Cost Optimization Strategies for AI Infrastructure: A Comprehensive, Real-World Guide
As organizations scale their machine learning (ML) initiatives, they often grapple with complex challenges in operationalizing ML models. According to a recent survey by Gartner, an estimated 85% of AI and machine learning projects fail to move beyond prototype stages, highlighting a significant gap in the processes, collaboration, and infrastructure required for successful deployment of ML systems at scale (Gartner, 2023) . This white paper examines common MLOps (Machine Learning Operations) implementation challenges in production environments and presents solutions that have been proven effective in real-world projects across industries. Drawing on data from market analyses and ViaCatalyst’s extensive field experience,…
Cost Optimization Strategies for AI Infrastructure: A Comprehensive, Real-World Guide
As organizations scale their machine learning (ML) initiatives, they often grapple with complex challenges in operationalizing ML models. According to a recent survey by Gartner, an estimated 85% of AI and machine learning projects fail to move beyond prototype stages, highlighting a significant gap in the processes, collaboration, and infrastructure required for successful deployment of ML systems at scale (Gartner, 2023) . This white paper examines common MLOps (Machine Learning Operations) implementation challenges in production environments and presents solutions that have been proven effective in real-world projects across industries. Drawing on data from market analyses and ViaCatalyst’s extensive field experience, it provides actionable frameworks to help organizations build robust, scalable, and efficient ML pipelines while maintaining governance, reducing technical debt, and delivering measurable business value
Introduction Imagine you’re baking a cake. You start by gathering ingredients, mix them together, bake it, check if it’s done, and finally, you serve it to guests. In the world of machine learning, the process is quite similar: you collect data (ingredients), train a model (mixing), evaluate its performance (checking if it’s baked), and finally, […]
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