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Machine Learning.


Business Analytics

  • Defining business needs a firm wants to address with machine learning.

  • Analyzing the existing machine learning environment.

  • Designing ML strategy.

  • Deciding on ML deliverables.

Technical Design

  • Designing an optimal feature set for an ML solution.

  • Architecting an ML system according to scalability, security, and compliance requirements.

  • Selecting optimal machine learning technologies to be used.

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Data Preperation

  • Exploratory analysis of the existing data sources.

  • Data collection, cleansing, and structuring.

  • Defining the criteria for the machine learning model evaluation.

ML Development

  • ML model exploration and refinement.

  • ML model testing and evaluation.

  • Fine-tuning the parameters of ML models until the generated results are acceptable.

  • Deploying the ML models.



  • Delivering machine learning output in an agreed format.

  • Integrating machine learning models into an application for users’ self-service, if required.


  • Continuous monitoring and tuning of ML models for greater accuracy.

  • Adding new data to the ML models for deeper insight.

  • Building new ML models to address new business and data analytics questions.

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