Predictive monitoring uses data analytics to predict the future status of an IT infrastructure. Analyzing the data involves various processes, including:
Using these methods, you can study the infrastructure’s behavior over a specific time frame to determine what problems are likely to arise later.
While predictive monitoring and maintenance may seem like interchangeable terms, the former focuses on forecasting the system’s status. In monitoring, the aim is to determine what task needs to be done to address the future problem.
However, predictive maintenance also uses the same processes as predictive monitoring. Its goal is to determine when the system should undergo maintenance.
Of course, you can implement both predictive monitoring and maintenance in your IT management practices.
Some main benefits of predictive monitoring include:
Monitoring can help spot past errors, allowing the IT team to spot patterns that occur before significant system failures. This helps the developers plan the correct course of action if these patterns arise, reducing downtime periods.
Since the IT team will determine a pattern in the infrastructure failures, they can also review the patterns themselves to find the main cause of the errors. They can now work on creating fixes to improve the system’s performance.
Predictive monitoring can also help spot any irregularities in the infrastructure that aren’t just developer errors but possibly cybersecurity vulnerabilities.
With features such as automated data versioning, immutable data lineage, and an easily understandable console, Pachyderm allows for efficient predictive monitoring with your project. As a result, you can better optimize your data science operations.
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