Natural Language Processing (NLP) Training in Faro

This instructor-led, live training in Faro (online or onsite) is aimed at intermediate-level financial analysts, data scientists, and investment professionals who wish to leverage LLMs for financial market analysis and prediction.

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

• Understand the application of LLMs in financial market analysis.
• Use LLMs to process financial news, reports, and data for market insights.
• Develop predictive models for stock prices, market trends, and economic indicators.
• Integrate LLM insights into investment decision-making processes.

This instructor-led, live training in Faro (online or onsite) is designed for cybersecurity professionals and data scientists at an intermediate level who wish to leverage LLMs to strengthen cybersecurity measures and threat intelligence.

Upon completing this training, participants will be able to:

• Grasp the role of LLMs in cybersecurity.
• Implement LLMs for threat detection and analysis.
• Utilize LLMs for security automation and response.
• Integrate LLMs with existing security infrastructure.

This instructor-led live training in Faro (online or on-site) is targeted at intermediate-level data scientists seeking to acquire a comprehensive grasp and practical skills in both Large Language Models (LLMs) and Reinforcement Learning (RL).

By the conclusion of this training, participants will be capable of:

• Understanding the structure and operation of transformer models.
• Optimizing and fine-tuning LLMs for specific tasks and use cases.
• Grasping the core principles and methodologies of reinforcement learning.
• Learning how reinforcement learning techniques can boost LLM performance.

This instructor-led, live training in Faro (online or onsite) is aimed at intermediate-level ML practitioners and AI developers who wish to fine-tune and deploy open-weight models like LLaMA, Mistral, and Qwen for specific business or internal applications.

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

• Understand the ecosystem and differences between open-source LLMs.
• Prepare datasets and fine-tuning configurations for models like LLaMA, Mistral, and Qwen.
• Execute fine-tuning pipelines using Hugging Face Transformers and PEFT.
• Evaluate, save, and deploy fine-tuned models in secure environments.

This instructor-led, live training in Faro (online or on-site) is designed for intermediate to advanced-level machine learning engineers, AI developers, and data scientists who wish to learn how to employ QLoRA to efficiently fine-tune large models for specific tasks and customisations.

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

• Comprehend the theory behind QLoRA and quantization techniques for LLMs.
• Implement QLoRA for fine-tuning large language models in domain-specific applications.
• Optimize fine-tuning performance on limited computational resources through quantization.
• Deploy and evaluate fine-tuned models in real-world applications efficiently.

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 lightweight platform designed for running large language models locally.

This instructor-led live training (available online or onsite) is designed for intermediate-level healthcare practitioners and IT teams looking to deploy, customize, and operationalize Ollama-based AI solutions within clinical and administrative settings.

Upon completing this training, participants will be able to:

• Install and configure Ollama for secure use in healthcare environments.
• Integrate local LLMs into clinical workflows and administrative processes.
• Customize models for healthcare-specific terminology and tasks.
• Apply best practices for privacy, security, and regulatory compliance.

Course Format

• Interactive lectures and discussions.
• Hands-on demonstrations and guided exercises.
• Practical implementation in a sandboxed healthcare simulation environment.

Customization Options

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

Ollama is a platform that enables the local execution of large language and multimodal models, supporting robust secure deployment strategies.

This instructor-led live training (available online or onsite) targets intermediate-level professionals aiming to deploy Ollama with stringent data privacy and regulatory compliance standards.

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

• Deploy Ollama securely within containerized and on-premises environments.
• Apply differential privacy techniques to protect sensitive information.
• Implement secure logging, monitoring, and auditing procedures.
• Enforce data access controls in alignment with compliance mandates.

Course Format

• Interactive lectures and discussions.
• Hands-on labs focusing on secure deployment patterns.
• Compliance-oriented case studies and practical exercises.

Course Customization Options

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

This instructor-led, live training in Faro (online or onsite) is aimed at intermediate-level professionals who wish to leverage the power of prompt engineering and few-shot learning to optimize LLM performance for real-world applications.

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

• Understand the principles of prompt engineering and few-shot learning.
• Design effective prompts for various NLP tasks.
• Leverage few-shot techniques to adapt LLMs with minimal data.
• Optimize LLM performance for practical applications.

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.

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.

Vertex AI offers robust tools for constructing multimodal LLM workflows that seamlessly integrate text, audio, and image data into a unified pipeline. By leveraging long context window capabilities and Gemini API parameters, it facilitates advanced applications in planning, reasoning, and cross-modal intelligence.

This instructor-led, live training (available online or onsite) is designed for intermediate to advanced practitioners looking to design, build, and optimize multimodal AI workflows within Vertex AI.

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

• Utilize Gemini models to handle multimodal inputs and outputs.
• Implement long-context workflows for complex reasoning tasks.
• Design pipelines that integrate text, audio, and image analysis.
• Optimize Gemini API parameters to enhance performance and cost efficiency.

Course Format

• Interactive lectures and discussions.
• Hands-on labs focused on multimodal workflows.
• Project-based exercises for practical multimodal use cases.

Course Customization Options

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

Ollama is a platform that enables running and fine-tuning large language and multimodal models locally.

This instructor-led, live training (online or onsite) is aimed at advanced-level ML engineers, AI researchers, and product developers who wish to build and deploy multimodal applications with Ollama.

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

• Set up and run multimodal models with Ollama.
• Integrate text, image, and audio inputs for real-world applications.
• Build document understanding and visual QA systems.
• Develop multimodal agents capable of reasoning across modalities.

Format of the Course

• Interactive lecture and discussion.
• Hands-on practice with real multimodal datasets.
• Live-lab implementation of multimodal pipelines using Ollama.

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.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI developers and localization engineers who wish to design scalable, automated translation pipelines using both proprietary and open-source LLMs.

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

• Design and deploy translation workflows using modern LLM frameworks and APIs.
• Integrate open-source and commercial models into scalable translation systems.
• Optimize translation quality through fine-tuning, prompt engineering, and automation.
• Implement cost-efficient and compliant translation infrastructure for enterprise environments.

This instructor-led, live training in Faro (online or onsite) is aimed at advanced-level professionals who wish to specialise in cutting-edge deep learning techniques for NLU.

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

• Understand the key differences between NLU and NLP models.
• Apply advanced deep learning techniques to NLU tasks.
• Explore deep architectures such as transformers and attention mechanisms.
• Leverage future trends in NLU for building sophisticated AI systems.

This instructor-led live training in Faro, offered online or onsite, targets intermediate to advanced AI developers, architects, and product managers. The course focuses on identifying and mitigating risks associated with LLM-powered applications, including prompt injection, data leakage, and unfiltered outputs. Participants will learn to implement security controls such as input validation, human-in-the-loop oversight, and output guardrails.

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

• Understand the core vulnerabilities of LLM-based systems.
• Apply secure design principles to LLM app architecture.
• Use tools such as Guardrails AI and LangChain for validation, filtering, and safety.
• Integrate techniques like sandboxing, red teaming, and human-in-the-loop review into production-grade pipelines.

LangGraph serves as a graph-based orchestration framework that empowers conditional, multi-step workflows involving large language models (LLMs) and tools, making it highly suitable for automating and personalizing content pipelines.

This instructor-led live training, available either online or onsite, 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 equipped to:

• Design graph-structured workflows for content and email, incorporating conditional logic.
• Integrate LLMs, APIs, and data sources to enable automated personalization.
• Manage state, memory, and context across complex, multi-step campaigns.
• Evaluate, monitor, and optimize workflow performance and delivery outcomes.

Course Format

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

Course Customization Options

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

This instructor-led, live training in Faro (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.

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.

Speech recognition and transcription using AI involves converting spoken language into written text through machine learning models and natural language processing systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to implement, evaluate, and optimize AI-powered speech-to-text solutions for real-world use cases.

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

• Understand how modern speech recognition models are trained and deployed.
• Evaluate open-source and commercial APIs for speech-to-text transcription.
• Handle multilingual and domain-specific transcription challenges.
• Build simple transcription workflows for different audio sources.

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) 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.

LangGraph is a framework designed for constructing 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.

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.

An exploration of artificial intelligence fundamentals demonstrates how intelligent technology transforms digital strategy, automation, and decision-making processes within enterprise operations. The course examines core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside aspects of communication, perception, and autonomous action. It equips executives and architects to evaluate AI-driven transformation opportunities, assess emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This two-stage training programme is tailored for software delivery teams seeking to embed AI-assisted engineering workflows into their daily operations using Claude Code.

Artificial intelligence training covers data analysis, distributed artificial intelligence, and natural language processing. The course aims to provide developers and data scientists with the application technologies needed to build large-scale AI systems. Course content includes distributed machine learning, Apache Spark MLlib, and the core principles of MapReduce-based predictions. It also delves into practical methods for recommendation engines, text classification, semantic analysis using Word2Vec, and RNN-LSTM architectures. This course helps professionals deploy artificial intelligence pipelines suitable for production environments, transforming raw data into automated decision-making capabilities.

This instructor-led live training in Faro (online or onsite) is aimed at intermediate-level engineers and architects who wish to use Tencent Hunyuan to deploy large and MoE models with lower latency, higher throughput, and better cost control.

By the end of this training, participants will be able to: explain Hunyuan production deployment patterns, optimise inference performance, implement batching and quantisation strategies, and plan scalable serving operations.

Natural Language Processing converts raw text into actionable intelligence, allowing machines to understand human communication. The course explores fundamental techniques including corpus analysis, sentence structure parsing, text preprocessing pipelines, and dimensionality reduction using SVD and NMF methods. Participants are guided through practical implementations of document clustering, topic modelling, sentiment analysis, named entity recognition, and Latent Dirichlet Allocation in Python. This equips practitioners with the skills to build intelligent text analytics systems for automated content classification and search.

Ollama is an open-source utility designed to run large language models locally on both consumer and enterprise-grade hardware. By condensing model quantization, GPU resource allocation, and API serving into a single command-line interface, it empowers organisations to host LLMs such as Llama, Mistral, and Qwen independently, without transmitting prompts or sensitive data to OpenAI, Anthropic, or Google.