Machine Learning.
Business Analytics
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Defining business needs a firm wants to address with machine learning.
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Analyzing the existing machine learning environment.
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Designing ML strategy.
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Deciding on ML deliverables.
Technical Design
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Designing an optimal feature set for an ML solution.
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Architecting an ML system according to scalability, security, and compliance requirements.
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Selecting optimal machine learning technologies to be used.
Data Preperation
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Exploratory analysis of the existing data sources.
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Data collection, cleansing, and structuring.
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Defining the criteria for the machine learning model evaluation.
ML Development
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ML model exploration and refinement.
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ML model testing and evaluation.
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Fine-tuning the parameters of ML models until the generated results are acceptable.
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Deploying the ML models.
Reporting
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Delivering machine learning output in an agreed format.
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Integrating machine learning models into an application for users’ self-service, if required.
Maintenance
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Continuous monitoring and tuning of ML models for greater accuracy.
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Adding new data to the ML models for deeper insight.
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Building new ML models to address new business and data analytics questions.
