Enterprise-Scale MLOps Modernization with Kubeflow & MLflow

Executive Snapshot

A large enterprise managing a fast-growing portfolio of machine learning models partnered with Transcloud to modernize and standardize its fragmented, manual, and non-scalable MLOps ecosystem. Their on-prem workflows lacked automation, governance, audit readiness, and the ability to support the increasing number of ML initiatives.
Transcloud designed and implemented a centralized, automated, production-ready MLOps platform using Kubeflow and MLflow running on Kubernetes – enabling governed collaboration, model traceability, parallelized pipelines, and scalable serving for enterprise workloads.

Key Outcomes:

• Centralized platform replacing all manual MLOps activities
  
• Full experiment, model, and dataset traceability with audit-ready lineage
  
• Parallel multi-model execution using Kubeflow Pipelines
  
• Production-ready deployment and autoscaling with KServe
  
• Significant reduction in time-to-production for new models
  
• Enterprise observability with integrated monitoring and metadata tracking
  

The Challenge

The client operated a large and fast-evolving ML environment with multiple teams developing dozens of models every year. However, their existing approach lacked structure, governance, and automation. As their model volume and complexity grew, their manual MLOps processes became a bottleneck—slowing down deployment, risking inconsistency, and leaving no room for scale or auditability.

They needed a unified, enterprise-ready MLOps platform that could bring standardization, automation, and visibility across experiments, models, datasets, and production serving.

Key Challenges:

• No standardized MLOps workflow across teams, resulting in siloed and inconsistent practices
  
• Limited collaboration, with GitLab unable to support secure model sharing or review at scale
  
• No experiment or model tracking, leading to missing metadata, lineage, or reproducibility
  
• Manual versioning, increasing audit risk and operational inconsistency
  
• No data versioning or structured logging, making model rebuilds slow and error-prone
  
• No production-ready serving, lacking autoscaling, monitoring, and standardized deployment workflows
  

The Solution

Transcloud architected and implemented a unified MLOps platform built on Kubeflow, MLflow, and KServe—transforming the client’s ML lifecycle from manual to fully automated and scalable.

Phase 1 — Standardized Development & Experimentation

• Introduced Jupyter Notebooks on Kubernetes for consistent, isolated environments
  
• Centralized experiment tracking in MLflow, capturing parameters, metrics, comparisons, and artifacts
  
• Enforced uniform workflows across all ML teams
  

Phase 2 — Automated Pipelines & Multi-Model Execution

• Implemented Kubeflow Pipelines for parallel, repeatable, multi-model workflows
  
• Standardized pipelines for preprocessing → training → evaluation → deployment
  
• Added team-based grouping to structure collaboration and access
  

Phase 3 — Model Registry & Governance

• Built a centralized model registry supporting versioning, promotion, rollback, and reuse
  
• Established clear lineage between datasets, experiments, and production versions
  
• Enabled governance and auditability across the entire lifecycle
  

Phase 4 — Production Deployment & Observability

• Integrated KServe for autoscaled, production-grade serving
  
• Added Prometheus & Grafana for end-to-end model & infrastructure observability
  
• Adopted MinIO for secure artifact and model storage
  
• Prepared for DVC integration to extend dataset lineage capabilities
  

The Impact

Transcloud’s solution transformed the client’s ML operations from manual and fragmented into a centralized, automated, and enterprise-managed ecosystem. The platform improved speed, governance, and operational efficiency while ensuring full traceability and readiness for high-scale workloads.

Operational & Business Impact

• Full traceability across models, datasets, and experiments
  
• Automated workflows significantly reduced time-to-production
  
• Centralized collaboration eliminated duplication across teams
  
• Improved governance and audit readiness through metadata and lineage
  
• Unified infrastructure empowered teams to innovate and experiment independently
  

Performance & Scalability Gains

• Parallel execution accelerated experimentation cycles
  
• Autoscaling ensured readiness for high-volume inference
  
• Reliable versioning enabled safe rollback, reuse, and iterative improvement
  
• Centralized monitoring improved operational visibility and response time
  
• Standardized deployment patterns reduced long-term maintenance overhead
  

Why Transcloud

The client required a partner capable of combining deep MLOps expertise, enterprise governance, and scalable architecture design. Transcloud brought the technical experience and structured approach needed to modernize and unify their ML operations at scale.

Why the client chose Transcloud:

• Proven expertise in MLOps, Kubeflow, MLflow, and Kubernetes-based ML platforms
  
• Strong governance frameworks for model lineage, metadata, and auditability
  
• Ability to design scalable, production-grade serving with KServe and autoscaling
  
• Experience delivering unified, enterprise-ready ML platforms that enhance collaboration and accelerate deployment