Struggling with Machine Learning? You’re Not Alone
Today’s organizations are up against a great machine learning paradox. Most are investing more than ever in artificial intelligence and machine learning (AI/ML), but far too few have implemented ML models or realized the business impact that AI/ML promises. With businesses pouring resources into AI and machine learning, why are results still so elusive? DataRobot dove deep into the AI/ML strategies of over 400 organizations across industries to find out.
The Promise of ML
Our research shows that 86% of organizations have increased their AI/ML budgets from FY20 to FY21, and 86% of companies rank AI/ML above other IT initiatives in terms of strategic importance. Clearly, they recognize the potential of AI/ML and know it’s crucial for their future success. Businesses are also organizing their workforces around driving AI/ML success, with 57% of organizations now employing 50 or more data scientists.
At the same time, the complexity of AI/ML projects poses a substantial challenge to businesses: 90% of organizations struggle with complex infrastructure or workload needs, 88% struggle with integration and compatibility of ML technologies, and 86% struggle with the frequent updates required for data science tooling.
Beyond technical complexity, organizations struggle with constantly changing regulatory and security requirements. In fact, IT security is the #1 hurdle for many enterprises as they grow their AI/ML initiatives. 88% of respondents ranked it as a challenge, with 25% — the largest percentage for any single challenge — naming it their “top challenge.” 85% also struggle with IT governance, compliance, and auditability requirements.
In looking at how organizations are handling these challenges, our research found that simply adding more people resources to AI/ML projects does not equal success. Rather than automating processes for deploying, managing, and optimizing models in production, organizations are taking on more manual work to scale the impact of AI/ML. It’s clear that this is unsustainable. How do businesses break this pattern?
The Platform Solution
Closing this gap requires an evolution in how AI/ML is delivered. This is where an end-to-end AI/ML platform with enterprise-grade machine learning operations (MLOps) built for automation comes in. A unified platform provides a center of excellence for production AI, giving organizations a central place to deploy, monitor, manage, and govern any machine learning model in production, regardless of how it was created or when and where it was deployed.
As the environment for AI and ML continues to become more complex and challenging, and organizations increasingly work across multi-cloud infrastructures and rapidly evolving security and regulatory requirements grow, the clearest path to success lies within AI platforms that automate ML pipelines and centralize their AI/ML applications. The extensive security and controls built into MLOps alleviate this burden on organizations so they can rapidly deploy models into production.
We believe that MLOps is critical to solving today’s most pressing AI/ML challenges. Organizations that get MLOps right are the ones that will be able to scale effectively and apply AI/ML in ways that drive true business impact.
Read the full report, “5 Latest Trends in Enterprise Machine Learning”.
Former Executive Vice President of MLOps, DataRobot
Diego Oppenheimer is the former EVP of MLOps at DataRobot, and previously was co-founder and CEO of Algorithmia, the enterprise MLOps platform, where he helped organizations scale and achieve their full potential through machine learning. After Algorithmia was acquired by DataRobot in July 2021, he continued his drive for getting ML models into production faster and more cost-effectively with enterprise-grade security and governance. He brings his passion for data from his time at Microsoft where he shipped Microsoft’s most used data analysis products including Excel, Power Pivot, SQL Server, and Power BI. Diego holds a Bachelor’s degree in Information Systems and a Masters degree in Business Intelligence and Data Analytics from Carnegie Mellon University.
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