According to Forrester, more than half of global businesses implemented or are in the process of implementing AI. Despite the increasing use of ML, many organizations struggle with brittle development and deployment processes. Only 8% of organizations consider their ML programs sophisticated, a survey of decision-makers says. More than 40% of organizations could move their model into production after a month.
AI and data are at the epicenter of most digital transformation projects. Reducing time to market, minimizing risk, improving productivity, increasing the top line by launching new business models quickly, improving net promoter scores (NPS), improving the bottom line by improving the total cost of ownership and an ever increasing technical debt within organizations are daunting challenges for every CXO.
How do you overcome these challenges in your AI-led transformation journey?
Using MLOps methodology, rapidly growing organizations are 3x more likely to get models into production.
DataOps and MLOps
The close concept related to DevOps in software engineering, DataOps, and MLOps reduces the technical friction by automating data pipelines and bringing the AI model to production in the shortest possible time.
Empower data scientists and promote reliable AI solutions with:
Scalable data pipelines
Automated data pipelines and scalable data ingestion to meet rapid data demands.
Govern your AI inventory by cataloging and tracking the existing and new models.
Collect, organize and track model training information across multiple runs with different configurations.
Scaling deployments automatically to meet demand and save costs.
Watch for data drift, implement continuous learning and automatically re-train models.
How do we enable a trusted MLOps framework?
Our experts leverage practical knowledge and AI capabilities to set up practices for collaboration and communication in your data and AI ecosystem.
Our MLOps framework
Data Management and Preparation
- Automated data pipelines with our out-of-the-box solution InsightBox
- Pre-built cleansing routines and validation against the set schema
- Data integration with pre-built connectors for 150+ data sources
- Distribute validated data automatically into training data sets
- Version control and metadata management automation
- Storage-agnostic version control systems to suit ML workflows
- Auto-committing to version control as the metadata from new runs gets checked
- Metadata store for future analysis
- Model monitoring and validation with a pre-built framework
- Auto capture of performance data for each model run
- Records to enable reusability
- Triggers for subsequent re-training if the model performance degrades
- Set up ‘AI models as a service’
- Create a production-ready model repository
- Storing model metadata and setting up a model registry
- Real-time model monitoring
- Detect data drift, capture anomalies, and monitor model accuracy
- Re-training triggers and alerts