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Data-driven approaches and sound MLOps strategies enable organizations to unlock the full potential of AI and ML. Abhijit Bose of Capital One discusses that while

AI and ML are being used to transform enterprises and improve customer experiences, incomplete machine learning operationalization prevents the full potential of AI strategies.

Its an incredibly exciting time to be working in the field of AI and ML. AI is in the headlines daily, permeating culture and society and creating capabilities and experiences we have never witnessed before. And importantly, AI can transform how organizations evolve to reach decisions, maximize operational efficiency, and provide differentiated customer experience and value. But scaling AI and machine learning to realize its maximum potential is a highly complex process based on a set of standards, tools, and frameworks, broadly known as machine learning operations or MLOps. Much of MLOps is still being developed and is not yet an industry standard.

The quality of an organizations data directly impacts machine learning deployments effectiveness, accuracy, and overall impact. High-quality data makes ML models more resilient, less expensive to maintain, and dependable. It offers the agility to react to data and model score drifts in real-time and makes refitting the model easier so it can re-learn and adjust its outputs accordingly. This requires organizations to create and execute a comprehensive data strategy incorporating data standards, platforms, and governance practices.

This starts with making sure that data scientists and ML engineers have standard tools, ML model development lifecycle (MDLC) standards, and platforms; making sure data is secure, standardized, and accessible; automating model monitoring and observability processes; establishing well-managed, human-centered processes like model governance, risk controls, peer review, and bias mitigation.

See More: The Growth of MLOps and Predictions for Machine Learning

MLOps has a set of core objectives: develop a highly repeatable process over the end-to-end model lifecycle, from feature exploration to model training and deployment in production; hide the infrastructure complexity from data scientists and analysts so that they can focus on their models and optimization strategies; and develop MLOps in such a way that it scales alongside the number of models as well as modeling complexity without requiring an army of engineers. MLOps ensures consistency, availability, and data standardization across the entire ML model design, implementation, testing, monitoring, and management life cycle.

Today, every enterprise serious about effectively driving value with AI and ML is leveraging MLOps in some capacity. MLOps helps standardize and automate certain processes so engineers and data scientists can spend their time on better optimizing their models and business objectives. MLOps can also provide important frameworks for responsible practices to mitigate bias and risk and enhance governance.

Even as businesses increasingly acknowledge what AI can do for them, a seemingly relentless wave of adoption since 2017 began to plateau last year at around 50% to 60% of organizations, according to McKinseys latest State of AI reportOpens a new window . Why? I argue that MLOps programs that standardize ML deployment across organizations are beset by too many data quality issues.

Data quality issues can take several forms. For example, you often see noisy, duplicated, inconsistent, incomplete, outdated, or just flat-out incorrect data. Therefore, a big part of MLOps is to monitor data pipelines and source data because, as most of us know, AI and ML are only as good as the collected, analyzed, and interpreted data. Indeed, the most misunderstood part of MLOps is the link between data quality and the development of AI and ML models. Conversely, incomplete, redundant, or outdated data leads to results nobody can trust or use effectively.

Unfortunately, with so much data being created every second of the day, organizations are losing the ability to manage and track all the information their ML models use to arrive at their decisions. A recent Forrester surveyOpens a new window revealed 73% of North American data management decision-makers find transparency, traceability, and explainability of data flows challenging. Over half (57%) said silos between data scientists and practitioners inhibit ML deployment.

See More: The Competitive Advantage of Quality Data

Data transparency is a persistent challenge with ML because to believe an algorithms insights or conclusions, you must be able to verify its accuracy, lineage, and freshness of data. You must understand the algorithms, the data used, and how the ML model makes decisions.

Doing all those things requires data traceability, which involves tracking the data lifecycle. Data can change as it moves across different platforms and applications from the point of ingestion. For example, multiple variations of merchant names or SKUs could be added to simple transaction data that must be sorted and accounted for before being used in ML models. Data must also be cleansed and transformed before reaching that point.

Rigorous traceability is also important for ensuring that data is timely and relevant. It can quickly degrade or drift when real-world circumstances change, leading to unintended outcomes and decisions. During the pandemic, for instance, demand-planning ML models couldnt keep up with supply chain disruptions, leading to inventory shortages or excesses in various industries.

Successful companies also deploy sophisticated technology platforms for testing, launching, and inspecting data quality within ML models. They supplement those platforms with modern data quality, integration, and observability tools. They bolster everything with policies and procedures like governance, risk controls, peer review, and bias mitigation. In short, they give data scientists, data and ML engineers, model risk officers, and legal professionals the tools, processes, and platforms to do their jobs effectively.

When we have integrated data, governance tools, and AI platforms, MLOps processes work remarkably well. When someone builds an enterprise ML model and pushes it to production, they can begin tracking its entire lifecycle. They can monitor how and where data moves and where it lives, preventing data quality and drift issues. As such, they are more confident their ML models can guide business and operational decisions.

See More: The Evolution of Data Governance

Engineers, data scientists, and model developers understand this. But its up to them to help senior business leaders understand why investing in data tools, technologies, and processes is critical for MLOps and, ultimately, ML. Business depends on the technology imperatives of data and ML teams, and no enterprise organization can hope to compete without an AI/ML roadmap. As Forrester saysOpens a new window , AI is an enterprise essential and is becoming critical for enterprises of all shapes and sizes. Indeed, the analyst firm predicts one in four tech executives will report to their boards on AI progress this year.

Part of that conversation must involve letting senior leadership know they cannot take their feet off their collective data and MLOps gas pedals. Today, many businesses success is tied to MLOps and the technologies data science and ML teams deploy. Leaders must understand the importance of building around a foundation of data and a modern cloud stack. If they dont, they are likely to be outperformed by competitors that do.

What data-driven considerations and approaches should organizations consider to get the most out of MLOps? Let us know on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window . Wed love to hear from you!

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MLOps & Quality Data: The Path to AI Transformation - Spiceworks News and Insights