Tetrate

DevOps Engineer

Kube Natives

weekly digest for all things Cloud Native! Tailored for both beginners and advanced users, our focus is on delivering insightful and engaging content centered around Kubernetes. Stay ahead in the rapidly evolving world of cloud-native technologies with our expertly curated articles, tutorials, and industry updates.

Founder

DEVOPS BEAST

Devopsbeast specializes in guiding companies through adopting and integrating Kubernetes and its vast ecosystem.

Our mission is to empower your business with cutting-edge container orchestration, enabling you to scale seamlessly, enhance reliability, and accelerate deployment cycles.

We understand that the shift to Kubernetes can be complex, so our team of seasoned experts is committed to providing technical leadership every step of the way.

From crafting bespoke Kubernetes solutions to optimizing your existing infrastructure, DevOpsBeast ensures your transition is smooth, efficient, and tailored to your unique business requirements.

Our consulting services extend beyond implementation to encompass comprehensive training, support, and strategic insight, ensuring your team adopts Kubernetes and thrives using it.

DevOps Consultant

Syntax

Python Engineer - Devops 

Allianz

Design and Implement Helm charts for the deployment. ( Kafka, Ml-flow,Elasticsearch etc). 
Deploy and Manage Data science-related services in Azure AKS
Prepare in-depth technical Documentation for the team.
Roll-out releases to Customer environment using Argo CD/ Jenkins Multi-branch pipeline. 
Deploy cloud infrastructure services/changes using Terraform.
Containerize applications using Docker adhering to best practices.
Attend L2 incidents in the customer environment and resolve them timely manner.

Accomplishments
Developed Luigi Workflow that can work with spark and Kubernetes
Developed Grafana chart from scratch for the ECK (Elastic cloud on Kubernetes) Operator

Persistent Systems

Design, implement and maintain all AWS infrastructure and services within a managed service environment.
Deploy and maintain enterprise-class security, network and systems management applications within an AWS environment.
Create a lift and shift process model clearly defining the individual steps of the lift and shift process.
Provide process and quality improvements through task automation, Institute infrastructure as code, security automation and automation or routine maintenance tasks.
Manage solution design for client opportunities in one or more AWS Competencies or general cloud managed services.

Engineering Lead

DigitalX (Pvt) Ltd  

Designed and defined the architecture for new cloud computing systems or existing systems migrating to cloud architectures.
Created, maintained and shared technical documentation used by engineers and other team members.
Collected, researched, and analyzed the operation team's requirements and design and develop applications to solve issues.
Provides hands-on technical design and implementation to automate application build and deployment solutions. Maintained and deployed infrastructure using infrastructure as Code
(IAC) to streamline infrastructure through code.

Duo Software

Develop Python/Go programs to automate repetitive tasks
Working with Developers and Solutions Architect teams incorporating automated build, testing, and reporting processes following an Agile Software Development Lifecycle
Automate, optimize and drive efficiency of effort, code, and process (Pipeline automation)
Developing and maintaining service documentation such as architecture diagrams, roadmaps, and utilization reports
Work closely with development teams to help design, architect, configure, monitor, and support their services and infrastructure.

Omobio (Pvt) Ltd.

Omobio (Pvt) Ltd was established in February 2011 by a team of Professional Telecom Software Developers, tendering its service to Telecom Operators and Telecom Service Aggregators..
Deploy containerized applications to Kubernetes using  Helm Charts and Argo CD.
Containerize applications using Docker
Build CI/CD pipelines using Jenkins
Worked with Operations and Engineering teams to build and maintain templated infrastructure and application stacks deployed on bare metal servers.
Actively participated in User acceptance testing and meetings or training with clients to ensure a smooth transition.

Analyzed and suggested the implementation of an infrastructure architecture for different types of projects.

Engineer

A results-driven Senior DevOps Consultant with over seven years of industry experience, expert in leveraging cloud-native technologies, and distinct Kubernetes expertise. Accomplished in managing and executing DevOps, SRE, and data-centric projects across the AWS cloud. Command a deep proficiency with Terraform for intricate cloud infrastructure buildouts and data-driven solutions.

Key competencies include:

Kubernetes, Terraform, Python, Go, Github actions, ArgoCD.

Certifications: Holds AWS (x5), Kubernetes CKA & CKAD, Terraform, and other key industry certifications, reflecting the commitment to industry evolution.

As DevOps engineers, understanding the fundamentals of scaling is crucial. There’s no one-size-fits-all solution — every scaling challenge requires a different approach. 

The key is to identify the problem accurately and select the appropriate scaling strategy for the use case.

In this article, I break down the basics of scaling and explain how to approach it effectively.

Sharon Sahadevan

Kubernetes & MLOps Architect | Scaling LLMs and AI Workloads on GPU and Cloud Infrastructure | Kubenatives

Introduction As your application grows, so does the demand on your infrastructure. Whether you’re experiencing increased traffic, larger datasets, or 

Open article: Scaling Patterns: Your Guide to Vertical, Horizontal & Functional Sharding by Sharon Sahadevan • 3 min read

Scaling Patterns: Your Guide to Vertical, Horizontal & Functional Sharding

I am not a financial expert but I feel like history is repeating itself and very soon AI bubble may burst.

I teach Data Engineering and create data & AI content | 10+ years of experience | 3x LinkedIn Top Voice | 230k+ YouTube subscribers

AI Hype a Classic Bubble Like 2008 & 2000!
I've been doing some research this morning and it's scary:

✅ Investor overconfidence → “AI will grow forever”, “AI will replace jobs and whole processes.”
✅ Regulatory lag → Scaling fast while rules haven’t caught up.
✅ Innovation not fully understood → People assume magic, ignoring typical problems like cost, reliability, and integration.
✅ Excess capital & Massive investments → $100B Nvidia deal, massive VC bets.

💥 Parallels to dot-com bubble: the same ingredients.
Then: “Every company needs a website → infinite growth.”
Now: “Every company needs AI → infinite growth.”

Then: Cisco, Intel sold hardware to dot-com startups.
Now: Nvidia sells GPUs to OpenAI/Microsoft.

Then: stock valuations soared on assumptions of endless demand.
Now: Nvidia stock up massively, valuations sky-high.

Then: collapse when revenue didn’t match hype.
Risk: same thing if AI monetization is slower than infra spending.

Just leaving this here for future reference 😬

No BS AI/ML Content | ML Engineer with a Plot Twist 🥷50M+ Views 📝

Microsoft just killed the GPU mafia. 💀

100B parameter models. On your laptop CPU. Today.

I'm not kidding. 🤯

BitNet.cpp just dropped, and it's about to flip everything you know about local inference.

Here's what's actually insane:

1️⃣ The Speed Demon
⚡ 6x faster inference than anything else
⚡ On a regular x86 CPU
⚡ No CUDA. No TPUs. Just your Intel chip sweating bullets.

2️⃣ The Energy Vampire Slayer
82% less power consumption 🔋
⇨ Your laptop battery just sent a thank you note
⇨ Data centers crying tears of joy

3️⃣ The Memory Magic
Ternary weights: -1, 0, +1
That's it. Three values.
⇨ 100B parameters suddenly fit where 10B struggled 🧙‍♂️

But here's where it gets spicy...

They didn't just release the framework.

They dropped BitNet b1.58 2B4T—a fully functional model using 1.58 bits per weight.

1.58 bits.

And it still crushes benchmarks. 📊

Think about what this means:
• Your MacBook Air running Claude-sized models
• Edge devices becoming AI powerhouses
• Zero cloud dependency for serious inference

The GPU industrial complex is shaking right now.

Who else is tired of paying $40K for H100s when CPUs could do the job? 🙋‍♂️

What are you building with this? Drop your wildest ideas below 👇

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