Continuous Delivery for Machine Learning and AI Models
Automate the process of Machine Learning from model building to model deployment in production. Automation involves following operations followed by uniting them –
- Build a Machine Learning Model
- Integrate Machine Learning model with the existing Data Pipelines
- Train model over a large dataset
- Versioning of Machine Learning/Deep Learning models
- Alpha/Beta testing of Machine Learning models
- Analyze the performance of various models
- Serve Machine Learning models
- Continuous deployment of Machine Learning models
Challenge for Deploying CI/CD Pipelines for Machine Learning and AI at Scale
- Develop a platform that can build, version, validate and serve Machine Learning models.
- Establish a standardized platform that enables cross-company sharing of features, data, and components involving consistent training, streaming, scoring logic.
- Inconsistency between Machine Learning Workflows.
- Team Struggle to initiate Machine Learning.
- Existing Machine Learning workflows are slow, fragmented and brittle.
Solution Offerings for Devops for Machine learning and AI
AKIRA.AI platform to perform Model Building, Validation, Versioning, Serving and Deployment of Machine Learning Models involving following features –
- Distributed training of Machine Learning models over Big Data
- Model Versioning
- Machine Learning Model Analytics
- Model Validation
- Model Visualization
- Model Impact Analysis
- Model comparison
- Model Serving in Production and Sandbox environments
Technology Stack –
- Model building
- Tensorflow, Keras, Scikit-Learn
- Model training Distributed / Standalone
- Data Warehouse
- Data Pipeline
- Data Visualization
- Model Versioning and Serving