Description

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

1. Data Extraction – ingesting data from various sources
2. Exploratory Data Analysis – understanding the data format
3. Data Preparation – cleaning and processing the data for easy processing
4. Model Training – creating and training a model to process the data
5. Model Validation and Evaluation – evaluating the model on test data to validate the performances
6. Model Versioning – releasing a version of the model
7. Model Deployment – deploying the model in production

• Core Elements of MLOps
• What Are Artificial Intelligence Operations?
• The core capabilities of AIOps

1. 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.
2. Performance analytics – Anticipates performance bottlenecks by examining trends and making necessary improvements as needed.
3. Predictive intelligence – Utilizes machine learning to categorize incidents, suggest solutions, and proactively alert critical issues.
4. AI search – Offers precise, personalized answers through semantic search capabilities.
5. 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?