Machine Learning

Data Analytics Journey Phase 3

 

After the modern data platform has been well-implemented, we can enhance the analytics with ML (Machine Learning) to get more insights.

Automation

  • Machine learning can automate many processes and tasks.
  • Enhanced Decision Making – Machine Learning algorithms can analyze large amounts of data to identify patterns, trends, and correlations that can help guide decision making.
  • Improved Customer Experience – Machine learning can be used to personalize customer experiences, such as as recommending products or creating tailored content.
  • Fraud Detection – Machine Learning can detect fraudulent behavior fastly.
  • Predictive Analytics – Machine Learning can be used to analyze data and build predictive models to forecast future outcomes.

Traditional Programming vs Machine Learning

Traditional programming methodology is based on predefined “rules” to calculate or predict the results.

Machine Learning (ML) is based on the training with past transactions to generate the rules, which is just needed to focus on the Data Input and Output.

Machine Learning (ML)

Machine Learning Operations (MLOps)

1.Data Ingestion

  • Data movement

 

2.Data Preparation

  • Normalization
  • Transformation
  • Validation
  • Featurization

 

3.Model Training

  • Hyper-parameter tuning
  • Automatic model selection
  • Model testing
  • Model validation

 

4.Model Deployment

  • Deployment
  • Batch scoring
  • Context capture

 

5.Operationalization

  • Instrumentation
  • Monitoring
  • Alerting
Machine Learning Operations (MLOps)

Value of Machine Learning

  • Discover hidden patterns
  • Analyze in multiple dimensions
  • Continuously improving
Value of Machine Learning (ML)

Output of Machine Learning

  • Through API or Storage to output results to Mobile Apps, Websites, Report/Dashboard, RPA System, etc.
Machine Learning (ML) Output

Example usages of Machine Learning:

  • Customer Scoring
  • Customer Segmentation
  • Customer Behavior Analysis
  • Data Profiling
  • Abnormal Detection

 

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Should you have any question or interest to check out more details, welcome to contact us.

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