Machine Learning Operations
Our MLOps services empower customer organizations to streamline and scale their machine learning operations with confidence. We build robust CI/CD pipelines for ML models, enable real-time model monitoring and governance, automate infrastructure management, and create efficient collaboration workflows between data science and engineering teams. Our MLOps solutions ensure faster model deployment, improved reliability, regulatory compliance, and continuous performance optimization, helping businesses unlock greater value from their AI and ML initiatives.
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Case Study
Accelerating Model Deployment for E-commerce Analytics
Customer Challenges:
An e-commerce company faced long delays in moving machine learning models from development to production due to manual handoffs, inconsistent environments, and error-prone deployments.
Solution Delivered:
Avashya Tech built end-to-end CI/CD pipelines tailored for ML models:
- Automated model validation, testing, and approval workflows.
- Containerized models with Docker and Kubernetes-based deployment.
- GitOps-based management for model version control and rollback strategies.
Results/Outcomes:
- Reduced model deployment time by 75% (from weeks to days).
- Increased success rate of production deployments to 98%.
- Enabled frequent and reliable updates of recommendation algorithms, improving customer conversion rates by 12%.
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Case Study
Auto-Scaling ML Infrastructure for Retail Forecasting
Customer Challenges:
A retail chain struggled with unpredictable compute demands during promotional seasons, leading to either expensive over-provisioning or outages during ML-based inventory forecasting.
Solution Delivered:
Avashya Tech automated the ML infrastructure using:
- Infrastructure-as-Code (IaC) tools like Terraform and Ansible.
- Auto-scaling clusters on AWS/GCP for ML training and inference workloads.
- Spot instance utilization and intelligent resource orchestration to optimize costs.
Results/Outcomes:
- 35% reduction in cloud infrastructure costs.
- 100% uptime during peak sales periods.
- Forecasting model throughput improved by 2.5x during scaling events.
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Case Study
Streamlining Team Productivity for Pharma Research
Customer Challenges:
A pharmaceutical company had data scientists, ML engineers, and business stakeholders working in silos, causing long feedback loops, misaligned priorities, and delayed drug discovery projects.
Solution Delivered:
Avashya Tech implemented collaborative MLOps workflows by:
- Centralizing model and experiment tracking with tools like MLflow and Weights & Biases.
- Creating shared dashboards for model metrics, business KPIs, and version history.
- Integrating Slack and Jira for real-time collaboration and ticketing linked to model experiments.
Results/Outcomes:
- Reduced time-to-insight for experimental models by 50%.
- Increased cross-functional alignment, speeding up new drug candidate identification by 30%.
- Data scientists spent 20% more time on actual research instead of operational tasks.
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Case Study
Quality Assurance System for Healthcare AI Assistant
Customer Challenges:
A healthcare provider deployed an LLM-based assistant for patient queries but lacked a robust way to monitor its output for factual accuracy, patient safety, and compliance with healthcare regulations (HIPAA, etc.).
Solution Delivered:
Deevi Tech implemented a comprehensive LLM monitoring system including:
- Real-time logging of inputs/outputs.
- Accuracy and safety scoring with automated flags for sensitive cases.
- Human-in-the-loop feedback mechanism for continuous model improvement.
- Dashboards tracking drift, hallucinations, and harmful outputs.
Results/Outcomes:
- 98% safe response rate achieved within 3 months.
- Continuous improvement cycles reduced hallucination incidents by 45%.
- Regulatory compliance audits passed without major findings.