English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 1h 15m | 173 MB
IT operations is one of the key business functions for modern enterprises. As data centers become large, distributed, and integrated, the need to monitor and manage hardware, software, networks, and data increases exponentially. And while the elements in a network generate tons of data in terms of logs and events, the need to collect and understand this data to predict future outcomes is also increasing. In this course, learn how to solve common challenges in IT operations using the power of AI. Instructor Kumaran Ponnambalam reviews the key issues that IT ops teams face in their day-to-day operations. He then goes over several uses cases in the world of IT ops, explaining in detail how AI technology can speed up processes like root cause analysis, improve response times at your IT help desk, and more. Along the way, he uses Python, Jupyter Notebooks, Keras, and deep learning techniques to step through practical solutions.
Table of Contents
1 Artificial intelligence and its many uses
2 What you should know
3 Intro to IT ops
4 IT ops challenges
5 AI and IT ops
6 IT ops use cases overview
7 Setting up the exercise files
8 What is root cause analysis
9 Classification with deep learning
10 Data for root cause analysis (RCA)
11 Preprocessing RCA data
12 Building a classification model with Keras
13 Predicting root causes with Keras
14 Automating helpdesk functions
15 Latent semantic analysis (LSA) and latent semantic indexing (LSI)
16 Data for the help desk
17 Building a document vector
18 Creating the LSI model
19 Recommending FAQs
20 Time series forecasting
21 Recurrent neural network (RNN) and long short-term memory (LSTM)
22 Preparing sequence data
23 Building an LSTM model with Keras
24 Testing the time series model
25 Forecasting future service loads with Keras
26 Anomaly detection
27 Predicting alerting
28 Incident categorization
29 Spam filtering
30 Network traffic analysis
31 Model development best practices
32 Using machine learning platforms
33 Model serving best practices
34 Security and privacy best practices
35 Next steps
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