March 8, 2023 By Gauri Mathur 4 min read

Imagine a team of explorers venturing into uncharted territory. To accomplish their objectives safely and efficiently, the team needs a leader. Their leader must be trustworthy, resourceful, adaptable and proficient with their tools: maps, compasses and other navigational instruments used to chart their course through the unknown. 

The rest of the team is responsible for working together to support the leader by using their unique skills and areas of subject matter expertise. The team ensures their leader has the tools and resources to accomplish their shared objectives. When working together, the team is a cohesive whole, able to navigate new challenges.

Data science and machine learning operations (MLOps) are tools analogous to those used by explorers in a wild land. Just as explorers use various instruments and techniques to gather information about a new place, Data Scientists use data science and MLOps to create predictive models that help organizations make informed decisions. Unfortunately, without a solid data science and MLOps foundation, many valuable AI projects struggle to leave the lab.

The increasing public acceptance of AI is reflected in increased spending on AI projects and hiring in related fields. However, the time it takes to deploy a model is also growing, indicating that the foundation of AI model production, specifically data collection and analysis, requires improvement. TechTarget reports 83% of organizations have increased their AI budgets, with the average number of data scientists employed rising by 76%; however, the time required to deploy a model has increased, with 64% of organizations taking months or longer. According to Gartner, 53% of AI and ML projects remain in pre-production phases, and most machine learning models never make it to production. In addition, one-third of enterprises analyze less than half of generated IoT data, highlighting the challenges in data collection and analysis that many organizations face.

Click here to read the IDC MarketScape report.

The importance of high-quality data for AI and ML models

The field of data science has experienced tremendous growth in recent years, with AI driving much of this expansion. However, in this changing landscape, the data scientist—our expedition leader—has become mired. Manual tools introduce drift and bias, and poorly documented processes complicate the data scientist’s job, requiring them to spend valuable time troubleshooting.

The quality and quantity of data used to build AI and ML models, as well as algorithms, are critical to success. Incomplete, inaccurate or biased data sets can lead to faulty algorithms and skewed analytic outcomes. In addition, organizations face challenges aggregating disparate data sets from silos across the enterprise, from external sources and various deployments, including both on-premises and the cloud. A model that uses insufficient data can introduce risk to your business operations—wasting both time and resources—and must be retrained or discarded entirely. Many experiments die on the vine without ever achieving business value.

CEOs, CDOs and CIOs worldwide are overseeing a lot of experimentation with no insight into when the model will give back. Data science and machine learning investments are critical for ensuring accurate, unbiased models and for effectively collecting and using high-quality data.

IDC names IBM a leader in Data Science and MLOps

According to studies conducted by IDC—a global provider of market intelligence for IT, telecommunications and consumer technology—organizations tend to increase their investments in AI and ML initiatives once they succeed with early implementations. However, managing multiple use cases can significantly increase the complexity of model management. IDC suggests that organizations plan for scalability by developing machine learning pipelines that simultaneously move multiple models from experimentation to production.

IDC placed IBM Data Science and MLOps in the “Leaders” category in their 2022 MarketScape vendor assessment. IBM’s core machine learning portfolio—IBM Watson Studio—provides end-to-end support for the entire machine learning life cycle, from data ingestion to deployment and monitoring. In addition, IBM’s MLOps includes AI tools that assess fairness and explainability. It also features built-in bias mitigation algorithms and governance, risk, and compliance capabilities to ensure trustworthy AI.

MLOps is a powerful tool for organizations looking to build, train and deploy models at scale. Using automated low-code and no-code tools, MLOps streamlines the entire AI lifecycle: automated processes ensure that data is high quality, governed and easily accessible while predicting bias and drift.

Change Machine and IBM team up to create economic change

Insufficient data creates bias that can lead to unfair decisions and skewed results, which poses a significant problem for organizations in sensitive areas like healthcare, finance and criminal justice. Change Machine—a non-profit tech organization that seeks to build financial security for low-income communities, improve economic security and minimize systemic barriers for low-income borrowers—worked with IBM to apply ML to the recommendation engine in their Fintech platform. 

The platform contains various Fintech products and services that Change Machine has vetted to be inclusive, safe and effective. However, the initial launch exposed gaps in the system. Namely, recommendations were based on data from a small sample size and not all customer data was being used effectively.

Development proceeded quickly, and Change Machine was able to integrate the new recommendation models into the platform at the end of six weeks. Ongoing usage of the platform increased from 60% to 90%, as users grew to trust the results, and the project team achieved a 20% improvement in customer net promoter score (NPS) also within six weeks. The models are self-learning, scalable and based on trusted AI; meaning, the reasoning behind the recommendations is always explainable.

Data science and MLOps is critical for organizations to get better value from their data and build more accurate and unbiased models. IDC’s MarketScape named IBM a leader in data science and MLOps, citing IBM’s end-to-end support for the entire machine learning life cycle, including AI tools that assess the fairness, explainability, bias mitigation algorithms and governance, risk and compliance capabilities.

Click here to learn more about IBM’s data science and MLOps offerings and read the IDC MarketScape report.

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