TelcoCloud Training

12/05/2026

One thing that should be appreciated about Rakuten is that it didn’t follow the old single-vendor network path; instead, it built a mobile network using:

- Radios from one vendor
- Software from another
- AI from another
- Cloud from hyperscalers
- All integrated through Kubernetes and open interfaces

Today, Rakuten is often seen as the closest example of a “𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲-𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝘁𝗲𝗹𝗲𝗰𝗼𝗺 𝗼𝗽𝗲𝗿𝗮𝘁𝗼𝗿.”

The main contributors in the Rakuten ecosystem include:

- 𝗜𝗻𝘁𝗲𝗹 → vRAN acceleration - as the “compute engine” powering software-based RAN
- 𝗖𝗶𝘀𝗰𝗼 → networking infrastructure - the “connective tissue” of the cloud-native telecom network
- 𝗡𝗘𝗖 & 𝗙𝘂𝗷𝗶𝘁𝘀𝘂 → Radio Units (RU) - the “wireless front-end” of the network
- 𝗠𝗮𝘃𝗲𝗻𝗶𝗿 → Open RAN software components - “software-defined brain” of Open RAN
- 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 → Edge cloud & AI infrastructure - “digital foundation and intelligence layer” of the network.
- 𝗡𝗩𝗜𝗗𝗜𝗔 → AI acceleration & AI-RAN direction - providing AI infrastructure

This shift is telecom is heading the industry towards a totally new direction where -
-->Networks becoming cloud platforms
--> AI becoming part of the RAN itself
--> Kubernetes becoming the operating system of telecom

Such networks are becoming a blueprint for the AI-native telco model.

28/04/2026

There is one topic in the TelcoCloud and NFV space that has been gaining momentum since the last few months, which is the "𝗽𝘂𝘀𝗵 𝗯𝘆 𝘃𝗲𝗻𝗱𝗼𝗿𝘀 𝗳𝗼𝗿 𝗰𝗹𝗼𝘂𝗱-𝗻𝗮𝘁𝗶𝘃𝗲 𝗼𝗽𝗲𝗻 𝗥𝗔𝗡."

That is the reason behind two recent developments in this area -

1. CNCF momentum around hosted/centralized control planes (𝗸𝟬𝘀, 𝗸𝟬𝗿𝗱𝗲𝗻𝘁)
2. Red Hat OpenShift “𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝘀.”

Both of these products somehow serve the same purpose. I'll explain in simple terms -

- 𝗛𝗼𝘀𝘁𝗲𝗱 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗣𝗹𝗮𝗻𝗲𝘀 - Instead of running a full Kubernetes brain (control plane) on every site, you centralize it and manage many clusters from one place.

Use case: Thousands of edge sites, retail stores, or cell towers.

- 𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝘀 - Think of it as running your apps in a locked room - even the cloud provider can’t see inside. Data stays encrypted while being processed.

Use case: Sensitive telecom data, AI models, financial workloads.

For Telco operators & vendors who want to run Telco + AI workloads together on EDGE sites

Hosted control planes = simpler operations at scale + Enables distributed AI at the edge with centralized control

Confidential clusters = secure multi-vendor environments + Protects models + data (especially proprietary AI)

This is not just a telco story.
It’s the foundation for secure, distributed AI + telecom infrastructure at scale.

And it aligns perfectly with where Open RAN is heading, i.e., cloud-native, multi-vendor, and now secure by design.

31/03/2026

Here is something that many of us don't want to hear, but most of us have already heard this one.

Nokia has announced to cut 𝟮𝟬% 𝗼𝗳 𝗶𝘁𝘀 𝗴𝗹𝗼𝗯𝗮𝗹 𝘄𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲 (~𝟭𝟰,𝟬𝟬𝟬 𝗷𝗼𝗯𝘀), which is a bad signal of where telecom is heading.

- 𝗜𝗻𝗱𝗶𝗮 𝗶𝗺𝗽𝗮𝗰𝘁
Although no set figure is announced for India, with 17,000+ employees, India is likely to see significant cuts across shared and global roles.

- 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴

* Slowing 5G investments and operator spending
* Pressure on margins - net sales fell 15% year-on-year to €393 million.
* Overlapping roles - duplicate job roles created from the 2023 merger of Cloud and Network Services (CNS) with Mobile Networks (MN)
* Industry-wide shift toward lean, software-driven models

- 𝗜𝗺𝗽𝗮𝗰𝘁 𝗼𝗻 𝘁𝗲𝗹𝗲𝗰𝗼𝗺 𝗺𝗮𝗿𝗸𝗲𝘁

* Vendors focusing on fewer, high-value domains
* A temporary surge in the hiring of AI, Platform, and Cloud-native job roles
* Increased competition between Nokia, Ericsson, and new cloud-native players

- 𝗪𝗵𝗶𝗰𝗵 𝗱𝗼𝗺𝗮𝗶𝗻𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗮𝗳𝗳𝗲𝗰𝘁𝗲𝗱 𝗺𝗼𝘀𝘁?

* Legacy hardware & deployment roles
* Managed services & support functions
* Overlapping cloud + network teams
* Non-core business units

Telecom is entering a “𝗹𝗲𝘀𝘀 𝗽𝗲𝗼𝗽𝗹𝗲, 𝗺𝗼𝗿𝗲 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲, 𝗺𝗼𝗿𝗲 𝗔𝗜” phase.

For professionals, the message is clear:
Move closer to 𝗰𝗹𝗼𝘂𝗱, 𝗔𝗜, 𝗮𝗻𝗱 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 or risk being left behind.

26/03/2026

This post is for every telecom professional who has seen '𝗰𝗮𝗯𝗹𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗲 𝗲𝘅𝗰𝗵𝗮𝗻𝗴𝗲 𝗿𝗼𝗼𝗺𝘀 𝗼𝗿 𝗯𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝗿𝗮𝗰𝗸.'

You can't imagine the scale of the problem that NVIDIA has solved with its latest product, NVlink Spine (the red spine the CEO is holding in the picture below)

They just quietly solved a problem most data centers struggle with: too many cables.

Did you know how effective it is? Read below:

- 𝟮 𝗺𝗶𝗹𝗲𝘀 of cables
- 𝟱𝟬𝟬𝟬 𝗰𝗮𝗯𝗹𝗲𝘀 structured
- 𝗔𝗹𝗹 𝗰𝗼𝗮𝘅𝗲𝗱, impedance matched
- Connect all 𝟳𝟮 𝗚𝗣𝗨𝘀 𝘄𝗶𝘁𝗵 𝗲𝗮𝗰𝗵 𝗼𝘁𝗵𝗲𝗿.
- Having a bandwidth of 𝟭𝟯𝟬 𝗧𝗲𝗿𝗮𝗯𝘆𝘁𝗲𝘀 per sec

Just to put in perspective that the peak internet traffic is 112.5 Terabytes per sec only, i.e., handling the traffic of 𝗲𝗻𝘁𝗶𝗿𝗲 𝗰𝗶𝘁𝗶𝗲𝘀’ 𝗶𝗻𝘁𝗲𝗿𝗻𝗲𝘁 𝘂𝘀𝗮𝗴𝗲 𝘄𝗶𝘁𝗵𝗶𝗻 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗔𝗜 𝗰𝗹𝘂𝘀𝘁𝗲𝗿

🔹 What changed?
Traditional AI clusters rely heavily on external networking—tons of cables, switches, and latency. SpineLink creates a fabric inside the GPU layer, drastically reducing physical wiring.

🔹 Efficiency gains
Less cabling → lower power consumption
Fewer switches → reduced latency
Cleaner design → easier scaling

🔹 Compared to traditional setups
Earlier architectures depended on leaf-spine networks with massive cabling overhead. SpineLink compresses that complexity into integrated high-speed interconnects, making clusters faster and simpler.

As AI workloads grow, moving data becomes the bottleneck, not compute. SpineLink shifts the focus from “networking GPUs” to “turning GPUs into the network.”

19/03/2026

It is the perfect time for all 𝗞𝟴𝘀 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝘁𝗼 𝘂𝗽𝘀𝗸𝗶𝗹𝗹 𝗶𝗻 𝘁𝗵𝗲 𝗚𝗣𝗨 𝗲𝗿𝗮.

Kubernetes is no longer just for apps; it’s becoming the control plane for AI infrastructure.

If you’re in cloud or telecom, now is the time to adapt.

For years, we focused on CPU-based workloads: microservices, scaling pods, and keeping clusters stable. But AI workloads are different, and GPUs are changing the game.

My few suggestions for K8s engg upskilling:

🔹 𝗟𝗲𝗮𝗿𝗻 𝗚𝗣𝗨-𝗮𝘄𝗮𝗿𝗲 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 - Understand how GPUs are exposed to pods using device plugins and operators. This is now a core skill, not optional.

🔹 𝗪𝗼𝗿𝗸 𝘄𝗶𝘁𝗵 𝗔𝗜/𝗠𝗟 𝘄𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀 - Get familiar with concepts of model training vs inference, and how they behave differently on clusters (batch vs real-time workloads).

🔹 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗡𝗩𝗜𝗗𝗜𝗔 𝗚𝗣𝗨 𝘀𝘁𝗮𝗰𝗸 - Tools like GPU Operator and CUDA integration simplify deployment, but you need to know how they work in the background

🔹𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴 & 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 - GPU is expensive. Learn efficient scheduling, sharing (MIG), and workload placement strategies.

🔹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗠𝗟𝗢𝗽𝘀 𝗯𝗮𝘀𝗶𝗰𝘀 - Pipelines, model deployment, versioning, this is where K8s meets real AI use cases.

I am pasting the "𝘂𝘀𝗲𝗳𝘂𝗹 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 here". Don't wait, start learning now. Slow & consistent would win the game.

🔹 NVIDIA Developer
https://www.youtube.com/

Search inside channel:

GPU Operator Kubernetes
Kubernetes + CUDA
MIG (Multi-Instance GPU)

🔹 CNCF / Production Use Cases
https://www.youtube.com/

Search inside channel:

Kubernetes GPU scheduling
ML workloads Kubernetes

🔹 Structured Learning (TechWorld with Nana)
https://www.youtube.com/

Search for - Kubernetes + MLops

🔹 The Linux Foundation
https://www.youtube.com/

Search for:

Kubeflow
Cloud-native AI

18/03/2026

NVIDIA just made some big announcements at GTC 2026. Let me tell you how they matter for the telecom industry.

🔹 𝗔𝗜 𝗙𝗮𝗰𝘁𝗼𝗿𝗶𝗲𝘀 𝗘𝘅𝗽𝗮𝗻𝘀𝗶𝗼𝗻 – NVIDIA and partners are rapidly building large-scale AI data centers globally, including for telecom operators. These “AI factories” will power network intelligence and new services.

🔹 𝗦𝗼𝘃𝗲𝗿𝗲𝗶𝗴𝗻 𝗔𝗜 𝗳𝗼𝗿 𝗧𝗲𝗹𝗰𝗼𝘀 – New solutions allow operators to build AI systems within their own country and control, which is important for data privacy, regulation, and national networks.

🔹 𝗔𝗜-𝗥𝗔𝗡 𝗣𝘂𝘀𝗵 – NVIDIA is doubling down on integrating AI directly into RAN, with the motive: base stations that don’t just transmit data, but also run AI apps and optimize themselves in real time.

🔹 𝟲𝗚 𝗩𝗶𝘀𝗶𝗼𝗻: 𝗔𝗜-𝗡𝗮𝘁𝗶𝘃𝗲 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 – Future networks will be software-driven and continuously evolving, with AI embedded across RAN, edge, and core.

🔹 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 & 𝗖𝗹𝗼𝘂𝗱 𝗣𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽𝘀 – Collaboration with cloud providers and telecom vendors to make AI deployment easier and scalable for operators.

In short, this means the following evolutions in the network-

1. RAN is shifting from hardware → software + AI
2. Networks will self-optimize and reduce manual operations
3. Telcos may become “AI service providers,” not just connectivity players

But penetrating telecom is totally a different game.

On what factors do you think they would be adopted by the operators across the world?

17/03/2026

If you're following the developments around agentic AI in Telecom, then this post is for you.

- Microsoft says “agility is money” as agents cut Vodafone B2B cycles from weeks to minutes.
- SK Telecom is equipping every employee to build agents.
- Deutsche Telekom is rolling out guardrails to keep those agents under check

Put together, these are not isolated pilots; they’re the signal that agentic AI is graduating into production-grade network control. Lightweight, purpose-built agents will become the first responders for faults, capacity shifts, and security events, only if telcos treat agents like software products with SLAs, identity, audit trails, and lifecycle pipelines.

Here’s what I think this really means…
- Platform teams must add “AgentOps”: CI/CD for agents, sandboxing, intent verification, and observability. - Cloud architects must design trusted Kubernetes/OpenShift zones at the edge for safe inference and policy enforcement.
- Operators will need new KPIs and runbooks that close the loop between detection and automated remediation.

If you run NetOps, Platform, or Cloud, who owns agent governance in your organization today?

05/03/2026

"GitOps is transforming the way we deliver software, making deployments consistent, auditable, and automated. Curious how? Here’s a quick breakdown:

1. What is GitOps?
GitOps uses Git as the single source of truth to manage and update software and infrastructure, bringing a revolutionary approach to operations.

2. What makes GitOps unique?
- Declarative Configuration: Clearly define your desired state.
- Reconciliation Loop: Automatically ensures actual state matches desired state.
- Pull-Based Model: Enhances security by having agents pull updates.
- Immutable Infrastructure: Changes happen by replacing rather than patching.

3. GitOps Workflow:
From IDE to build, push, test, deploy, monitor, and manage — all the way back to deploy again. This creates a seamless, continuous delivery pipeline.

4. Why is GitOps important?
It guarantees consistency and reproducibility, makes changes auditable and traceable, establishes a single source of truth, and simplifies management complexity.

5. Simplified GitOps Architecture:
Application repo handles build and test, linked with CI and container registry. The configuration repo syncs via Argo CD for continuous delivery to Kubernetes. Deployments are pulled from the container registry by ArgoCD.

Imagine a world where you never have to ask ""Who deployed this?"" or worry about configuration drift ever again.

To master GitOps, start with the basics, embrace automation fully, and turn deployment chaos into smooth harmony.

If this insight was helpful, please like, comment your thoughts, and share to spread the power of GitOps in software delivery.

"

10/11/2025

𝗧𝗲𝗹𝗰𝗼 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀: 𝗔𝗿𝗲 𝘆𝗼𝘂 𝗿𝗲𝗮𝗱𝘆 𝘁𝗼 𝗺𝗮𝗻𝗮𝗴𝗲 𝘁𝗼𝗺𝗼𝗿𝗿𝗼𝘄'𝘀 𝗻𝗲𝘁𝘄𝗼𝗿𝗸?

The shift from "𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 (𝗩𝗡𝗙𝘀)" running on VMs to "𝗖𝗹𝗼𝘂𝗱-𝗡𝗮𝘁𝗶𝘃𝗲 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 (𝗖𝗡𝗙𝘀)" running on containers is the single biggest architectural change defining the future of the network—from 5G core to the RAN. This modern 𝗧𝗲𝗹𝗰𝗼𝗖𝗹𝗼𝘂𝗱 foundation demands a deeper, integrated skillset.

To truly master the deployment and lifecycle management of these modern CNFs, you need to understand both sides of the coin:

𝗢𝗽𝗲𝗻𝗦𝘁𝗮𝗰𝗸: The bedrock IaaS that continues to provide the robust foundation for underlying infrastructure, even for modern VNF management.

𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀: The engine of orchestration, essential for automating the deployment, scaling, and healing of next-generation containerized network functions (CNFs) at unprecedented speed.

𝗬𝗼𝘂𝗿 𝗰𝗮𝗿𝗲𝗲𝗿 𝗴𝗿𝗼𝘄𝘁𝗵 𝗵𝗶𝗻𝗴𝗲𝘀 𝗼𝗻 𝗰𝗹𝗼𝘀𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝘀𝗸𝗶𝗹𝗹 𝗴𝗮𝗽.

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07/10/2025

"𝗗𝗶𝘀𝗰𝗼𝘂𝗻𝘁 𝗰𝗼𝘂𝗽𝗼𝗻 𝗮𝗹𝗲𝗿𝘁"

Many of the Telcos are adopting new technologies, such as the introduction and widespread
adoption of AI.

Actually, this is the impact of the ongoing migration from legacy to cloud-oriented infrastructure, VNFs, CNFs, and cloud-native processes, so that the telcos can handle and manage their data & create services on top of it.

There are still many operators transitioning to the cloud, and this is only possible by using either 𝗢𝗽𝗲𝗻𝗦𝘁𝗮𝗰𝗸 𝗼𝗿 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀, the two virtualization and containerization technologies for managing VMs & container workloads.

You can start your TelcoCloud journey with my bestselling courses on these two topics at a fraction of the actual cost. They include the following:

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