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Webority Logo
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End-to-End MLOps & LLMOps Services

We transform your machine learning models from experimental projects into production-ready solutions. Our MLOps and LLMOps services streamline model deployment, optimize AI infrastructure, and deliver real-time performance monitoring—helping you achieve faster time-to-market and measurable business outcomes.

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Mobile App Development

What Are MLOps & LLMOps?

MLOps (Machine Learning Operations) brings DevOps principles to machine learning—automating the entire ML lifecycle from development to deployment and monitoring. LLMOps extends these practices specifically for Large Language Models, addressing unique challenges like prompt management, fine-tuning, and cost optimization.

At Webority, we bridge the gap between data science and IT operations. Our team builds robust infrastructure that takes your ML and LLM models from experimentation to enterprise-scale production. We automate workflows, ensure reliability, and maintain peak performance—so your AI investments deliver real business value.

The Challenge: 87% of ML models never reach production. We change that.

Why Your Business Needs MLOps & LLMOps

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Eliminate Deployment Bottlenecks

Manual ML deployment slows innovation and increases errors. Our automated CI/CD pipelines accelerate model releases from weeks to hours—ensuring your AI solutions reach production faster and more reliably.

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Prevent Model Degradation

AI models lose accuracy over time due to data drift. We implement continuous monitoring and automated retraining workflows that keep your models performing at peak accuracy in changing environments.

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Control Infrastructure Costs

Unoptimized AI infrastructure drains budgets quickly. Our MLOps solutions right-size compute resources, optimize GPU utilization, and implement cost controls—reducing infrastructure spend by up to 40%

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Ensure Compliance & Governance

Regulatory requirements demand transparency and auditability. We build governance frameworks with version control, audit trails, and bias monitoring to meet GDPR, HIPAA, and industry standards.

Our MLOps Technology Stack

Cloud Platforms

AWS SageMaker
AWS SageMaker
Azure Machine Learning
Azure Machine Learning
Google Vertex AI
Google Vertex AI
Databricks
Databricks

Orchestration

Kubernetes
Kubernetes
Docker
Docker
Kubeflow
Kubeflow
Apache Airflow
Apache Airflow
MLflow
MLflow

Monitoring

Prometheus
Prometheus
Grafana
Grafana
Weights & Biases
Weights & Biases
Evidently AI
Evidently AI
WhyLabs
WhyLabs

CI/CD Tools

GitHub Actions
GitHub Actions
GitLab CI/CD
GitLab CI/CD
Jenkins
Jenkins
Azure DevOps
Azure DevOps
ArgoCD
ArgoCD

LLM & GenAI Tools

LangChain
LangChain
Hugging Face
Hugging Face
OpenAI
OpenAI
Pinecone
Pinecone
Weaviate
Weaviate
Milvus
Milvus

Core Service Offerings

Scalable MLOps solutions to deploy, manage, and optimize machine learning models

Model Deployment Services

From Lab to Production in Record Time

We deploy your ML and LLM models to production environments with enterprise-grade reliability. Our deployment services cover real-time inference, batch processing, and edge deployment—ensuring your models perform consistently across all environments. Our team implements blue-green deployments, canary releases, and automated rollback mechanisms to minimize risk. We containerize models using Docker and orchestrate them with Kubernetes for seamless scaling. Whether you need cloud deployment on AWS, Azure, or GCP—or hybrid infrastructure—we deliver solutions tailored to your needs.

Service Features:

  • Real-Time & Batch Deployment : Deploy models for instant predictions or scheduled batch processing  
  • Containerization & Orchestration: Docker and Kubernetes-based deployment for consistent performance  
  • Multi-Cloud Deployment: AWS SageMaker, Azure ML, Google Vertex AI integration
  • Edge Deployment: Deploy models on IoT devices for low-latency applications 
  • Blue-Green & Canary Releases: Deploy models for instant predictions or scheduled batch processing Zero-downtime deployments with automated rollback  
  • API-Based Model Serving: RESTful APIs for seamless integration with existing systems  
  • Model Versioning: Track and manage multiple model versions in production
Enterprise Software Development Solutions
Enterprise Software Development Solutions

AI Infrastructure Services

Scalable Infrastructure Built for AI Workloads

We design and manage AI infrastructure that scales with your business. Our infrastructure services cover everything from GPU optimization and compute resource allocation to building automated ML pipelines that run 24/7. Our engineers implement Infrastructure-as-Code (IaC) practices using Terraform and Bicep for reproducible, auditable infrastructure. We build feature stores for efficient data management, set up experiment tracking systems, and create CI/CD pipelines specifically designed for machine learning workflows.

Service Features:

  • Cloud Infrastructure Setup: AWS, Azure, GCP configuration optimized for ML workloads
  • GPU Optimization: Right-size GPU allocation and maximize utilization  
  • Automated ML Pipelines: End-to-end automation from data ingestion to model training
  • CI/CD for Machine Learning: Continuous integration and deployment for ML models 
  • Data Versioning & Management: Track datasets, features, and model artifacts  
  • Infrastructure-as-Code: Reproducible, version-controlled infrastructure with Terraform
  • Hybrid & On-Premise Solutions: Secure infrastructure for sensitive data requirements

Performance Monitoring Services

Keep Your Models Accurate and Reliable

We implement comprehensive monitoring systems that track every aspect of your ML model performance. Our monitoring services detect issues before they impact your business—from data drift and model degradation to latency spikes and infrastructure bottlenecks. Our team deploys real-time dashboards, configures intelligent alerting systems, and sets up automated retraining triggers. We monitor model accuracy, prediction quality, and business KPIs to ensure your AI delivers consistent value.

Service Features:

  • Model Drift Detection: Identify concept drift and data drift before accuracy drops  
  • Real-Time Dashboards: Visualize model performance, latency, and throughput  
  • Automated Alerting: Instant notifications when performance thresholds are breached
  • A/B Testing Frameworks: Compare model versions with statistical rigor
  • Bias & Fairness Monitoring: Detect and address model bias in production
  • Latency & Throughput Tracking: Monitor response times and system capacity
  • Automated Retraining Triggers: Initiate model updates when performance degrades
Enterprise Software Development Solutions

Specialized Services for Large Language Models

Large Language Models require specialized operational practices. Our LLMOps services address the unique challenges of deploying, managing, and optimizing LLMs at enterprise scale—from prompt engineering to cost optimization and hallucination detection.

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Prompt Engineering & Management

We design, version, and optimize prompts to maximize LLM performance. Our prompt management systems track iterations and measure output quality across use cases.

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LLM Fine-Tuning Services

We fine-tune foundation models on your proprietary data. Our approach improves accuracy for domain-specific tasks while maintaining cost efficiency and compliance.

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RAG Pipeline Development

We build Retrieval-Augmented Generation pipelines that connect LLMs to your knowledge bases. Get accurate, contextual responses grounded in your enterprise data.

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Vector Database Integration

We implement and optimize vector databases like Pinecone, Weaviate, and Milvus. Enable semantic search and efficient context retrieval for your LLM applications.

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Cost Optimization for LLM Inference

We reduce LLM operational costs through caching strategies, model distillation, and smart routing. Achieve the same performance at significantly lower infrastructure spend.

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Hallucination Detection & Mitigation

We implement guardrails and validation systems that detect and prevent LLM hallucinations. Ensure your AI outputs remain accurate, reliable, and trustworthy.

Our Journey of Making Great Things

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MLOps & LLMOps Across Industries

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Healthcare & Life Sciences

AI models for medical imaging, drug discovery, and patient outcome prediction with HIPAA-compliant pipelines.

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Banking & Financial Services

Models for fraud detection, credit scoring, and algorithmic trading with regulatory compliance.

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Retail & E-Commerce

Recommendation engines, demand forecasting, and dynamic pricing with scalable infrastructure.

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Manufacturing & Industrial

Predictive maintenance and quality inspection with edge deployment capabilities.

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Government & Public Sector

Secure, compliant AI systems for citizen services and operational efficiency.

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Technology & SaaS

AI-powered product features with rapid iteration and A/B testing capabilities.

Why Choose Webority for MLOps & LLMOps?

End-to-End Lifecycle Management
We manage the complete ML lifecycle—from data preparation to deployment and monitoring.
Automated & Reproducible Pipelines
Every experiment and deployment can be traced and replicated with precision.
Scalable & Flexible Architecture
Infrastructure that grows with you without performance degradation.
End-to-End Lifecycle Management
We manage the complete ML lifecycle—from data preparation to deployment and monitoring.
Automated & Reproducible Pipelines
Every experiment and deployment can be traced and replicated with precision.
Scalable & Flexible Architecture
Infrastructure that grows with you without performance degradation.
Enterprise-Grade Security
Robust security with encryption, access controls, and audit trails.
Multi-Cloud Expertise
Work across AWS, Azure, and GCP without vendor lock-in.
CMMI Level 5 Quality
Process excellence with consistent quality and reliable delivery.
Enterprise-Grade Security
Robust security with encryption, access controls, and audit trails.
Multi-Cloud Expertise
Work across AWS, Azure, and GCP without vendor lock-in.
CMMI Level 5 Quality
Process excellence with consistent quality and reliable delivery.

Flexible Hiring Models 

We understand that every business has unique requirements and budget constraints. Choose the engagement model that aligns with your project scope, timeline, and objectives. 

Benefit Description Impact
Faster Time-to-Market Accelerate model deployment from months to days 60% faster  
Reduced Operational Costs Optimize infrastructure and automate workflows Up to 40% savings
Improved Model Accuracy Continuous monitoring prevents performance degradation 25% better  
Enhanced Reliability Automated testing and rollback ensure uptime 99.9% uptime  
Regulatory Compliance Built-in governance meets industry standards Audit-ready  

Our Proven MLOps Implementation Process

We follow a structured methodology that delivers results at every stage. Our approach balances thoroughness with efficiency, ensuring you see value quickly while building sustainable governance capabilities.

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Assessment &
Discovery

We analyze your current ML infrastructure, identify bottlenecks, and understand your business objectives. Our team evaluates your data pipelines, existing models, and operational requirements to create a tailored roadmap
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Strategy & Architecture Design

We design a comprehensive MLOps architecture aligned with your goals. This includes selecting the right tools, defining deployment patterns, and planning infrastructure that scales with your needs.
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Infrastructure
Setup

We build your ML infrastructure using Infrastructure-as-Code principles. Our engineers configure cloud resources, set up container orchestration, and establish secure networking for your AI workloads.
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Pipeline Development & Automation

We develop automated ML pipelines that handle data processing, model training, validation, and deployment. Every pipeline includes version control, testing, and rollback capabilities.
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Deployment & Integration

We deploy your models to production environments and integrate them with existing business systems. Our deployment strategies ensure zero downtime and seamless transitions.
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Monitoring & Continuous Improvement

We implement comprehensive monitoring and establish feedback loops for continuous optimization. Regular reviews identify improvement opportunities and ensure sustained performance.

What Our Clients Say About Us

"Webority transformed our document processing workflows with intelligent automation. Their AI solution reduced manual effort by 60% and improved accuracy significantly. Their team understood our compliance requirements and delivered a solution that exceeded expectations."

Director of Digital
Transformation

Healthcare Organization

Moumita

"The AI readiness assessment from Webority gave us clarity on where to start our AI journey. Their practical approach helped us prioritize high-impact use cases and build a realistic roadmap. We achieved ROI within six months of implementation."

CTO

Financial Services Company

Kumar Anubhav

"Working with Webority on our MLOps infrastructure was seamless. They established robust deployment pipelines that reduced our model deployment time from weeks to hours. Their ongoing support ensures our AI systems perform consistently."

Head of Data Science

E-commerce Platform

Prasanta Kumar

Any More Questions?

MLOps extends DevOps principles to machine learning. While DevOps focuses on software code, MLOps addresses unique ML challenges like data versioning, experiment tracking, model monitoring, and handling concept drift. We combine both practices to deliver reliable AI systems.

Implementation timelines vary based on complexity. A basic MLOps setup typically takes 4-8 weeks, while enterprise-scale implementations may require 3-6 months. We start with quick wins and incrementally build comprehensive capabilities.

We work across all major cloud providers—AWS, Microsoft Azure, and Google Cloud Platform. Our team has deep expertise in platform-specific services like SageMaker, Azure ML, and Vertex AI, as well as cloud-agnostic tools.

We implement comprehensive drift detection systems that monitor data distributions and model predictions. When drift exceeds thresholds, our automated pipelines trigger alerts or initiate model retraining to maintain accuracy.

LLMOps is a specialized subset of MLOps focused on Large Language Models. It addresses LLM-specific challenges like prompt management, fine-tuning at scale, managing hallucinations, and optimizing inference costs for billion-parameter models.

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Ready to Build Your Future with Us?

If you're passionate about technology, driven by purpose, and eager to grow alongside talented professionals, Webority is where you belong.

View Open Positions Connect With Us on LinkedIn
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