Scale Smart. Operate Seamlessly.
Machine learning success is not just about building great models; it’s about running them reliably, at scale, and in production. This is where ML Ops comes into play.
At Levi9, we combine data science expertise with DevOps best practices to help you deploy, monitor, and manage machine learning models throughout their lifecycle. From versioning and automation to LLM Ops for large language models, we ensure your models deliver consistent, real-world impact.
Statistics indicate that only 13% of machine learning projects make it to production.
The primary reason for failure is a lack of operational readiness, rather than model quality (VentureBeat).
Model drift and performance decay are genuine challenges.
Without continuous monitoring and retraining, the accuracy and relevance of models can degrade over time.
Large language models (LLMs) require specialized care.
They need unique pipelines for prompt management, fine-tuning, and cost control that go beyond standard ML Ops.
Our approach:
01
CI/CD Pipelines:
Automating testing and deployment of models using CI/CD pipelines.
02
Infrastructure as Code:
Implementing tools like Terraform or Ansible to manage scalable cloud resources.
03
Enabling bridges:
Establishing clear communication channels between teams for continuous feedback and model validation.
04
Monitoring and Alerting:
Proactive monitoring of model performance with alerting systems to address issues before they impact operations.
05
Security and Compliance:
Ensuring that security measures and compliance standards are embedded in every stage of the ML lifecycle.
WHAT YOU GAIN: KEY OUTCOMES & DELIVERABLES
Faster time-to-value for ML initiatives
Less manual effort and fewer deployment bottlenecks
Scalable infrastructure that grows with your data needs
