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

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