DEV Community: Aashrith D

MLOps Explained: Why Building an ML Model Is Only Half the Job in 2026

Aashrith D — Mon, 29 Jun 2026 11:56:01 +0000

Training a machine learning model is exciting.
Getting that model for production and making it accurate over time and it is where the real engineering begins.
This is the problem MLOps is designed to solve.
MLOps (Machine Learning Operations) applies DevOps principles to machine learning, helping teams automate the entire ML lifecycle from data preparation and model training to deployment, monitoring, retraining, and governance.

Why MLOps Matters

Many machine learning projects perform well in notebooks but struggle in production.

Common challenges include:

Model drift
Data quality issues
Manual deployments
Lack of version control
Inconsistent environments
Difficult collaboration between data scientists and engineering teams

Without a structured workflow, maintaining ML systems becomes increasingly difficult as applications scale.

What MLOps Looks Like

A typical MLOps pipeline includes:

Data collection and validation
Model training
Automated testing
Model versioning
CI/CD for ML pipelines
Production deployment
Continuous monitoring
Automated retraining

Instead of treating machine learning as a one-time project, MLOps treats it as a continuously evolving software system.

Real-World Applications

MLOps powers many AI systems we use every day.
Examples include:

Fraud detection in banking
Recommendation engines in e-commerce
Dynamic pricing platforms
Predictive maintenance in manufacturing
Personalized customer experiences

These systems continuously learn from new data while maintaining reliability and performance.

Best Practices

Successful MLOps implementations usually focus on:

Infrastructure as Code (IaC)
Automated CI/CD pipelines
Feature stores
Model registries
Continuous monitoring
Cloud-native deployment
Strong data governance

These practices reduce deployment time, improve reproducibility, and make ML systems easier to maintain.

Final Thoughts

Building an accurate model is only the first milestone.
The real challenge is operating that model reliably in production.

As organizations continue investing in AI, MLOps is becoming just as important as DevOps is for modern software development. Teams that automate deployment, monitor model performance, and continuously improve their ML pipelines will build AI systems that remain scalable, reliable, and production-ready long after the first model is deployed.

Why Disaster Recovery Starts with Remote Infrastructure, Not Backups in 2026

Aashrith D — Fri, 26 Jun 2026 07:36:00 +0000

Every engineer has heard it before:
"We have backups, so we're covered."
Unfortunately, backups alone don't guarantee business continuity.

A ransomware attack, cloud outage, hardware failure, or even a misconfigured deployment can take production offline. The real question isn't whether your data exists, it's how quickly your systems can recover.
According to ITIC's 2024 report, enterprise downtime can cost organizations up to $300,000 per hour, making recovery time just as important as data protection.

Modern Disaster Recovery Is Built Around Remote Infrastructure

Traditional on-premises disaster recovery often depends on secondary hardware, manual failover, and lengthy recovery procedures.
Remote infrastructure changes that model.
Applications, databases, and workloads are continuously replicated to cloud environments or geographically separate data centers. If the primary environment becomes unavailable, workloads can fail over with minimal disruption
Cloud-based Disaster Recovery as a Service (DRaaS) has made this approach even more practical by automating replication, recovery, and failover while reducing infrastructure costs.

Why Remote Infrastructure Management Matters

Infrastructure doesn't stay healthy on its own.
This is where Remote Infrastructure Management (RIM) becomes essential.
A modern RIM platform typically handles:

24/7 infrastructure monitoring
Automated patch management
Performance monitoring
Backup verification
Incident response
Compliance monitoring Instead of reacting after failures occur, engineering teams can detect issues early and resolve them before they impact production.

Recovery Without Security Doesn't Work

Disaster recovery should never introduce new security risks.
A modern recovery strategy should include:

End-to-end encryption
Multi-Factor Authentication (MFA)
Role-Based Access Control (RBAC)
Immutable or secure backups
Continuous security monitoring Recovery isn't just about restoring systems, it's about restoring them securely.

Final Thoughts

As more organizations adopt hybrid and multi-cloud architectures, disaster recovery is becoming an infrastructure engineering problem rather than simply an IT process.

Remote infrastructure, automated failover, and proactive infrastructure management help teams reduce Recovery Time Objectives (RTOs), improve resilience, and build systems designed to withstand failure, not just recover from it.

Because in modern infrastructure, resilience isn't an afterthought, it's part of the architecture.

Agentic AI vs Generative AI: The Next Evolution of AI Systems

Aashrith D — Thu, 25 Jun 2026 06:05:45 +0000

Generative AI has been the main topic of discussion for the past few years.

We can see that now AI writes code, generates content, summarizes documents, creates images, and assists developers with everyday tasks. A new concept improves alone side, gaining attention: Agentic AI

While generative AI creates, Agentic AI acts.
Understanding the difference is becoming important for developers, architects, and business leaders building AI-powered systems.

Generative AI: built for creation

Generative AI is designed to produce outputs based on prompts.

Examples such as:

Code generation
Content creation
Image generation
Report summarization
Conversational AI

Tools like AI coding assistants and large language models have dramatically improved productivity by helping users create content faster.
Generative AI typically stops after generating answers.
It also provides information that already exists, by modifying it as you need. But it doesn’t execute any actions.

Agentic AI: build for action

Agentic AI introduces a different approach.
Instead of simply responding to prompts, agentive systems can

Define goals
Create plans
Make decisions
Execute actions

Think of it as moving from an AI assistant to an AI operator.
For example, rather than generating a troubleshooting guide, an Agentic AI system could detect an infrastructure issue, investigate logs, create a remediation plan, and trigger corrective actions automatically.

Why This Matters

Organizations are rapidly increasing their AI investments, with 92% planning to boost AI spending over the next three years. As AI adoption matures, companies are looking beyond content generation toward systems that can automate entire workflows.

This is where Agentic AI becomes interesting.

Generative AI Use Cases

Content creation
Code assistance
Documentation
Marketing assets
Knowledge management

Agentic AI Use Cases

IT operations automation
Incident response
Workflow orchestration
Supply chain optimization
Customer service automation

The Future Is Probably Both. Agentic AI isn't replacing Generative AI.

In fact, most next-generation systems combine both.
A Generative AI model creates insights, recommendations, or content, while an Agentic AI layer decides what actions to take and executes them across systems.

Final Thoughts

The shift from Generative AI to Agentic AI represents a move from information generation to autonomous execution.
For developers, this means future AI applications won't just answer questions; they'll increasingly complete tasks, interact with APIs, manage workflows, and operate as intelligent software agents. And that may be the biggest evolution in AI since the rise of large language models.

2026 AI Trends Every Developer Must Know

Aashrith D — Fri, 19 Jun 2026 11:47:46 +0000

For the past few years, AI has dominated every technology conversation.

We've seen chatbots write code, generate content, create images, and automate workflows. But in 2026, the conversation is finally shifting away from hype and toward something more important:

How are organizations actually using AI at scale?

The answer is surprisingly practical.

AI is no longer treated as an experimental technology. It has become part of modern software development, cloud operations, cybersecurity, customer support, and business intelligence workflows.

According to recent industry research, 78% of organizations now use AI in at least one business function, while 92% plan to increase AI investments over the next three years.

"GLOBAL AI ADOPTION AND TRENDS IN 2026"explore the detailed whitepaper for more in-depth insights by Teleglobal

The Real Problem Isn't Adoption

Most companies have already experimented with AI.
The real challenge is operationalizing it.
Many organizations successfully build proof-of-concept projects but struggle when moving them into production environments.
Common obstacles include:

Poor data quality
Security concerns
Compliance requirements
Infrastructure costs
Model monitoring
Governance and accountability

This explains why only 1% of organizations consider themselves fully AI-mature, despite widespread adoption.

AI Is Becoming Part of the Development Stack

For developers, AI is increasingly becoming another layer of the technology stack.
Alongside:

Frontend frameworks
Backend services
Databases
Cloud infrastructure

we now have:

AI copilots
Vector databases
LLM APIs
AI agents
Retrieval-Augmented Generation (RAG)
Model monitoring systems

Building software in 2026 often means understanding how these components interact.

AI Delivers Value When Connected to Business Problems

The organizations seeing the highest returns aren't necessarily using the most advanced models.

They're using AI to solve specific problems.

Research shows that 74% of advanced Generative AI initiatives meet or exceed ROI expectations, particularly when focused on measurable outcomes rather than experimentation.

Examples include:

AI-assisted software development
Fraud detection
Predictive maintenance
Intelligent customer support
Personalized recommendations

Final Thoughts

The state of AI in 2026 is not about whether AI works.It clearly does.

The challenge now is building reliable, secure, and scalable systems around it.

The developers and organizations that learn how to integrate AI into existing workflows—not just experiment with it will be the ones creating the next generation of software.

Cloud Migration Is No Longer Optional for Modern Engineering Teams

Aashrith D — Tue, 09 Jun 2026 10:28:11 +0000

For years, companies relied heavily on on-premise infrastructure.

Physical servers sitting inside office buildings or private data centers powered everything:

applications
databases
backups
internal systems

But in 2026, maintaining that infrastructure is becoming increasingly difficult to justify.

The problem is not just hardware costs anymore.

It is scalability, deployment speed, disaster recovery, security, and the ability to support modern development workflows.

The Real Cost of On-Prem Infrastructure

Most engineering teams already know the pain points:

hardware refresh cycles every few years
expensive maintenance
downtime risks
slow procurement processes
limited scalability
complex disaster recovery planning

Scaling an application often means waiting weeks for new hardware instead of deploying resources instantly.

That slows everything down.

And as workloads grow, infrastructure management starts consuming engineering time that should be spent building products.

Why Cloud Migration Changes the Game

Cloud platforms like:

AWS
Microsoft Azure
Google Cloud Platform (GCP)

Replace fixed infrastructure with scalable, on-demand services.

One of the biggest advantages of cloud migration is scalability.

Instead of manually managing servers, teams can provision:

compute resources
databases
storage
networking
monitoring tools

within minutes.

The biggest advantage is flexibility.

Cloud infrastructure scales dynamically based on demand, which makes handling traffic spikes significantly easier compared to traditional infrastructure.

Cloud Migration Is Also About Developer Velocity

Modern cloud environments support:

CI/CD pipelines
Kubernetes
serverless architectures
Infrastructure as Code (IaC)
automated monitoring
container orchestration

This allows teams to ship faster while reducing operational complexity.

Cloud-native development also makes remote collaboration easier for distributed engineering teams.

Security and Recovery Improve Too

One of the biggest misconceptions is that cloud environments are less secure.

In reality, major cloud providers now offer:

built-in encryption
IAM controls
automated backups
disaster recovery systems
real-time threat monitoring

For many businesses, cloud infrastructure is actually more resilient than aging on-premise systems.

Final Thoughts

Cloud migration is no longer just an infrastructure upgrade.

It is becoming the foundation for:

scalable applications
AI workloads
DevOps automation
distributed systems
modern software delivery

The companies modernizing early are not simply reducing hardware costs.

By leveraging Teleglobal’s cloud expertise and modern cloud platforms, businesses that modernize their infrastructure early will be better prepared for AI adoption, digital transformation, and long-term growth in the rapidly evolving technology landscape.

They are building engineering environments designed for speed, scalability, and long-term innovation.

Data & AI in 2026: Why Developers Need to Understand Both

Aashrith D — Wed, 20 May 2026 08:47:07 +0000

AI is no longer just a research topic or a buzzword. It is now deeply integrated into modern software systems. Every time you see a recommendation engine, fraud detection system, AI chatbot, or personalized dashboard, you are seeing Data and Artificial Intelligence working together.

But here’s something developers often overlook:

AI is only as good as the data behind it.

Every AI system’s basic need is data; without data, it can’t learn any pattern, make predictions, or it can’t even improve, for everything it needs data. Whether it’s Machine learning, natural language processing, or generative AI, the quality of output depends on the quality of the data that is being used for it.
Without quality data, even the most advanced AI models fail to produce reliable results. That is why understanding Data and AI together has become an essential skill for developers in 2026.

The Technologies Powering Modern AI:

Most modern applications use some form of AI technology, including:

Machine Learning (ML)
Deep Learning
Natural Language Processing (NLP)
Computer Vision
Generative AI

These systems process massive amounts of data to identify patterns, automate tasks, and improve decision-making.

For example:

Streaming platforms use recommendation algorithms
Banks use AI for fraud detection
E-commerce platforms personalize user experiences
Logistics companies optimize routes using predictive analytics
AI copilots generate text, code, and documentation The common factor in all these systems is data.

Why Developers Should Care About Data Engineering
Many developers focus heavily on model building while ignoring data quality. In reality, poor data is one of the biggest reasons AI projects fail.

Problems like:

Duplicate records
Missing values
Outdated information
Inconsistent formats
Data silos can break AI systems faster than bad code.

This is why data engineering is becoming just as important as backend development or DevOps.

Modern AI development now involves:

Data pipelines
ETL workflows
Data cleaning
Real-time analytics
Database optimization Understanding these concepts helps developers build more reliable AI-powered applications.

The Role of Cloud Platforms

Cloud computing is another major part of the AI ecosystem.

Platforms like AWS, Microsoft Azure, and Google Cloud Platform (GCP) provide:

Scalable storage
GPU-powered infrastructure
Managed AI services
Serverless computing
Real-time analytics tools Without cloud infrastructure, training and deploying large AI models would be extremely expensive for most teams. This is why AI, Data Engineering, and Cloud Computing are becoming tightly connected skill sets.

Final Thoughts:

In 2026, Data, AI, and cloud computing are getting dependent on each other and getting deeply connected.
Developers who have understood how all these systems work together are building better applications scales faster, and are staying ahead in the evolving tech industry or ecosystem.
The future of software developers is not writing code anymore, it's building intelligent systems powered by DATA.
The real value comes from building systems that can learn, adapt, and make decisions intelligently.

AWS vs Azure vs GCP: Which cloud is the right choice?

Aashrith D — Wed, 20 May 2026 07:04:58 +0000

“Developer-friendly,” every cloud provider claims it.
For developers, it is not about features in AWS, Microsoft Azure, and Google Cloud Platform (GCP); it’s about how quickly a developer can build, scale, debug, deploy, and serve financially while learning cloud technologies.

After going through all three platforms, one thing has become obvious
-The best Cloud platform is not the one that has more offerings or more services. It’s the one that fits your business’s workflow without creating unnecessary complexity.

The Developer perspective:

A few core things most developers see:
• Easy deployments
• Reliable computing
• Managed databases
• Kubernetes support
• CI/CD integrations
• Scalability
• Predictable pricing
• Good documentation

These services might perform similar functions, but each provider’s approach differs based on scalability and pricing.

Why AWS:
AWS has been dominating the cloud market because of its massive ecosystem and flexibility.
A developer can build everything using AWS:
• microservices
• event-driven systems
• scalable APIs
• container platforms
• enterprise-grade infrastructure

Why Azure:
Azure is widely known for its adaptability for enterprise environments, especially where Microsoft technologies already exist.

Developers perspective:
• .NET
• Windows server
• Active Directory
• Microsoft 365
• SQL Server

Asure is preferred for its smoother integrations and easier management.
When it comes to Azure’s hybrid cloud environment, it performs well with businesses that combine on-premise infrastructure with cloud workloads.

Why GCP:
GCP is renowned for its strong AI, Machine learning, analytics, and developer-friendly tools.

Developer perspective:
• Kubernetes
• AI and machine learning
• Analytics
• Cloud-native applications

GCP has one of the strongest managed Kubernetes services, which is also called Google Kubernetes Engine (GKE)
Developers prefer GCP because of its cleaner interface, simpler architecture, and strong developer tooling.

Visit the blog AWS v/s Azure v/s GCP for more in-depth knowledge and a complete guide.

Final Verdict
In 2026, we can see that there will be massive rapid growth in cloud computing, and all three platforms are heavily investing in AI, automation, and cybersecurity. As cloud adoption continues to grow in 2026, businesses that choose the right cloud strategy early will be better prepared for innovation, automation, and long-term digital transformation.
• AWS is best for its scalability and advanced cloud services.
• Azure is best for Microsoft integration and enterprise solutions.
• GCP is best for AI, analytics, and developer-focused projects.
No matter which platform you choose, cloud technology can help businesses become more flexible, secure, and future-ready.
The real skill in cloud engineering is not just deploying infrastructure, it is understanding how cloud providers monetize convenience.

AWS vs Azure vs GCP: Which cloud is the right choice?

Aashrith D — Fri, 15 May 2026 06:37:55 +0000

The Developer perspective:

These services might perform similar functions, but each provider’s approach differs based on scalability and pricing.

Why Azure:
Azure is widely known for its adaptability for enterprise environments, especially where Microsoft technologies already exist.

Developers perspective:
• .NET
• Windows server
• Active Directory
• Microsoft 365
• SQL Server

Why GCP:
GCP is renowned for its strong AI, Machine learning, analytics, and developer-friendly tools.

Developer perspective:
• Kubernetes
• AI and machine learning
• Analytics
• Cloud-native applications

Visit the blog AWS v/s Azure v/s GCP for more in-depth knowledge and a complete guide.

AWS vs Azure vs GCP: Which cloud is the right choice?

Aashrith D — Fri, 15 May 2026 06:37:55 +0000

The Developer perspective:

These services might perform similar functions, but each provider’s approach differs based on scalability and pricing.

Why Azure:
Azure is widely known for its adaptability for enterprise environments, especially where Microsoft technologies already exist.

Developers perspective:
• .NET
• Windows server
• Active Directory
• Microsoft 365
• SQL Server

Why GCP:
GCP is renowned for its strong AI, Machine learning, analytics, and developer-friendly tools.

Developer perspective:
• Kubernetes
• AI and machine learning
• Analytics
• Cloud-native applications

Visit the blog AWS v/s Azure v/s GCP for more in-depth knowledge and a complete guide.