Uniting DevOps + MLOps: India’s Move Toward a Unified Software Supply Chain

level of rigor, security, and automation.

This article explores why Indian organizations are merging DevOps and MLOps, what this unified pipeline looks like, and how it’s reshaping the future of software engineering in one of the world's fastest-growing tech ecosystems.

Why Are DevOps and MLOps Merging?

Historically, DevOps focused on software release cycles—CI/CD, infrastructure-as-code, and rapid deployments. On the other hand, MLOps addressed the unique challenges of machine learning workflows—such as model training, versioning, drift detection, and retraining.

But as AI features become embedded in everyday applications, the separation between app code and ML models is becoming inefficient. Indian tech teams are recognizing the need for:

• Unified workflows: Combining ML model release with app deployment avoids delays.
• Faster experimentation: Developers and data scientists collaborate in real time.
• Improved governance: Security and compliance apply to both code and models.
• Scalability: A single, scalable pipeline for everything from microservices to AI. 

Simply put, a fragmented toolchain can't support modern AI-powered products. Integration is the way forward.

What Does a Unified DevOps + MLOps Pipeline Look Like?

Forward-looking Indian startups and enterprises are building hybrid pipelines that manage both code and models in a single lifecycle. Here’s how it works:

1. Shared Repositories
Both code and ML artifacts (models, datasets, notebooks) are versioned using tools like Git and DVC, enabling reproducibility and traceability.

2. Joint CI/CD Pipelines
Automated pipelines now handle:
Code builds (via Jenkins, GitLab CI, or GitHub Actions)
Model training (triggered on model/data changes)
Containerization and deployment (via Docker, Kubernetes)

3. Security + Compliance
Security scans cover:
Infrastructure-as-code
Model inputs and outputs
Data lineage and bias checks
Teams implement policy-as-code to enforce organizational rules.

4. Monitoring & Drift Detection
Tools like Evidently AI and Fiddler AI monitor model performance in production and detect drift or decay. This feeds into automated retraining pipelines.

5. Reproducibility
Every step—from data ingestion to model training to API deployment—is tracked for reproducibility. Frameworks like MLflow and Kubeflow Pipelines play a major role.

Real-World Use Cases in India

Several Indian tech firms are already embracing this unified approach:

🔹 Razorpay
Uses a single CI/CD framework for both microservices and fraud detection models. Feature engineering, training, and deployment are tied into the same release cycles.

🔹 CureBay
A healthtech startup that integrates model pipelines with clinical app deployment, ensuring rapid iteration while complying with health data regulations.

🔹 Tredence
This analytics unicorn uses a multi-tenant MLOps + DevOps pipeline that supports hundreds of projects across BFSI, retail, and manufacturing—delivering both dashboards and models from the same stack.

Future Outlook: One Team, One Chain

The convergence of DevOps and MLOps is leading to the rise of “AI-first software teams”—cross-functional units where developers, ML engineers, and DevOps professionals collaborate in one value stream.
 

Key predictions for India’s tech ecosystem:

• By 2026, over 70% of AI-powered products in India will follow unified delivery pipelines.
• DevSecMLOps will become a mainstream practice, blending model governance with cybersecurity.
• Tooling consolidation will continue, with platforms offering both ML and software CI/CD features (e.g., Vertex AI, Azure ML, Amazon SageMaker Pipelines).
• Startups will lead innovation, but large enterprises will follow as AI integration scales across domains like BFSI, e-commerce, healthcare, and logistics.

Final Thoughts

India’s tech industry is at a pivotal point. As AI becomes deeply embedded into applications, the days of treating ML and software as separate domains are ending. A unified DevOps + MLOps pipeline doesn’t just improve speed and security—it aligns the entire software supply chain under one strategic umbrella.

For CTOs, architects, and engineering leaders, now is the time to reimagine how your teams build, test, secure, and deploy both code and models. One team. One pipeline. One mission. 
If your team is exploring DevOps and MLOps, connect with UBS Forums #UBSVERSE DevCom Community to share experiences, access toolkits, and stay ahead of the curve.

Sources

• McKinsey & NASSCOM – “AI Adoption and MLOps in India, 2024”
• Razorpay Engineering Blog
• Tredence – ML Engineering Case Studies
• DevOps.com – “Why MLOps Needs DevSecOps Principles”
• Fiddler AI – “Monitoring ML Models at Scale”
• Kubeflow and MLflow Documentation

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