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?