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In this episode, you will come to know  How AI and ML are playing a big role in the acceleration of DevOps?What Are Machine Learning Operations?Lifecycle of a Machine Learning ModelData Extraction – ingesting data from various sourcesExploratory Data Analysis – understanding the data formatData Preparation – cleaning and processing the data for easy processingModel Training – creating and training a model to process the dataModel Validation and Evaluation – evaluating the model on test data to validate the performancesModel Versioning – releasing a version of the modelModel Deployment – deploying the model in productionCore Elements of MLOpsWhat Are Artificial Intelligence Operations?The core capabilities of AIOpsProcess optimization – Enhances efficiency throughout the enterprise by comprehensively understanding the connections and effects between systems. After identifying a problem, it facilitates refinement and ongoing monitoring of processes.Performance analytics – Anticipates performance bottlenecks by examining trends and making necessary improvements as needed.Predictive intelligence – Utilizes machine learning to categorize incidents, suggest solutions, and proactively alert critical issues.AI search – Offers precise, personalized answers through semantic search capabilities.Configuration management database – Enhances decision-making with visibility into the IT environment by connecting products throughout the digital lifecycle, allowing teams to comprehend impact and risk.Core Element of AIOpsAIOps ToolsetWhat Is the Difference Between MLOps and AIOps?