MLOPS
Let your Data Scientist team focus on delivering Innovation & Business Value. Offload non-core tasks to our MLOPS
Why MLOPS :
- Data Scientist gets bogged down with technical details for resolving operational technical issues than focus on innovation
- Ongoing monitoring & retraining is needed to models deployed in production to address data drift
- Lack of consistent, repeatable process for model management, which results in loss of trust in model performance and accuracy
- Significant efforts are spent by data scientists on nonfunctional requirements such as scalability, high availability, performance, security, software upgrades
- Lack of Automation of model deployment and management process
Our MLOPS team helps to :
- Keep your models in production up and running
- Proactively monitor ML models and detect issues like data drift before they cause problems
- Automate and execute model retraining, data pipelines, and batch processing to keep data scientists focused on innovation
- Enable reproducible models by tracking data, models, code, and model versioning
- Package and deliver models in repeatable configurations to support reusability
- Proactively address any security & regulatory compliance related issues
Our MLOPS Methodology:
Our MLOps methodology includes a process for streamlining model re-training, validation, deployment, and monitoring to help ensure ML projects run consistently from end-to-end
Benefits of engaging with us :
- Team of MLOPS expert, which are supporting multiple AI/ML projects running in production
- Expertize on various tools used to in MLOPS
- Partnership with leading MLOPS vendors
- Our proven managed services model for Production Support which includes Proactive monitoring, On-call support and Incident management
- Cloud scale automation by blending resources having expertize in devops and machine learning