vodQA Bangalore 2019 - Ml and dev ops for qa
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Machine learning and dev ops for QA
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vodQA Bangalore 2019 - Ml and dev ops for qa
- 1. MACHINE LEARNING + DEV OPS FOR QA BY: SHILPA C DELL R&D
- 3. WHAT IS MACHINE LEARNING Supervised Learning Unsupervised Learning Reinforcement Learning
- 4. THE GEARS OF AUTOMATION -- DEVOPS – QA – ML ????
- 5. ROADMAP FOR QA IN ML Root Cause Analysis Defect classification Defect identification Recognize Patterns in Test Establish Predictability in Data for Continuous Integration Apply Human Ingenuity to Complex Algorithms
- 6. ROOT CAUSE ANALYSIS FROM LOG ANALYSIS USING ML
- 9. FUTURISTIC PROCESS FOR ROOT CAUSE ANALYSIS USING ML Data processing •Cleaning •Wrangling •Processing Machine learning Model •Build vector matrix •Feature Engineering/Dimensionality reduction •Cluster •Classify Predict Propose Solution Based on data labeling
- 10. ORCHESTRATION
- 11. SR ANALYSIS USING ML MODELS In coming Service Requests Process the SRs Cluster the SRs SR Analysis Improved T.A.T
Editor's Notes
- Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs Unsupervised learning is a type of self-organized Hebbian learning that helps find previously unknown patterns in data set without pre-existing labels Reinforcement learning, in the context of artificial intelligence, is a type of dynamic programming that trains algorithms using a system of reward and punishment
- The general approach currently used by many manufacturers when it comes to root cause analysis is to rely on on-site expert knowledge. With the growing scale and complexity of the IT systems, debugging and testing become more difficult. As it is very difficult for the verification engineer to have an insight in every piece of work that the developers do. The logs generated by the systems, in another point of view, reveal the states of a running system and contain a wealth of information produced by the system to support diagnosis, hence, the log analysis is a vital method of detecting failures or problems in a large system. It is, however, not realistic and practical to analyze huge amounts of logs manually. With the growing scale and complexity of the IT systems, debugging and testing become more difficult. As it is very difficult for the verification engineer to have an insight in every piece of work that the developers do. The logs generated by the systems, in another point of view, reveal the states of a running system and contain a wealth of information produced by the system to support diagnosis, hence, the log analysis is a vital method of detecting failures or problems in a large system. It is, however, not realistic and practical to analyze huge amounts of logs manually.
- Log Processing Bug/Defect classification Top issues Similar issues
- Noise Removal , Tokenization, Normalization , TFIDF, Stemming DBSCAN: Density-based spatial clustering of applications with noise Hierarchical clustering :Agglomerative hierarchical clustering KMeans