The Artificial Intelligence Fundamentals course is designed to provide participants with a comprehensive introduction to the field of artificial intelligence (AI). Through a combination of theoretical concepts and practical applications, participants will gain a solid foundation in AI and its various subfields, including machine learning, natural language processing, computer vision, and robotics. We explore the use of AI in industry, and in particular in Telecoms and related sectors. By the end of the course, participants will have a strong grasp of AI concepts, techniques, and their real-world applications – particularly in telecoms. They will be equipped to pursue further studies in AI or apply their knowledge to diverse domains where AI plays a significant role. Prerequisites There are no specific prerequisites for this course. However, a basic understanding of programming concepts would be beneficial. Course Contents                             Introduction to Artificial Intelligence Machine Learning Natural Language Processing (NLP) Computer Vision Robotics and Autonomous Systems AI in Industry Ethics and Responsible AI

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The Artificial Intelligence Fundamentals course is designed to provide participants with a comprehensive introduction to the field of artificial intelligence (AI). Through a combination of theoretical concepts and practical applications, participants will gain a solid foundation in AI and its various subfields, including machine learning, natural language processing, computer vision, and robotics. We explore the use of AI in industry, and in particular in Telecoms and related sectors. By the end of the course, participants will have a strong grasp of AI concepts, techniques, and their real-world applications – particularly in telecoms. They will be equipped to pursue further studies in AI or apply their knowledge to diverse domains where AI plays a significant role. Prerequisites There are no specific prerequisites for this course. However, a basic understanding of programming concepts would be beneficial. Course Contents                             Introduction to Artificial Intelligence Machine Learning Natural Language Processing (NLP) Computer Vision Robotics and Autonomous Systems AI in Industry Ethics and Responsible AI

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This course introduces cloud computing and containerisation for telecommunications professionals. It explains the main cloud service and deployment models, the relationship between compute, storage and networking, and the importance of scalability, automation and availability in telecoms cloud environments. Participants explore how containers differ from virtual machines and examine the roles of container images, runtimes and registries. The course introduces LXC/LXD, Docker and Kubernetes, explaining how these technologies support application deployment, isolation and orchestration. The course relates these concepts to the evolution from Virtual Network Functions (VNFs) to Cloud Native Network Functions (CNFs), including the roles of NFV infrastructure and management and orchestration. Guided practical activities introduce container platforms, networking, Docker and a lightweight Kubernetes environment. Course Modules Cloud Computing Concepts for Telecom Engineers Introduction to Containers Introduction to Docker Kubernetes Basics Cloud Native in Telecom Prerequisites Participants should have a basic understanding of telecommunications networks and IP networking. Familiarity with mobile core network functions will be helpful when considering the telecoms examples. No prior experience of cloud platforms, Docker or Kubernetes is required. Basic familiarity with Linux and command-line operations will be beneficial for the practical activities. Who the Course Is For This course is intended for telecoms engineers, core network engineers, technical support and operations specialists, and infrastructure professionals who need an introduction to cloud computing and containerisation. It is particularly relevant to those moving from traditional telecommunications platforms towards virtualised and cloud-native environments, or working alongside teams deploying and supporting containerised network functions. Learning Objectives By the end of the course, participants will be able to: Describe cloud service and deployment models used in telecommunications. Explain core cloud concepts, including compute, storage, networking, scalability and automation. Distinguish containers from virtual machines and explain containerisation benefits. Describe the roles of container images, runtimes and registries. Explain the purpose and architecture of Docker and LXC/LXD. Describe how namespaces and cgroups provide isolation and resource control. Explain Kubernetes architecture and the roles of control plane and worker nodes. Distinguish Virtual Network Functions (VNFs) from Cloud-native Network Functions (CNFs). Describe the roles of NFV infrastructure and MANO in telecoms environments. Apply container deployment, networking and resilience concepts in practical lab scenarios.

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This course explores the principles, architectures and technologies that underpin private cloud networking, with a particular focus on telecommunications environments. It explains how private cloud differs from traditional data centres and public cloud, and how virtual networks provide connectivity, isolation and resilience for cloud-hosted workloads. Participants examine Virtual Private Cloud (VPC) design, including addressing, subnetting, routing, security controls and connectivity between networks. The course then explores the underlying networking technologies, including VXLAN, Geneve and BGP EVPN, alongside IPsec VPNs for secure connectivity. Design considerations and common troubleshooting challenges help connect these technologies to their operational use. Building on these foundations, the course considers the requirements of telecoms workloads across central, regional and edge cloud deployments. Topics include the GSMA NG.126 Cloud Infrastructure Reference Model, network separation, performance, security and resilience. Practical laboratory exercises complement the taught content, helping participants develop their understanding of private cloud networking and its application in telecoms. Course Modules What is Private Cloud? Virtual Private Cloud VPC Networking Technologies and Protocols Private Cloud Networking in Telecom Prerequisites Participants should have a working understanding of IP networking, including IPv4 addressing and subnetting, switching, routing and basic network security. Familiarity with VLANs, NAT and general telecommunications network concepts will be beneficial. An awareness of virtualisation and cloud computing is helpful, but prior experience of designing or operating private cloud networks is not required. Who the Course Is For This course is intended for network engineers, telecoms engineers, cloud and infrastructure engineers, technical architects, and operations or support specialists involved in planning, deploying or maintaining private cloud connectivity. It is particularly relevant to technical professionals moving from traditional IP or telecoms networks into cloud infrastructure roles, and those who need to understand how virtual networking supports telecoms workloads. Learning Objectives By the end of the course, participants will be able to: Explain private cloud principles and how they differ from public cloud and traditional data centres. Describe VPC components for connectivity, routing, isolation and security. Design a VPC using addressing, subnetting, routing and high availability principles. Compare VPC interconnection methods, including peering, transit and shared services. Explain underlay and overlay networking, including VXLAN and Geneve. Describe how BGP EVPN supports VXLAN-based networks. Explain how IPsec and IKEv2 provide secure connectivity. Identify common VPC, overlay and VPN troubleshooting considerations. Describe the GSMA NG.126 Cloud Infrastructure Reference Model. Explain how performance, security and resilience requirements shape telecoms cloud deployments.

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This one-day course explores how zero-touch operations can transform the management of 5G and cloud-native telecommunications networks. It examines the progression from manual processes to automated, intent-driven operations, with a focus on improving efficiency, reducing recovery times and ensuring operational consistency. Participants explore how business and operational requirements are translated into policies and technical actions through orchestration and closed-loop control. Use cases include automated configuration management, service activation, self-healing, scaling and incident handling across RAN, core, transport and cloud environments. The course also considers how AI and analytics support prediction, root cause analysis and proactive assurance. Throughout, emphasis is placed on the governance, verification and safety controls needed to introduce automation responsibly, retain appropriate human oversight and align with existing operational processes. Course Modules Introduction to Zero Touch Operations (ZTO) Intent Driven Operations & Policy Automation Automating Operational Processes Across Network Domains AI Assisted Digital Operations Operational Governance, Safety & Trust Controls Prerequisites Participants should have a working understanding of telecommunications networks and basic familiarity with 5G and cloud computing concepts. Experience of network operations, service assurance, incident management or change management will be beneficial. No prior experience of AI, machine learning or programming is required. Who the Course Is For This course is intended for network operations and service assurance engineers, NOC specialists, technical architects, automation and orchestration professionals, and operational team leaders involved in improving telecommunications service delivery. It is particularly relevant to those responsible for provisioning, configuration, incident resolution, performance optimisation or operational governance in 5G and cloud-native networks. Learning Outcomes Explain the evolution from manual operations to automated and intent-driven network management. Describe the roles of orchestration, assurance, closed-loop control and AI in network operations. Explain how intents are translated into policies and automated actions.Identify automation opportunities for provisioning, scaling, healing and service management. Describe automated remediation workflows, from detection to verification.Explain how automated ticketing and closed-loop processes improve operational efficiency. Describe how AI and analytics support prediction, anomaly detection and root cause analysis.Explain the role of NWDAF and analytics platforms in digital operations. Distinguish between human-led and autonomous operational tasks.Identify key governance, safety and control mechanisms for automation. Explain how zero-touch operations integrate with NOC/SOC and change management processes.

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This one-day course explores how artificial intelligence and machine learning can enhance network automation in 5G and cloud-native telecommunications environments. It explains the progression from configuration automation to orchestration, assurance and autonomy, and distinguishes rule-based automation from AI-assisted approaches. Participants examine practical applications across RAN, core, transport and cloud domains, including load prediction, anomaly detection, path optimisation and automated scaling. The course introduces the machine learning concepts, data requirements and operational constraints that underpin these applications. The course also explores how AI supports closed-loop and intent-based automation, including interaction with orchestration and assurance systems. Participants consider telemetry, model deployment and integration with existing operational systems, alongside the controls needed to ensure reliable and safe automated actions. Course Contents Foundations of Network Automation in 5G/Cloud Environments AI & ML Concepts for Telecom Networks AI-Enhanced Automation Use Cases Across Network Domains Integrating AI into Closed Loop and Intent-Based Automation Data, Telemetry & Model Deployment Architecture Who the Course Is For This course is intended for network engineers, technical architects, network operations and assurance specialists, and professionals involved in network automation, orchestration or operational support systems. It is particularly relevant to those working across RAN, core, transport and cloud domains who need to understand how AI and machine learning can improve network operations and support the progression towards autonomous networks. Learning Objectives By the end of the course, participants will be able to: Explain the evolution from automation to orchestration, assurance and autonomous networks. Differentiate rule-based and AI-assisted automation and their appropriate use cases. Describe supervised, unsupervised and reinforcement learning in telecoms. Explain the machine learning lifecycle from data preparation to deployment and retraining. Identify AI-driven automation opportunities across RAN, core, transport, cloud and enterprise networks. Explain predictive and reactive closed-loop automation. Describe how intent is translated into policies and automated network actions. Identify the telemetry, metrics, logs and traces required for network AI. Compare edge and central cloud AI deployment models. Explain key considerations for AI governance, accuracy and OSS integration.  

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Artificial intelligence (AI) is revolutionizing the telecoms industry and driving connected innovation to new heights. This comprehensive course delves into the role of AI in telecoms and explores its various applications, from machine learning to natural language processing, computer vision, and robotics. Participants will acquire a solid understanding of how AI is reshaping telecoms networks to be more efficient and sustainable, as well as its integration into evolving architectures to support cutting-edge use cases like the Metaverse, Intelligent Transportation, and Autonomous Driving. Moreover, the course highlights the synergy between AI and the Internet of Things (IoT), forming the powerful concept of AIoT (Artificial Intelligence of Things). Participants will discover how AI is catalyzing innovation across industries, enabling smarter cities, more efficient manufacturing processes, and personalized healthcare solutions. By the end of the program, participants will be well-versed in AI concepts and techniques, ready to apply their knowledge in telecoms and beyond. Whether pursuing further studies in AI or leveraging their expertise in diverse domains, participants will be equipped to navigate the exciting intersection of AI and telecoms innovation. In a rapidly evolving digital landscape, understanding the fundamentals of AI is essential for professionals in the telecoms industry. This course not only equips participants with practical skills in AI but also instills a deep appreciation for the ethical considerations and responsible implementation of AI technologies. With a focus on real-world applications and industry relevance, this course empowers participants to leverage AI to drive innovation, enhance efficiency, and unlock new possibilities in the dynamic realm of telecoms and connected innovation. Course Contents                             Introduction to Artificial Intelligence Machine Learning Applying Machine Learning to AI Natural Language Processing (NLP) Computer Vision Robotics and Autonomous Systems AI in Industry AIoT (Artificial Intelligence of Things) Ethics and Responsible AI

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