Machine learning (ML) is required to study the speed, volume, and diversity of Big Data. The findings of patterns and anomalies are used by DevOps teams to improve the development of new applications.
How Does Machine Learning Work?
Machine learning programs are based on neural networks. These are layered algorithms that feed on data streams from Big Data packages and process it through the layers. The systems are trained by inputs with known end results. By adjusting the coefficients, those results are modeled. The neural network learns to relate specific data with exact results.
Pattern Recognition And Machine Learning
Pattern recognition algorithms are used by DevOps to extract relevant information out of Big Data packages. Patterns are pulled out to identify what fits together and what features are in common. An example is how Google learned to identify cats in images.
Back in 2012, Google analyzed ten million YouTube videos and through deep learning algorithms, it managed a 70% success rate in identifying cats and humans. It researched metadata for words that appeared in correlation with the images for cats and humans, resulting in almost accurate recognition.
How can Pattern Recognition Help App Performance?
Consider the transaction response times of your application, they should be reaching an optimized level. When patterns appear in transaction data you can use a scatterplot tool to visually reflect movements.
Transactions are shown as data points and you can easily check for timeouts, stalls, client-side queuing and load thrashing. Use color-code to separate different transaction types and locations for better visuals.
For example, groups of transactions that appear separated from the rest could represent external bot interference or other factors affecting the application’s performance. However, many patterns are hidden in huge amounts of data. This is where ML helps.
Machine Learning & Apps. What are the main advantages?
ML And Finding the Cause Of Issues
ML can examine performance indicators across all app levels to help DevOps identify issues in containerized environments with millions of nodes. The capacity to learn what is normal and what is abnormal contributes to surfacing issues, increase troubleshooting speeds and sets off alarms for operations’ breaches.
Creating a Historical Context For DataTo avoid investigating the same problem twice, the best strategy is to create a historical roadmap of the problem and what was done to resolve it. ML programs can examine data to provide a clear image of what happened 24 hours ago, a week, month or a year ago, to indicate trends.
Analyze Trends to Predict A Fault
Prerequisites for a fault can be detected by the specific readings produced by the ML systems before a failure occurs. Understanding the origin of the fault will allow future prevention.
Optimize the Metrics of Your App
If your goal is to maximize uptime, increase performance or reduce time between deployments then you can use ML systems to process data in real time and help your team improve processes and remediate app behavior.
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