolov et al.).

When employed together, SyntaxNet and Word2Vec enable users to generate Learned Embedding models from Natural Language input.

Audience

This course is designed for developers and engineers who plan to integrate SyntaxNet and Word2Vec models into their TensorFlow graphs.

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

• Grasp the structure and deployment mechanisms of TensorFlow
• Execute installation, production environment setup, architecture tasks, and configuration
• Assess code quality, perform debugging, and implement monitoring
• Implement advanced production-level tasks such as training models, embedding terms, constructing graphs, and logging