Course Outline
Telecom Automation Landscape and Maturity
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Why telecom automation differs from isolated task scripting.
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Network, service, customer, and business layers.
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Automation use cases across RAN, core, transport, telco cloud, and OSS/BSS.
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Task automation, workflow automation, orchestration, closed-loop automation, and autonomy.
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Autonomous-network maturity from manual operations to full autonomy.
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AIOps, intent-based operations, and zero-touch concepts.
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Exercise: Assessing the current automation maturity of a telecom environment.
Automation Architecture and Safe Design Principles
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Sources of truth, inventory, topology, policy, and configuration state.
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Controllers, orchestrators, adapters, APIs, event buses, and workflow engines.
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Declarative versus imperative automation.
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Idempotency, repeatability, state management, and dependency handling.
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Human-in-the-loop and human-on-the-loop controls.
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Approval gates, maintenance windows, blast-radius control, and rollback.
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Exercise: Designing a safe end-to-end automation workflow.
Python and API Foundations for Telecom Automation
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Python structures and functions used in automation.
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Working with JSON, YAML, CSV, and XML.
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Consuming REST APIs and handling authentication.
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Timeouts, retries, pagination, validation, and error handling.
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Logging and producing auditable execution results.
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Git-based version control and peer review.
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Hands-on lab: Retrieving network or service data through a simulated API.
Model-Driven Network Automation
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Limitations of screen scraping and command-line automation.
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NETCONF concepts and configuration datastores.
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RESTCONF and API-based network management.
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YANG models for configuration, operational state, RPCs, and notifications.
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Capabilities, schema discovery, and vendor-neutral models.
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Transactions, candidate configuration, validation, commit, and rollback.
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Hands-on lab: Reading state and applying a validated change through a model-driven interface or simulator.
Configuration and Compliance Automation
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Device and service inventory.
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Templates, variables, and reusable automation roles.
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Python- and Ansible-based configuration workflows.
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Pre-change and post-change validation.
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Configuration backup, drift detection, and compliance checking.
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Multi-vendor abstraction and exception handling.
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Secrets management and least-privilege access.
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Hands-on lab: Automating a configuration change with validation and rollback.
Telemetry, Events, and Operational Observability
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Polling versus event-driven and streaming approaches.
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Collecting KPIs, alarms, events, logs, traces, and configuration changes.
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SNMP, streaming telemetry, message queues, and webhooks at a conceptual level.
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Normalizing timestamps, identifiers, severities, and topology context.
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Event deduplication, suppression, enrichment, and service-impact mapping.
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Dashboards, alerting, and automation triggers.
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Hands-on lab: Processing and enriching a stream of representative telecom events.
AIOps for Telecom Operations
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Baselines, thresholds, statistical rules, and machine-learning models.
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Time-series anomaly detection.
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Event correlation and alarm-noise reduction.
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AI-assisted root-cause and impact analysis.
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Incident classification, prioritization, and routing.
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Predictive service assurance and capacity alerts.
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Measuring precision, recall, false positives, detection time, and operational value.
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Hands-on lab: Detecting anomalies and producing a prioritized incident recommendation.
Service and Telco-Cloud Orchestration
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SDN, NFV, virtual network functions, and cloud-native network functions.
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Lifecycle management and service orchestration.
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ETSI NFV-MANO concepts.
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Integrating controllers, orchestrators, inventory, assurance, and ticketing.
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TM Forum Open API and Open Digital Architecture concepts.
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Service activation, scaling, healing, and termination workflows.
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Automation across hybrid and multi-domain environments.
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Exercise: Mapping an end-to-end service lifecycle to automation components.
Closed-Loop and Intent-Driven Automation
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Observe-orient-decide-act and detect-analyze-decide-act loops.
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Defining objectives, policies, constraints, and intent.
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Trigger, analysis, decision, execution, validation, and learning stages.
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Closed-loop anomaly detection and resolution.
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Selecting deterministic rules versus AI/ML decisions.
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Confidence thresholds, approval gates, safe actions, and rollback.
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Preventing unstable loops and conflicting automations.
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Hands-on lab: Designing or implementing a guarded self-healing workflow.
Generative AI and Operational Assistants
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Telecom use cases for large language models and generative AI.
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Summarizing alarms, incidents, tickets, and change records.
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Retrieving knowledge from runbooks and technical documentation.
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Generating draft diagnostics, queries, test cases, and remediation plans.
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Tool-using assistants and agentic workflow concepts.
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Hallucination, prompt injection, data exposure, and excessive agency.
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Requiring evidence, approval, audit trails, and bounded permissions.
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Exercise: Designing a safe NOC assistant workflow.
Testing, Security, Governance, and Scaling
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Unit, integration, simulation, and pre-production testing.
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Digital twins and lab validation.
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CI/CD and GitOps concepts for network automation.
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Canary changes, staged deployment, rollback, and disaster recovery.
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Identity, access, secrets, API security, and audit logging.
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Ownership, support model, change management, and automation lifecycle.
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Operational KPIs:
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Provisioning time.
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Change failure rate.
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Mean time to detect and restore.
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Alarm reduction.
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Automation success and rollback rates.
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Capstone: Creating a phased AI and automation roadmap for a telecom use case.
Testimonials (1)
real life examples
