AIOps vs. MLOps: Harnessing big data for “smarter” ITOPs

Digital data has exploded in recent decades. Driven by significant advancements in computing technology, everything from mobile phones to smart appliances to mass transit systems generate and digest data, creating a big data landscape that forward-thinking enterprises can leverage to drive innovation.

However, the big data landscape is just that. Big. Massive, in fact. Wearable devices (such as fitness trackers, smart watches and smart rings) alone generated roughly 28 petabytes (28 billion megabytes) of data daily in 2020. And in 2024, global daily data generation surpassed 402 million terabytes (or 402 quintillion bytes).

As IT environments become more complex-with the adoption of cloud services and the use of hybrid environments, microservices architectures and increasingly integrated systems, DevOps practices and other digital transformation technologies-traditional IT operations (ITOps) management tools often struggle to keep pace with the demands of ever-increasing data generation.

Instead, businesses tend to rely on advanced tools and strategies-namely artificial intelligence for IT operations (AIOps) and machine learning operations (MLOps)-to turn vast quantities of data into actionable insights that can improve IT decision-making and ultimately, the bottom line.

AIOps and MLOps: What's the difference?

AIOPs refers to the application of artificial intelligence (AI) and machine learning (ML) techniques to enhance and automate various aspects of IT operations (ITOps).

AI technology enables computing devices to mimic the cognitive functions typically associated with human minds (learning, perceiving, reasoning and problem solving, for instance). And machine learning-a subset of AI-refers to a broad set of techniques for training a computer to learn from its inputs using existing data and one or more "training" methods (instead of being explicitly programmed). ML technologies help computers achieve artificial intelligence.

Consequently, AIOps is designed to harness data and insight generation capabilities to help organizations manage increasingly complex IT stacks.

MLOps is a set of practices that combines machine learning (ML) with traditional data engineering and DevOps to create an assembly line for building and running reliable, scalable, efficient ML models. It helps companies streamline and automate the end-to-end ML lifecycle, which includes data collection, model creation (built on data sources from the software development lifecycle), model deployment, model orchestration, health monitoring and data governance processes.

MLOps helps ensure that everyone involved-from data scientists to software engineers and IT personnel-can collaborate and continuously monitor and improve models to maximize their accuracy and performance.

Both AIOps and MLOps are pivotal practices for today's enterprises; each one addresses distinct yet complementary ITOps needs. However, they differ fundamentally in their purpose and level of specialization in AI and ML environments.

Whereas AIOps is a comprehensive discipline that includes a variety of analytics and AI initiatives that are aimed at optimizing IT operations, MLOps is specifically concerned with the operational aspects of ML models, promoting efficient deployment, monitoring and maintenance.

Here, we'll discuss the key differences between AIOps and MLOps and how they each help teams and businesses address different IT and data science challenges.

MLOps and AIOps in practice

AIOps and MLOps methodologies share some commonalities due to their roots in AI, but they serve distinct purposes, operate in different contexts and otherwise differ in several key ways.

1. Scope and focus

AIOps methodologies are fundamentally geared toward enhancing and automating IT operations. Their primary objective is to optimize and streamline IT operations workflows by using AI to analyze and interpret vast quantities of data from various IT systems. AIOps processes harness big data to facilitate predictive analytics, automate responses and insight generation and ultimately, optimize the performance of enterprise IT environments.

In contrast, MLOps focuses on lifecycle management for ML models, including everything from model development and training to deployment, monitoring and maintenance. MLOps aims to bridge the gap between data science and operational teams so they can reliably and efficiently transition ML models from development to production environments, all while maintaining high model performance and accuracy.

2. Data characteristics and preprocessing

AIOps tools handle a range of data sources and types, including system logs, performance metrics, network data and application events. However, data preprocessing in AIOps is often a complex process, involving:

• Advanced data cleaning procedures to handle noisy, incomplete and unstructured data

• Transformation techniques to convert disparate data formats into a unified structure so data is uniform and ready for analysis

• Integration methods to combine data from different IT systems and applications and provide a holistic view

MLOps focuses on structured and semi-structured data (feature sets and labeled datasets) and uses preprocessing methods directly relevant to ML tasks, including:

• Feature engineering to create meaningful input variables from raw data

• Normalization and scaling techniques to prepare data for model training

• Data augmentation methods to enhance training datasets, especially for tasks like image processing.

3. Primary activities

AIOps relies on big data-driven analytics, ML algorithms and other AI-driven techniques to continuously track and analyze ITOps data. The process includes activities such as anomaly detection, event correlation, predictive analytics, automated root cause analysis and natural language processing (NLP). AIOps also integrates with IT service management (ITSM) tools to provide proactive and reactive operational insights.

MLOps involves a series of steps that help ensure the seamless deployability, reproducibility, scalability and observability of ML models. It includes a range of technologies-including machine learning frameworks, data pipelines, continuous integration/continuous deployment (CI/CD) systems, performance monitoring tools, version control systems and sometimes containerization tools (such as Kubernetes)-that optimize the ML lifecycle.

4. Model development and deployment

AIOps platforms develop a wide range of analytical models, including-but not limited to-machine learning. These can include statistical models (regression analysis, for instance), rule-based systems and complex event processing models. AIOps integrates these models into existing IT systems to enhance their functions and performance.

MLOps prioritizes end-to-end management of machine learning models, encompassing data preparation, model training, hyperparameter tuning and validation. It uses CI/CD pipelines to automate predictive maintenance and model deployment processes, and focuses on updating and retraining models as new data becomes available.

5. Primary users and stakeholders

The primary users of AIOps technologies are IT operations teams, network administrators, DevOps and data operations (DataOps) professionals and ITSM teams, all of which benefit from the enhanced visibility, proactive issue detection and prompt incident resolution that AIOps offers.

MLOps platforms are primarily used by data scientists, ML engineers, DevOps teams and ITOps personnel who use them to automate and optimize ML models and get value from AI initiatives faster.

6. Monitoring and feedback loops

AIOps solutions focus on monitoring key performance indicators (KPIs)-such as system uptime, response time and error rates-across IT operations and incorporating user feedback to iterate and refine analytical models and services. The real-time monitoring and alerting systems within AIOps technologies enable IT teams to identify and resolve IT issues quickly.

MLOps monitoring requires teams to continuously track metrics such as model accuracy (correctness), precision (consistency), recall (memory) and data drift (external factors that degrade models over time). Based on those metrics, MLOps technologies continuously update ML models to correct performance issues and incorporate changes in data patterns.

7. Use cases and benefits

AIOps helps businesses increase operational efficiency and reduce operational costs by automating routine tasks that would typically require a human worker. This automation helps free up IT staff to focus on more strategic AI initiatives (instead of repetitive maintenance tasks). It also accelerates incident management by harnessing predictive analytics and automating the remediation process, enabling AIOps systems to find and fix issues before they cause unexpected downtime or affect the user experience.

Given their ability to break down silos and foster collaboration between different teams and systems, AIOps solutions are frequently used by IT departments to manage a company's data centers and cloud environments. AIOPs enables ITOPs personnel to implement predictive alert handling, strengthen data security and support DevOps processes.

MLOps technologies help businesses accelerate time-to-market for ML models, increase collaboration between data science and operations teams and scale AI initiatives across the organization. MLOps can also help organizations maintain data compliance and governance standards by ensuring that ML models are deployed and managed according to industry best practices.

MLOps has a range of uses cases across industries, including finance, where it can facilitate fraud detection and risk assessment; healthcare, where it helps create diagnostic models and improve patient monitoring; and retail and e-commerce, which use MLOps services to create recommendation systems ("You may also like…" prompts in online shopping platforms, for instance) and streamline inventory management.

Implement high-quality AIOps and MLOps with IBM Turbonomic

AIOps and MLOps are integral to maintaining a competitive edge in a big data world. With the IBM® Turbonomic® platform, forward-thinking enterprises can manage and continuously optimize hybrid cloud environments (including Amazon Web Services (AWS), Azure, Google Cloud, Kubernetes, data centers and more) with intelligent automation.

IBM Turbonomic is a software platform that helps organizations improve the performance and reduce the cost of their IT infrastructure, including public, private and hybrid cloud environments. With Turbonomic, teams can automate optimization tasks in real-time without human intervention, proactively deliver network resources across IT stacks and prevent resource over-provisioning in cloud environments.

Explore IBM Turbonomic

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