Overview of Building End to End Machine Learning Platform
Automate the process of Machine Learning from model building to model deployment in production.
Automate and Unite the following Operations:-
- 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 Machine Learning Platform at Scale
Develop a platform that can build, version, validate and serve Machine Learning models.
- A Data Scientist builds Machine Learning model, version and train models.
- A Data Engineer integrates the model with the existing Data Pipelines.
- An analyst can visualize the data generated from the Machine Learning model.
- A testing team can perform Alpha/Beta testing.
Establish a standardized platform that enables cross-company sharing of features, data, and components. Hence, “Make it easy to do the right thing” (e.g., consistent training/streaming/scoring logic).
Other common issues
- Inconsistency between Machine Learning Workflows.
- Team Struggle to initiate Machine Learning.
- Existing Machine Learning workflows are slow, fragmented and brittle.
Solution Offerings for Machine Learning Platform
AKIRA.AI is a platform to perform Model Building, Validation, Versioning, Serving and Deployment of Machine Learning Models.
Features of the model :
- 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 Framework :
- Model building (Tensorflow, Keras, Scikit-Learn)
- Model training Distributed / Standalone
- Data Warehouse
- Data Pipeline
- Data Visualization
- Model Versioning and Serving
- Deployment