Predictive AI uses statistical algorithms and other advanced machine learning techniques to anticipate what might happen next in a system. By analyzing patterns and trends, predictive analytics enables teams to take proactive actions to prevent problems or capitalize on opportunity.
Technology and operations teams work to ensure that applications and digital systems work seamlessly and securely. They handle complex infrastructure, maintain service availability, and respond swiftly to incidents.
But when these teams work in largely manual ways, they don’t have time for innovation and strategic projects that might deliver greater value. Therefore, the integration of predictive artificial intelligence (AI) in the workflows of these teams has become essential to meet service-level objectives, collaborate effectively, and boost productivity.
What is predictive AI?
Predictive AI uses statistical algorithms and other advanced machine learning techniques to anticipate what might happen next in a system.
Predictive AI uses machine learning, data analysis, statistical models, and AI methods to predict anomalies, identify patterns, and create forecasts. By analyzing patterns and trends, predictive analytics helps identify potential issues or opportunities, enabling proactive actions to prevent problems or capitalize on advantageous situations.
When predictive AI is combined with a data lakehouse, like Dynatrace Grail, it can deliver value by automatically providing prescriptive insights using data from digital user experience layer to the infrastructure layer with full data context using supporting data, such as relationships, dependencies, and other context within entities and events. While investigative techniques such as root-cause analysis are essential for teams striving to understand issues that have already occurred, predictive AI techniques such as forecasting and anomaly prediction help teams preempt issues. With the advances in causal AI (that is, AI that can explain cause and effect by identifying root-cause issues), teams want to take it to the next level and combine it with predictive AI to create a seamless foresight-to-hindsight continuum of data-driven answers and prescriptive insights.
The importance of predictive AI for ITOps, DevSecOps, and SRE teams
- Early detection of anomalies. Predictive AI empowers site reliability engineers (SREs) and DevOps engineers to detect anomalies and irregular patterns in their systems long before they escalate into critical incidents. By identifying subtle deviations in system behavior, engineers can take preemptive measures to avert potential downtime, performance issues, or security threats.
- Proactive resource allocation. Through predictive analytics, SREs and DevOps engineers can accurately forecast resource needs based on historical data. This enables efficient resource allocation, avoiding unnecessary expenses and ensuring optimal performance.
- Capacity planning. Understanding future capacity requirements is crucial for maintaining system stability. Predictive AI assists engineers in predicting demand fluctuations and adjusting resource capacities accordingly, ensuring seamless user experiences.
- Enhanced incident response. Predictive analytics can anticipate potential failures and security breaches. SREs and DevOps engineers can implement targeted remediation strategies and prioritize incident response efforts to minimize the impact on systems and users.
- Continuous improvement. By analyzing past incidents and performance metrics, predictive analytics helps SREs and DevOps engineers identify areas for improvement. This data-driven approach fosters continuous refinement of processes and systems.
Predictive AI-based capacity management and automation
Proactive capacity management is essential for avoiding outages and ensuring that an organization’s applications and services are always available. Operators need to closely observe business-critical resource capacities such as storage, CPU, and memory to avoid outages that are driven by resource shortages. However, traditional capacity management approaches are often reactive and time-consuming. Using Dynatrace Grail and Davis AI, predictive capacity management is straightforward:
- use Notebooks to explore important capacity indicators;
- create workflows to trigger forecast reporting at regular intervals; and
- use Davis AI for Workflows to automate the prediction and remediation of future capacity demands.
Predictive capacity management is a powerful tool that can help improve the availability and performance of applications and services. By using Dynatrace Grail and Davis AI, you can gain the insights you need to make proactive decisions about capacity planning and gain additional benefits:
- Increased visibility into future capacity demands. Predictive capacity management can help you to anticipate what your future capacity demands will likely be. This provides organizations with the ability to make proactive decisions about capacity planning, such as adding additional resources or scaling back resources that are not being used.
- Improved decision making for capacity planning. With predictive capacity management, you can make more informed decisions about capacity planning. This is because you have a better understanding of your future capacity demands and the impact of those demands on applications and services.
- Reduced costs associated with unplanned capacity increases. Unplanned capacity increases are costly. Organizations may need to purchase additional resources or pay for overtime. Predictive capacity management can reduce these costs by enabling organizations to plan for future capacity demands.
- Increased customer satisfaction. When your applications and services are available and performing well, your customers are happy. Predictive capacity management can help you to improve customer satisfaction by reducing the number of outages and performance problems.
This is just one example of predictive AI in action. But in fact, that there are numerous use cases for ITOps, DevSecOps, and SRE teams where they get the foresight into issues before they escalate into costly problems and preemptively addressed. They see improved efficiency, reduced risks of security breaches, and better compliance with industry regulations.