Natural Language Processing (NLP) Training in Maia

This instructor-led, live training in Maia (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.

Ollama is a platform for running large language and multimodal models locally, supporting governance and responsible AI practices.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to implement fairness, transparency, and accountability in Ollama-powered applications.

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

• Apply responsible AI principles in Ollama deployments.
• Implement content filtering and bias mitigation strategies.
• Design governance workflows for AI alignment and auditability.
• Establish monitoring and reporting frameworks for compliance.

Format of the Course

• Interactive lecture and discussion.
• Hands-on governance workflow design labs.
• Case studies and compliance-focused exercises.

Course Customization Options

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

This instructor-led, live training in Maia (online or onsite) is aimed at intermediate-level AI researchers, machine learning professionals, and data scientists who wish to use LlamaIndex to enhance the capabilities of AI models, making them more accurate and reliable for various applications.

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

• Grasp the principles and components of LlamaIndex.
• Ingest and structure data for use with LLMs.
• Apply context augmentation to improve AI model performance.
• Integrate LlamaIndex into existing AI systems and workflows.

This instructor-led, live training in Maia (online or onsite) is aimed at intermediate-level web developers and UX designers who wish to leverage LangChain to create intuitive and user-friendly web applications.

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

• Understand the fundamental concepts of LangChain and its role in enhancing web user experience.
• Implement LangChain in web apps to create dynamic and responsive interfaces.
• Integrate APIs into web apps to improve interactivity and user engagement.
• Optimize user experience using LangChain’s advanced customization features.
• Analyze user behavior data to fine-tune web app performance and experience.

Mistral AI serves as an open-source AI platform, empowering teams to construct and embed conversational assistants within both enterprise operations and customer-facing processes.

This instructor-led training, available online or onsite, is tailored for beginner to intermediate-level product managers, full-stack developers, and integration engineers looking to design, integrate, and productize conversational assistants using Mistral's connectors and integrations.

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

• Connecting Mistral conversational models with enterprise and SaaS connectors.
• Implementing retrieval-augmented generation (RAG) to ensure grounded responses.
• Creating UX patterns for both internal and external chat assistants.
• Deploying assistants into product workflows to address real-world use cases.

Course Format

• Interactive lectures and discussions.
• Practical integration exercises.
• Live laboratory sessions for developing conversational assistants.

Course Customization Options

• For customized training requirements, please contact us to make arrangements.

Mistral AI is an open and enterprise-ready AI platform that provides features for secure, compliant, and responsible AI deployment.

This instructor-led, live training (online or onsite) is aimed at intermediate-level compliance leads, security architects, and legal/ops stakeholders who wish to implement responsible AI practices with Mistral by leveraging privacy, data residency, and enterprise control mechanisms.

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

• Implement privacy-preserving techniques in Mistral deployments.
• Apply data residency strategies to meet regulatory requirements.
• Set up enterprise-grade controls such as RBAC, SSO, and audit logs.
• Evaluate vendor and deployment options for compliance alignment.

Format of the Course

• Interactive lecture and discussion.
• Compliance-focused case studies and exercises.
• Hands-on implementation of enterprise AI controls.

Course Customization Options

• To request a customized 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.

This instructor-led, live training in Maia (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 in-depth course focuses on diagnosing, testing, and measuring model behaviour when running local or private Ollama deployments.

Delivered as instructor-led live training (available online or onsite), it is designed for advanced AI engineers, ML Ops professionals, and QA practitioners who wish to ensure the reliability, fidelity, and operational readiness of Ollama-based models in production.

Upon completion, participants will be able to:

• Conduct systematic debugging of Ollama-hosted models and reliably reproduce failure modes.
• Design and execute robust evaluation pipelines using 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.

Course Format

• Interactive lectures and discussions.
• Hands-on labs and debugging exercises using Ollama deployments.
• Case studies, group troubleshooting sessions, and automation workshops.

Customisation Options

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

LangGraph facilitates stateful, multi-actor workflows driven by Large Language Models (LLMs), offering precise control over execution paths and state persistence. In the healthcare sector, these capabilities are vital for ensuring compliance, interoperability, and the development of decision-support systems that align with clinical workflows.

This instructor-led, live training (available online or onsite) targets intermediate to advanced professionals looking to design, implement, and manage LangGraph-based healthcare solutions while navigating regulatory, ethical, and operational challenges.

Upon completing this training, participants will be able to:

• Design healthcare-specific LangGraph workflows that prioritise compliance and auditability.
• Integrate LangGraph applications with medical ontologies and standards (such as FHIR, SNOMED CT, and ICD).
• Apply best practices for reliability, traceability, and explainability in sensitive environments.
• Deploy, monitor, and validate LangGraph applications within healthcare production settings.

Format of the Course

• Interactive lectures and discussions.
• Hands-on exercises using real-world case studies.
• Implementation practice in a live-lab environment.

Course Customization Options

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

Mistral is a high-performance family of large language models optimized for cost-effective production deployment at scale.

This instructor-led, live training (online or onsite) is aimed at advanced-level infrastructure engineers, cloud architects, and MLOps leads who wish to design, deploy, and optimize Mistral-based architectures for maximum throughput and minimum cost.

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

• Implement scalable deployment patterns for Mistral Medium 3.
• Apply batching, quantization, and efficient serving strategies.
• Optimize inference costs while maintaining performance.
• Design production-ready serving topologies for enterprise workloads.

Course Format

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

Customization Options

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

Postgres is a sophisticated open-source relational database that serves as a robust foundation for AI-driven systems and data intelligence applications.

This instructor-led live training (available online or onsite) is designed for intermediate-level database professionals and developers who aim to integrate, manage, and optimize AI capabilities directly within Postgres.

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

• Set up and configure Postgres extensions tailored for AI workloads.
• Implement embeddings and perform similarity searches using pgvector.
• Integrate both open-source and proprietary LLMs with Postgres to gain real-time insights.
• Optimize Postgres to efficiently handle AI-driven queries and workflows.

Format of the Course

• Interactive lectures and discussions.
• Extensive exercises and practical 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 Maia (online or onsite) is aimed at advanced-level professionals who wish to master AI-driven NLG techniques for chatbot and virtual assistant applications.

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

• Understand the core principles of NLG for conversational AI.
• Leverage deep learning techniques to enhance chatbot dialogue generation.
• Integrate state-of-the-art NLG models like GPT-3 into chatbot frameworks.
• Apply contextual AI for improving conversational coherence and fluidity.

This instructor-led, live training in Maia (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 Maia (online or on-site) is aimed at intermediate-level data scientists and machine learning engineers who wish to create and apply small language models tailored for specific domains such as legal, medical, and technical fields.

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

• Understand the importance and application of domain-specific language models.
• Curate and preprocess specialized datasets for model training.
• Train and fine-tune language models for domain-specific applications.
• Evaluate and benchmark models using domain-relevant metrics.
• Deploy domain-specific language models in real-world scenarios.

This instructor-led, live training in Maia (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.

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 Maia, 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 Maia (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 course is tailored for individuals keen on extracting meaning from written English, although the concepts can be extended to other human languages.

It covers how to leverage text generated by people, such as blog posts and tweets.

For instance, an analyst can configure an algorithm to draw conclusions automatically from large datasets.

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 in Maia (online or onsite) is aimed at intermediate-level IT leaders, compliance professionals, security teams, and enterprise architects who wish to use sovereign AI principles and governance practices to design AI environments that protect sensitive data, support localization requirements, and reduce vendor lock-in.

By the end of this training, participants will be able to: explain sovereign AI concepts, evaluate hosting and governance options, define controls for prompts and logs, and create a practical adoption roadmap.

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