March 02, 2023

Ep 69 - How AIOps and MlOps will assist DevOps?

11 minutes

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?

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