AIOps and MLOps are both essential components of an AI-powered business. Many companies have used these terms interchangeably in recent years, but there is a difference between them. Understanding that difference can help you understand what role AI will play in your organization and how it will change your business practices.
What is AIOps?
Artificial intelligence for IT operations, also known as AIOps, is a category of tools and strategies that allows organizations to take advantage of big data and machine learning.
AIOps uses artificial intelligence to automate and optimize tasks in enterprise IT infrastructure. This process is called self-optimizing. The goal is to minimize downtime and maximize performance.
Read more on Datamation: Top AIOps Companies
Why is AIOps important?
AIOps’s most common use cases help with analytics and intelligent alerts in the event of a system failure or outage. AIOps tools offer a versatile approach to application performance management, given they can track business transactions in addition to application functions.
Other challenges that AIOps can solve include anomaly detection, compliance auditing, noise reduction, regulatory reporting, and end-to-end process monitoring.
Anomaly detection refers to identifying patterns in data that fall outside what would be considered normal. By using machine learning models trained on historical data, AIOps systems can flag situations where actions take place that are unusual for specific users or applications.
Identifying non-compliance with regulatory standards is another key benefit of AIOps solutions. Such standards include:
- Payment Card Industry Data Security Standard (PCI DSS)
- Health Insurance Portability and Accountability Act (HIPAA)
- ISO 27001/27002
- Sarbanes–Oxley Act (SOX)
These regulations are designed to protect sensitive data by requiring strong encryption techniques, access control measures, proper disposal procedures, and other safeguards.
There are many reasons why event noise reduction is important for data collected by enterprise software platforms. When reviewing logs and other forms of event data, unwanted noise makes it difficult to determine what information is important.
Excessive noise wastes time and resources because it requires additional human effort to sort through irrelevant events. High noise levels may indicate issues with software design, as well as underlying vulnerabilities that hackers might exploit if they gained access to a platform. Noise reduction in an AIOps system helps to filter out extraneous information so you can focus on meaningful signals.
Regulators often require businesses to produce reports describing how they adhere to industry guidelines, including PCI, HIPAA, and others. Unfortunately, manually generating such reports is tedious and time-consuming, especially if you’re managing thousands of servers or hundreds of apps.
However, with AIOps solutions, you can automate compliance audits through real-time monitoring of your entire infrastructure and integration with cloud storage services.
End-to-end process monitoring
AIOps provides information about all of an organization’s technology assets and how they perform while also providing insight into how they are used. AIOps tools provide organizations with visibility across multiple layers of IT operations, including application deployment and management, network monitoring, and security analytics.
What is MLOps?
Maching learning operations (MLOps) is a framework that supports a continuous, AI-based learning system for various processes and methods. It combines both people and tools, covering three aspects of machine learning: design, training, and deployment.
MLOps solutions make it possible to collect, analyze and interpret large amounts of data. Then, with various machine learning algorithms, these systems can detect anomalies in your infrastructure or applications that might be causing performance issues.
The goal of MLOps is to use these algorithms to automate repeatable tasks in data science projects and use historical analytics results for more efficient and optimized future operations. MLOps uses modern deep learning algorithms for large-scale data processing compared to traditional business intelligence applications.
Read more: Best MLOps Tools & Platforms for 2022
Why is MLOps important?
MLOps helps organizations be more efficient and successful with data-driven analysis. MLOps also improves operational intelligence so enterprise decision-makers can make better business decisions for an organization’s long-term success.
Other key benefits of MLOps solutions include deployment flexibility, repeatable workflows, risk mitigation, regulatory compliance, automated development, and rapid threat response.
MLOps solutions make it easy to deploy trained models on any node within an organization’s network, making it possible for teams to use their infrastructure for model training and testing.
MLOps solutions simplify repeatability by providing pre-built workflows for common tasks such as model training, validation, and deployment across environments.
The risk involved with using machine learning models can be mitigated with MLOps solutions because they provide tools for monitoring how well each model performs over time. This information helps organizations determine whether or not a particular model needs to be updated or replaced with a new version.
Organizations that handle sensitive customer data must comply with strict regulatory requirements to protect consumer privacy. To do so, many companies are turning to MLOps solutions that are designed specifically for compliance purposes. These platforms allow users to train and test machine learning models without putting personal information at risk of exposure.
Read more: How to Meet Regulatory Compliance
Automated MLOps solutions allow developers to build, train, and test machine learning models much more quickly than they could if they were doing those tasks manually.
Plus, MLOps solutions can be deployed in days or weeks rather than months or years. They offer faster time-to-market by allowing developers to get results faster and improve models with less effort.
This increased speed reduces overall costs and frees up human resources for other projects.
Rapid threat response
When a security breach occurs, IT professionals need to take action quickly to minimize damage and prevent further incidents from occurring. MLOps solutions can be used to identify threats and prevent future attacks by identifying similar events in real-time. They can also be used to assess the damage and notify stakeholders that may have been affected.
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AIOps vs. MLOps: How are they different?
AIOps and MLOps are sometimes confused as being the same. In reality, they have distinct purposes, processes, and responsibilities.
AIOps aims to take data from various sources and provide a consolidated view of what’s happening within an IT environment. This information can then be used to identify potential problems or opportunities for improvement before they occur. On top of that, AI-driven systems can also be used to automate tasks that may otherwise require human intervention.
MLOps, on the other hand, focuses on creating machine learning models for predictive analytics and real-time insights. These models can help with capacity planning, root cause analysis, automation recommendations, and more.
AIOps vs. MLOps: Which should you choose?
While AIOps and MLOps are often used together in an IT environment, it’s important to understand which tactic is best suited for various needs.
For instance, if you want to improve efficiency by streamlining certain operational tasks and processes, you might find that using MLOps technology is better suited than using AIOps technology.
Alternatively, suppose your main goal is to automate machines or identify potential risks or issues before they become actual problems. In that case, you might find that using AIOps technology is better than MLOps technology.
Understanding how these two solutions work together will also help ensure you get maximum value from them going forward.
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