What is MLOps?
MLOps stands for Machine Learning Operations. MLOps is a core function of Machine Learning engineering, focused on streamlining the process of taking machine learning models to production, and then maintaining and monitoring them. MLOps is a collaborative function, often comprising data scientists, devops engineers, and IT.
MLOps is a useful approach for the creation and quality of machine learning and AI solutions. By adopting an MLOps approach, data scientists and machine learning engineers can collaborate and increase the pace of model development and production, by implementing continuous integration and deployment (CI/CD) practices with proper monitoring, validation, and governance of ML models.
MLOps – The CEO’s Guide to Productionization of Data [Part 1]
MLOps (or Machine Learning Operations) is a core function of Machine Learning engineering, that focuses on streamlining the process of taking ML models to production, and maintaining and monitoring them. But before we get into more details about MLOps, it’s important to understand what operationalization of machine learning is, why it’s important, and how it […]Read More
MLOps – The CEO’s Guide to Productionization of Data [Part 2]
With data being touted as the oil for digital transformation in the 21st century, organizations are increasingly looking to extract insights from their data by building and deploying their custom-built ML models. In our previous article (MLOps – The CEO’s Guide to Productionization of Data, Part 1), we learned why and how embedding ML models […]Read More
Trust in Data: The Rise of Adversarial Machine Learning
Increased dependence on data and Machine Learning, and a lack of understanding of complex ML models are giving rise to a new category of cyber attacks called Adversarial Machine Learning attacks. Machine learning impacts our everyday lives – it determines what we see on eCommerce websites, social media platforms, and search engines. Since machine learning […]Read More
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