Welcome to Pextra Academy^™ — your place for open technology education.

This is a comprehensive course designed to take learners from core principles of AI and machine learning to advanced practices in deploying, operating, and governing large language models. Structured into six progressive modules, the course supports both academic exploration and hands-on application across enterprise and research contexts.

Curriculum

A comprehensive foundational course covering core concepts of cloud computing, virtualization technologies, and modern networking principles essential for building and managing scalable cloud environments. Designed for beginners and intermediate learners, this course takes you from fundamentals to real-world cloud architecture.

Curriculum

Appendices

• Linux System Commands Reference
• Major Public Cloud Providers
• Major Private Cloud Platforms

Build end-to-end ML pipelines from data ingestion to production serving. Covers experiment tracking, model registries, feature stores, and production ML system design.

Master bias detection, fairness frameworks, explainability techniques, privacy-preserving ML methods, and build organizational AI governance structures.

Build and administer private cloud environments from scratch. Covers virtualization, software-defined networking, storage, security hardening, and capacity planning.

Master CI/CD pipelines, containerization with Docker & Kubernetes, infrastructure as code, GitOps workflows, and full-stack observability for cloud-native systems.

This course is coming soon. Stay tuned!

What you'll learn in Module 1

• History and evolution of artificial intelligence
• Supervised, unsupervised, and reinforcement learning
• Neural network fundamentals and deep learning
• The path from traditional ML to large language models
• Hands-on lab: Your first ML model with Python

Explore the history and evolution of AI, core machine learning paradigms (supervised, unsupervised, reinforcement), neural network fundamentals, and the trajectory from classical ML to modern large language models.

Includes hands-on labs with Python and popular ML frameworks.

Deep-dive into the transformer architecture, self-attention mechanisms, multi-head attention, positional encoding, tokenization strategies, pre-training objectives, fine-tuning approaches, and prompt engineering techniques.

Learn to deploy LLMs at scale using cloud infrastructure and containerization. Covers model serving frameworks, GPU optimization, auto-scaling, and infrastructure-as-code for ML workloads.

Master production ML operations: performance monitoring, model drift detection, automated alerting, CI/CD pipelines for ML, logging strategies, and operational best practices for LLM systems.

Build frameworks for AI ethics, bias detection and mitigation, fairness metrics, transparency requirements, regulatory compliance (EU AI Act, NIST), and organizational governance structures for LLM systems.

Explore real-world LLM deployments across industries. Covers cost optimization, ROI measurement, vendor evaluation, integration patterns, and building LLM-powered products at enterprise scale.

Covers: Overview, Development Environment, Version Control, and Linux Fundamentals; Computer Networking Fundamentals for Cloud Computing.

Covers: Cloud Computing Foundation; Identity and Access Management in the Cloud.

Covers: Virtualization Technologies and Containerization; Private Cloud Platforms and Architectures; Public Cloud Compute Services and Scaling; Infrastructure as Code and Automation.

Covers: Cloud Storage Systems; Cloud Databases and Data Management; Cloud Networking Architecture and Hybrid Connectivity; Cloud Security Architecture and Governance.

Covers: Managed and Serverless Cloud Services; Monitoring, Logging, and Observability; Streaming and Real-Time Data Processing.

Covers: Cloud Architecture Frameworks and Design Patterns; Disaster Recovery and Business Continuity; Conclusion: From Foundations to Future Growth.