AIOps vs. MLOps

Hi, we're Apify, a full-stack web scraping and browser automation platform. This article about AIOps vs. MLOps was inspired by our work on getting better data for AI.

AIOps and MLOps are not synonymous

It’s commonplace to mistakenly use artificial intelligence (AI) and machine learning (ML) interchangeably. Thankfully, the same is not true of AIOps and MLOps. They’re two distinct approaches in the field of IT and data operations, each serving unique purposes.

I already covered Artificial Intelligence for IT Operations in What is AIOps? and Machine Learning Operations in What is MLOps? So, what we'll do here is hone in on the differences and explain when you need the one and when you need the other. In the end, we’ll look at how and why you might combine them.

What is AIOps?

AIOps refers to the application of AI and machine learning techniques to enhance and automate IT operations. Its primary goal is to improve the management and monitoring of complex IT environments by analyzing data from various sources to provide actionable insights and predictive capabilities.

Why use AIOps?

For proactive problem resolution: To detect and predict issues before they impact users and allow for quicker problem resolution.
For automation: To automate routine tasks and responses and reduce the workload on IT staff.
For enhanced visibility: To provide a holistic view of the entire IT infrastructure and identify bottlenecks and areas for improvement.
For reduced downtime: To predict and prevent outages so as to minimize downtime for systems and applications.
For cost optimization: To help optimize resource allocation and reduce unnecessary spending.

What is MLOps?

MLOps, on the other hand, is a set of practices and tools aimed at streamlining the deployment, monitoring, and management of machine learning models in production environments. It focuses on the operationalization of ML models and maintaining their reliability over time.

Why use MLOps?

For reproducibility: To ensure that machine learning experiments are reproducible and that models are auditable.
For scalability: To facilitate the scaling of ML models to handle large datasets and increased workloads.
For model governance: To provide tools for tracking model versions, lineage, and compliance.
For reliability: To maintain model performance over time with automated monitoring and retraining.

What are the differences between AIOps and MLOps?

Focus

AIOps focuses on IT operations and infrastructure management.
MLOps focuses on managing machine learning models and their lifecycle.

Use of AI/ML

AIOps uses AI/ML for monitoring, alerting, and optimizing IT environments.
MLOps uses AI/ML for model training, deployment, and monitoring.

Primary domain

AIOps is mainly used in the IT and DevOps domain.
MLOps is primarily used in data science and machine learning projects.

What are the use cases of AIOps and MLOps?

AIOps use cases

Network performance monitoring: AIOps can analyze network data to identify anomalies, predict congestion, and optimize network performance.
Incident management: It can automatically classify and prioritize incidents, which reduces response times.
Capacity planning: It helps in optimizing resource allocation by predicting demand and load patterns.
Root cause analysis: It can identify the root causes of problems, which helps IT teams resolve issues faster.
Security: It detects and responds to security threats by analyzing patterns in log and event data.

MLOps use cases

Recommendation systems: MLOps is used to deploy and maintain recommendation models in applications like e-commerce.
Predictive maintenance: It helps deploy predictive maintenance models to minimize equipment downtime in manufacturing.
Natural language processing: It’s used to manage NLP models for chatbots, sentiment analysis, and language translation.
Financial forecasting: It ensures that predictive models for stock prices or credit risk are up-to-date and reliable.
Healthcare diagnostics: It's employed to deploy and monitor ML models for disease diagnosis and patient monitoring.

When to use AIOps and when to use MLOps?

➡️

Use AIOps when:

You need to optimize and automate IT operations and infrastructure.
You want to detect and resolve IT issues proactively.
You're dealing with monitoring and managing IT systems and networks.

➡️

Use MLOps when:

You’re developing, deploying, and maintaining machine learning models in production.
You need to ensure model reliability and scalability.
You’re involved in data science and AI projects where ML models play a central role.

When can you combine AIOps and MLOps?

Organizations may sometimes combine AIOps and MLOps to enhance their overall operations and derive more value from their AI and ML investments. So, let’s end with some examples of how you can integrate these two disciplines.

Automated incident resolution with ML predictions

Using AIOps to monitor IT infrastructure for anomalies and incidents continuously

When an incident is detected, AIOps can trigger an MLOps pipeline to analyze relevant data and predict the root cause. ML models may suggest resolutions or actions for IT teams based on historical data. This combination streamlines incident management and thus reduces resolution time.

Dynamic resource allocation for ML workloads

Using AIOps to monitor resource utilization in data centers or cloud environments

AIOps may trigger an MLOps process when resource constraints or performance issues are detected. ML models are able to predict resource requirements for upcoming machine-learning tasks based on historical patterns. Resources can be dynamically allocated to meet these requirements to optimize cost and performance.

Security threat detection and response

Using AIOps to monitor logs, network traffic, and system behavior for security anomalies

AIOPs can trigger an MLOps pipeline when there's suspicious activity. ML models analyze the detected anomalies to determine if they represent real threats. If a threat is confirmed, automated responses or alerts can mitigate the risk.

Optimizing ML model deployment

Using MLOps to manage the entire ML model lifecycle, from training to deployment

AIOps is able to monitor the performance of deployed ML models in production environments. If AIOps detects a drop in model accuracy or unusual behavior, it can trigger MLOps to retrain or update the model automatically.

Predictive capacity planning for ML infrastructure

Using AIOps to analyze the utilization and performance of servers, GPUs, and other infrastructure components

ML models are able to predict future capacity requirements based on historical data and upcoming ML workloads. So, if AIOps identifies capacity constraints or bottlenecks, it could trigger MLOps processes to help scale ML infrastructure efficiently.

Anomaly detection in ML model behavior

Using AIOps to monitor the behavior of deployed ML models, such as input data distribution and model output

MLOps may be triggered by AIOps when deviations from expected behavior are detected. The ML models analyze the anomalies to identify potential issues with data quality, model drift, or external factors.

Cost optimization for ML workloads

Using AIOps to track the costs associated with IT resources used for ML workloads

AIOPs can trigger MLOps processes to analyze cost data in relation to model performance and business objectives. ML models are able to make recommendations for optimizing resource allocation to achieve cost-efficiency without compromising performance.

AIOps and MLOps are different but not mutually exclusive

If you want to optimize and automate IT operations and infrastructure, detect and resolve IT issues, or monitor and manage IT systems, AIOps is what you need.

If you’re developing, deploying, and maintaining machine learning models in production and want to ensure model reliability and scalability, you need MLOps.

That being said, combining the two allows organizations to create a closed-loop system where AI-driven insights from AIOps inform and automate actions within MLOps. This ensures both efficient IT operations and the reliability of machine learning applications.

Whichever one you want to use, you can’t build an AIOps or MLOps solution without data. Both solutions begin with data collection, aka web scraping. If you need a web scraping platform for data acquisition, Apify provides the tools and infrastructure you need to harvest data from any website, including scrapers and integrations for collecting data for AI and machine learning.