AI and machine learning In the right infrastructure-1

Artificial intelligence (AI) and machine learning (ML) are emerging fields that will transform businesses faster than ever before. In the digital era, success will be based on using analytics to discover key insights locked in the massive volume of data being generated today.

In the past, these insights were discovered using manually intensive analytic methods. Today, that doesn’t work, as data volumes continue to grow as does the complexity of data. AI and ML are the latest tools for data scientists, enabling them to refine the data into value faster.

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Data explosion necessitates the need for AI and ML

AI and ML use cases vary by vertical

  • Healthcare — Anomaly detection to diagnose MRIs scans faster
  • Automotive — Classification is used to identify objects in the roadway
  • Retail — Predictions can accurately forecast future sales
  • Contact center — Translation enables agents to converse with people in different languages

The right infrastructure, quality data needed

Data plays a key role in every use case of AI, although the type of data used can vary. For example, innovation can be fueled by having machine learning find insights into the large data lakes being generated by businesses. In fact, it’s possible for businesses to cultivate new thinking inside their organization based on data sciences. The key is to understand the role data plays at every step in the AI/ML workflow.

AI/ML workflows have the following components:

  • Data collection: Data aggregation, data preparation, data transformation, and storage
  • Data science/engineering: Data analysis, data processing, security and governance
  • Training: Model development, validation, and data classification
  • Deployment: Execution inferencing

One of the most significant challenges with data is building a data pipeline in real-time. Data scientists who conduct exploratory and discovery work with new data sources need to collect, prepare, model, and infer. Therefore, IT requires change during each phase, and as more data is gathered from more sources.

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It’s also important to note that the workflow is an iterative cycle in which the output of the deployment phase becomes an input to data collection and improves the model. The success of moving data through these phases depends largely on having the right infrastructure.

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