When applying a lifecycle approach to MLOps, it's essential to recognize that different layers within the MLOps framework may be at varying maturity levels. For more information about our layered approach to enterprise-ready MLOps (eMLOps) see: Enterprise-Ready MLOps has [...]

A Guide to Using Small Language Models with Retrieval-Augmented Generation You’ve probably heard of ChatGPT, Gemini, LLama, Claude AI by now, right? They're the Generative AI (Gen AI) chatbots that seem to know everything and can have surprisingly human-like [...]

In a productionized machine learning (ML) pipeline, CI/CD (Continuous Integration/Continuous Deployment) can be applied to several components beyond just the ML model itself. These include data pipelines, feature engineering, model monitoring, infrastructure, and the codebase that manages the entire [...]

Enterprise-Ready MLOps (eMLOps) for Business Leaders Who Want Less Risk, More Reward (and Maybe a Little Fun) Let’s face it—AI is like the cool kid in class everyone wants to sit with, but no one knows how to approach [...]

Beyond the general considerations previously discussed, there are several additional aspects to think about when deploying an ML model into production to ensure it's truly enterprise-ready. Here are some further considerations: 11. Model Interpretability and Explainability Transparency: Ensure that [...]

Deploying an ML model into production in an enterprise-ready manner requires careful consideration of multiple aspects to ensure the solution is scalable, reliable, secure, and maintainable. Here are some general considerations: 1. Model Performance and Accuracy Validation and Testing: [...]

Deploying an ML model into production in an enterprise environment where multiple teams or departments are involved adds complexity to the process. Each team may have its own tools, processes, and priorities, which can impact various aspects of the [...]

MLOps, or Machine Learning Operations, is an evolving discipline that enables enterprise-ready deployment and management of machine learning (ML) models. As organizations increasingly rely on ML to drive business decisions and innovation, the importance of MLOps has surged. Productionizing [...]

Deploying an ML model in an enterprise-ready environment using MLOps is best understood as a solution rather than a service or a product because it encompasses a comprehensive, end-to-end approach that integrates various processes, tools, and practices to address [...]

When productionizing and operationalizing ML models in an enterprise environment, the following risks are particularly pronounced compared to other environments: 1. Compliance and Regulatory Risks Regulatory Compliance: Enterprises often operate in heavily regulated industries (e.g., finance, healthcare) where non-compliance [...]