How to maximize the value of AI and machine learning
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How to maximize the value of artificial intelligence and machine learning

Looking to the future of digital innovation in enterprise

Every enterprise aspires to be a data-driven enterprise. Artificial intelligence (AI) and machine learning (ML) help business leaders turn data into insight and insight into action – essential for business resilience and growth.

In fact, in Genpact's recent CIO study, conducted in partnership with the MIT Sloan CIO Symposium, CIOs consider AI and ML the top technologies that will help them achieve their business goals.

But it's not enough to simply prioritize AI and ML – the way business leaders approach its use needs to change.

From experimentation to operationalization

Business applications driven by AI and ML models support faster and more intelligent decision-making. However, from our experience working with enterprises across multiple industries, we've witnessed that roughly only half of all AI proof of concepts are ever scaled to production.

Since the start of the COVID-19 pandemic, enterprises have made more effort to apply findings from AI initiatives and tie AI investments to business value. Many enterprises are also focused on modernization to take full advantage of AI capabilities.

In short, there has been a shift in how senior executives approach AI in the enterprise (figure 1). Today, AI innovation needs to move beyond experimentation to operationalization.

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A new approach is the only way to demonstrate significant top-line and bottom-line benefits connected to better customer experiences, competitive advantage, and business growth.

Exploring common challenges

ML models that go into production need to handle large volumes of data, often in real-time. Unlike traditional technologies, AI and ML deal with probabilistic outcomes – in other words, what is the most likely or unlikely result.

Therefore, the moving parts of the ML model need close monitoring and swift action when deployed to ensure accuracy, performance, and user satisfaction.

Data scientists must grapple with three key influencing factors to ensure the proper development of ML models:

  1. Data quality: Data can come from various sources in different volumes and formats. Since ML models are built on data, the quality, completeness, and semantics of data is critical in production environments.
  2. Model decay: In ML models, data patterns change as the business environment evolves. This evolution leads to a lower prediction accuracy of models trained and validated on outdated data. Such degradation of predictive model performance is known as concept drift, which makes creating, testing, and deploying ML models challenging.
  3. Data locality: Data locality and access patterns are used to improve the performance of a given algorithm. However, such ML models might not work correctly in production due to the difference in the quality metrics.

These factors push ML practitioners to adopt a 'change anything, change everything' approach – but this often leads to more problems.

Data-science teams waste time and effort navigating technology and infrastructure complexities. Costs increase due to communication and collaboration issues between engineering and data-science teams, and because of the trade-off between achieving business goals and providing stable and resilient infrastructure platforms, projects slow. On average, it could take between three months and a year to deploy ML models due to changing business objectives that require changes to the entire ML pipeline.

Finding a solution

ML needs to evolve to tackle these challenges. Data and analytics leaders have to look for repeatable and scalable standalone software applications. In other words, enterprise leaders must rely on machine learning operations, or MLOps.

MLOps help organizations achieve automated and reliable ML model deployment, consistent model training, model monitoring, rapid experimentation, reproducible models, and accelerated model deployment (figure 2). Enterprise leaders can only achieve these benefits through continuous and automated integration, delivery, and training.

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Exploring the benefits

The implementation of MLOps in the enterprise helps align business and technology strategies, and delivers multiple benefits such as:

  • Rapid innovation: Faster, effective collaboration among teams and accelerated model development and deployment will lead to rapid innovation, enabling speed-to-market
  • Consistent results: Repeatable workflows and ML models will support resilient and consistent AI solutions across the enterprise
  • Increased compliance and data privacy: Effective management of the entire ML lifecycle, data, and model lineage will optimize spending on data privacy and compliance regulations
  • High return on investment: Management systems for ML models and model metrics will lead to smarter spending on viable use cases to avoid implementation failures
  • A data-driven culture: IT and data asset tracking with improved process quality will foster a data-driven culture powered by augmented intelligence

Case study

Bringing MLOps to life in healthcare

A healthcare equipment manufacturer struggled with delays in invoice payments and lacked a solid accounts receivables process. Genpact used MLOps to design a streamlined data-processing and ML model pipeline using open-source tools for rapid integration into existing infrastructure. The solution used 3.6 million invoice metrics for pre-processing and subsequent ML model training. As a result, we developed a fully automated pipeline with daily data feeds to reduce overdue invoices to less than 12% from 20-25%.

Getting started

The benefits of MLOps can be seen in every industry as enterprises aspire to become increasingly data-driven. So, how do you develop MLOps in your enterprise? It starts with five steps (figure 3):

  1. Data engineering
  2. Algorithm development
  3. Model deployment
  4. Model monitoring
  5. Model hypercare

Nevertheless, several factors – such as changes in business environments, unstandardized data collection, and unstable legacy systems, among others elements – lead to frequent changes in ML models. These changes often result in frequent re-deployments in live production environments, which are both inefficient and disruptive. On the other hand, when enterprise leaders effectively implement MLOps from the start of each project, the entire ML model lifecycle is streamlined.

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The way forward

By incorporating machine learning operations, or MLOps, from the earliest design phases of any AI project, enterprise leaders can harness the full potential of ML – without the headaches. These ML solutions can then be effectively scaled and replicated across the enterprise for greater return on investment and competitive advantage.

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