From understanding GPT-4 and Claude to building production AI systems — your complete guide to the AI technology landscape, training paths, and career development.
Understanding the capabilities, strengths, and ideal use cases of today's most powerful AI models
The most capable general-purpose LLMs, excelling at reasoning, coding, creative writing, and multimodal understanding. GPT-4.5 introduces improved emotional intelligence and reduced hallucinations.
Key Strengths
Best For
Known for nuanced understanding, safety-first design, and exceptional long-context performance. Claude excels at careful analysis, coding, and following complex instructions.
Key Strengths
Best For
Google's most advanced AI models with native multimodal capabilities across text, code, images, audio, and video. Deep integration with Google's ecosystem.
Key Strengths
Best For
Meta's open-source LLM family, available for commercial use. Highly customizable and fine-tunable, driving the open-source AI movement forward.
Key Strengths
Best For
European AI lab producing efficient, high-performance models. Mixtral uses a Mixture of Experts architecture for exceptional efficiency without sacrificing quality.
Key Strengths
Best For
Chinese AI lab producing remarkably capable models at a fraction of the training cost. DeepSeek-R1 specialises in chain-of-thought reasoning and mathematical problem-solving.
Key Strengths
Best For
Structured learning paths from AI fundamentals to enterprise architecture, designed for every skill level
4-6 weeks
Start your AI journey with core concepts, terminology, and hands-on experience with leading AI tools.
What You'll Learn:
8-12 weeks
Build practical AI applications using APIs, frameworks, and modern development tools.
What You'll Learn:
12-16 weeks
Master enterprise AI architecture, model training, and organisational AI transformation.
What You'll Learn:
Deep-dive into the essential technologies powering modern AI systems
Understand how machines process, interpret, and generate human language. From tokenisation to transformer architectures.
Learn how AI interprets visual data. From image classification to real-time object detection and generation.
Discover how AI agents learn through interaction. The technology behind game-playing AI, robotics, and autonomous systems.
Master the data pipelines that power AI systems. From collection and cleaning to feature engineering and storage.
Explore the technology behind AI content creation — from text and images to music, video, and 3D models.
Critical training on ensuring AI systems are safe, aligned with human values, and free from harmful biases.
The tools and frameworks every AI practitioner should know
The leading deep learning framework, favoured by researchers and increasingly in production
Google's comprehensive ML platform with strong production deployment tools
The hub for pre-trained models, datasets, and ML tools — essential for modern AI development
Framework for building applications powered by LLMs with chains, agents, and retrieval
Data framework for connecting custom data sources to LLMs for RAG applications
Essential library for classical machine learning algorithms and data preprocessing
Modern Python framework for building high-performance AI API endpoints
MLOps platform for experiment tracking, model versioning, and collaboration
Validate your AI skills with recognised industry certifications
Design, build, and productionise ML models on Google Cloud Platform
Build, train, tune, and deploy ML models using the AWS Cloud
Design and implement AI solutions using Azure AI services
Comprehensive deep learning and AI courses from the world's leading AI educator
Large Language Models are neural networks trained on vast amounts of text data. They learn patterns in language — grammar, facts, reasoning, and even coding — by predicting the next token (word or sub-word) in a sequence. This simple objective, scaled to billions of parameters and trillions of tokens, produces remarkably capable systems.
The Transformer architecture, introduced in 2017, is the foundation of all modern LLMs. Its key innovation — the self-attention mechanism — allows the model to weigh the relevance of every word against every other word in a passage, enabling deep contextual understanding.
Modern LLMs go through multiple training stages: pre-training on large text corpora, supervised fine-tuning on curated examples, and RLHF (Reinforcement Learning from Human Feedback) to align the model with human preferences. This pipeline produces models that are not just knowledgeable, but helpful and safe.
Whether you're exploring AI for the first time or looking to upskill your team, our consulting experts can guide you to the right training path.
Veston Mansaram - Co-Owner/CEO
veston.mansaram@aitoolboard.comGene Da Rocha - Co-Owner/CTO
gene.da-rocha@aitoolboard.com