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
£95.00
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
POA: Private Course
Key Topics Covered Foundations of virtualisation in telecom, including the evolution to cloud-native architectures and the role of VMs and containers in 5G core and edge environments Cloud networking essentials, covering virtual networks, segmentation, overlay technologies, secure connectivity, and integration with transport and core networks Containerisation fundamentals, including container lifecycle, workload types, and resource isolation for telecom-grade deployments Kubernetes and container orchestration, including architecture, CNF management, resilience mechanisms, and deployment across core and edge environments Container networking models, including multi-interface support, high-performance data paths, and integration with orchestration and transport systems Learning Ourcomes Understand fundamentals of network & IT virtualisation/Containerization (NFV, CNF) and container orchstration Explain how virtualisation enables scalability, resiliency, and automation in converged core network Identify the role of cloud-native functions in Vodafone’s transition to software-defined networks Describe the operational impact on lifecycle management, orchestration, and assurance Target Audience Core network engineers transitioning to cloud-native architectures Network cloud and platform engineers Technical operations staff supporting Life Cycle Management (LCM) of CNF's and NS's
POA: Private Course
Key Topics Covered Fundamentals of telecom private cloud architecture, including NFVI components, high availability, edge/distributed deployments, and evolution from NFV to cloud-native platforms Design of virtual infrastructure and cloud platforms, covering compute, storage, networking, resource isolation, and data centre fabric integration Private cloud networking and security, including segmentation, tunnelling, service discovery, and protection of core and edge workloads Integration of containers and Kubernetes, supporting CNFs alongside virtual machines with advanced networking and data plane considerations Operational models and lifecycle management, including planning, monitoring, upgrades, disaster recovery, and governance processes Learning Outcomes Architect and operate Vodafone-engineered private cloud platforms using modern SDN & cloud-native tooling Design efficient VPC layouts, routing, service discovery and secure multi‑tier architectures Implement overlay/underlay technologies including VxLAN, GRE and IPsec Optimise VNFC networking using DPDK/SR‑IOV for high‑performance workloads. Course Modules Foundations of Telecom Private Cloud Architecture Virtual Infrastructure & Network Cloud Platform Design Private Cloud Networking & Security Container Integration on Private Cloud Operational Model & Lifecycle Management
POA: Private Course
Key Topics Covered Overview of multi-domain orchestration for coordinating RAN, Core, Transport, and Cloud to enable end-to-end 5G service delivery within Zero Touch Operations frameworks Architecture of orchestration systems, including layered orchestrators, API integration, and interaction with SDN controllers, NFV, and cloud-native platforms Use of information models and intent frameworks, including service templates, standard APIs, and data models for cross-domain orchestration and network slicing End-to-end service orchestration workflows, covering service design, resource allocation, activation, and multi-vendor interoperability Closed-loop automation and assurance integration, enabling telemetry-driven decisions, SLA monitoring, and automated remediation actions Learning Outcomes Understand the architecture and purpose of cross-domain orchestration (RAN/Core/Transport/Cloud) Translate business intents into automated service workflows Apply information models enabling vendor‑agnostic orchestration Explain orchestration’s role in delivering E2E 5G services including slicing and chaining Target Audience Network automation and orchestration engineers OSS/BSS and service orchestration architects Core, RAN, and transport integration engineers Network operations engineers supporting automated service delivery
POA: Private Course
Key Topics Covered Fundamentals of 5G network slicing, including slice types, identifiers, domain separation, and use cases for consumer and enterprise services Slice templates and SLA-driven design, covering performance parameters, KPI mapping, admission control, and policy-based configuration Static and dynamic slicing lifecycle automation, including on-demand instantiation, scaling, modification, and orchestration-driven provisioning End-to-end multi-domain slice coordination, covering RAN, Transport, and Core integration for complete service delivery across heterogeneous networks Slice assurance and closed-loop management, including real-time monitoring, analytics, and automated optimisation to maintain SLA performance Learning Outcomes Design slice templates using SLA‑driven parameters (latency, throughput, availability) Differentiate static vs dynamic slicing and lifecycle automation Integrate assurance, analytics and orchestration for closed-loop slice management Evaluate business use cases for enterprise and consumer slicing Target Audience 5G core and network slicing engineers Enterprise solutions architects Network service design and product teams Automation engineers supporting end-to-end service delivery
POA: Private Course
Key Topics Covered Overview of Zero Touch Operations (ZTO), including its role in 5G/cloud-native networks and evolution toward intent-driven, AI-enabled operations Intent-driven networking and policy automation, translating business intent into orchestrated actions for provisioning, scaling, and optimisation Automation of network operations such as configuration management, self-healing, scaling, service activation, and incident handling Application of AI/ML for predictive analytics, anomaly detection, root cause analysis, and network optimisation Governance, security, and trust frameworks to ensure safe, auditable, and reliable automated operations" Learning Outcomes Identify operational processes suitable for full automation vs human‑in‑the‑loop control Understand policy/intent frameworks enabling hands‑off operations Explain interactions between orchestration, assurance, analytics and AI in zero‑touch operations Recognise governance and trust considerations in automated domains. Target Audience Network operations engineers Automation and orchestration engineers Digital transformation and operations architects Service assurance and network management teams
POA: Private Course
Key Topics Covered Foundations of closed-loop automation, including the Monitor–Analyse–Decide–Act model and its role in improving service reliability, efficiency, and SLA performance across network domains Telemetry and real-time observability, covering data collection from multi-domain sources, streaming analytics, and integration with assurance systems Analytics and decision-making frameworks, including rule-based and AI-driven policy engines for SLA monitoring, predictive analysis, and automated decision triggers Automated orchestration and remediation actions, enabling scaling, rerouting, reconfiguration, and fault recovery across RAN, Core, Transport, and Cloud End-to-end service assurance integration, including KPI monitoring, SLA enforcement, incident automation, and alignment with operational processes Learning Outcomes Apply closed‑loop automation frameworks (monitor → analyse → decide → act) Identify relevant telemetry/KPI sources for real‑time decisions Explain interplay between analytics engines and orchestration layers Understand assisted vs full closed‑loop implementation models." Target Audience Network automation engineers AI/analytics engineers supporting network assurance OSS platform architectsOperations teams responsible for service assurance and performance
POA: Private Course
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
£980.00
