Ep 69 - How AIOps and MlOps will assist DevOps?

from Tech Stories · host Amit Bhatt

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 Model Data Extraction – ingesting data from various sources Exploratory Data Analysis – understanding the data format Data Preparation – cleaning and processing the data for easy processing Model Training – creating and training a model to process the data Model Validation and Evaluation – evaluating the model on test data to validate the performances Model Versioning – releasing a version of the model Model Deployment – deploying the model in production Core Elements of MLOps What Are Artificial Intelligence Operations? The core capabilities of AIOps Process 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 AIOps AIOps Toolset What Is the Difference Between MLOps and AIOps?

What this episode covers

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 Model Data Extraction – ingesting data from various sources Exploratory Data Analysis – understanding the data format Data Preparation – cleaning and processing the data for easy processing Model Training – creating and training a model to process the data Model Validation and Evaluation – evaluating the model on test data to validate the performances Model Versioning – releasing a version of the model Model Deployment – deploying the model in production Core Elements of MLOps What Are Artificial Intelligence Operations? The core capabilities of AIOps Process 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 AIOps AIOps Toolset What Is the Difference Between MLOps and AIOps?