Accelerated DevOps with AI, ML & RPA
By Stephen Fleming
Length2h 58m
About this audiobook
What comes to your mind after reading the below statements from a renowned industry research firm?
It is predicted that a large enterprise exclusive use of AIOps and digital experience monitoring tools to monitor applications and infrastructure will rise from 5% in 2018 to 30% in 2023.
Also, Only 47% of machine learning models are making it into production (Comes MLOPS!)
Do you have similar thoughts?
Is it just a new Buzzword or repackaging of the existing system? If it’s for real, how is it going to impact the Business/Industry?
How my business or job would get impacted?
If it has just started, how can I leverage from wherever I am?
Which are the major players/startups in this area?
Depending on your role, it may be useful for you to know about AIOPS & MLOPS:
If you are a Business Consultant trying to make the system more efficient and profitable, reaping the benefits of Automation in your application development process
If you are a Technology Consultant and want to make your operation more Agile, Automated and easily deployable
If you are a Technology Professional looking for a role in these upcoming areas to be an early adopter in your organization or just starting your career and want to understand the ecosystem
If you are from HR or Training field and want to understand the job/Training requirements for these upcoming roles
Beyond the apparent hustle and bustle of buzzwords and nomenclature every year, I genuinely believe that AI would drastically change the software development and deployment model in the next two years, and all these new startups would drive this change.
It’s astonishing how fast this cycle is moving. Especially for us who had seen the world before the internet came into our daily lives!! This book is my attempt to update you on the unfolding story of AIOPS and MLOPS as “story till now. “
So here is to our Continuous Learning and Progress! Cheers.
Audiobook details
Rating★★★★ 4.0 (10)
Includes Goodreads
GenreTechnology, Science and Nature
Length2 hrs 58 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
Publish dateMar 13, 2020
LanguageEnglish
Table of contents
1Automation in Classical DevOps
2Intelligent DevOps: Era of Smart Automation Landscape
3A Peek at Intelligent DevOps
4Cross-Life Cycle DevOps Intellect
5The new DevOps with AI & ML
Mostrar todos os capítulosVer menos
6AI in DevOps
7Machine Learning (ML) impact on DevOps
8Techniques AI and ML Will Change DevOps for the Better
9DevOps with Cloud and AI: Trinity of Automation
10The need for intelligent ways for IT infrastructure
11Symbiotic adoption of AI, Cloud, and DevOps
12DevOps with Emerging Technologies like Cloud, IoT, Blockchain, AI/ML, Containers, and DevOps
13AIOps Enters the Scene: Right here is just how AIOps can assist your IT division:
14Deploying AIOps allows achieving the following positive results:
15Ideas on What AIOps is and also why it is necessary
16Artificial Intelligence to increase DevOps Efficiency
17Artificial Intelligence for Agile/DevOps
18Software application's AI idea
19AI comes to DevOps pipeline through containerization.
20Why we need AI intervention in DevOps pipeline.
21AI adoption can get made complex
22The tools you'll need
23AI DevOps tools are involving market in droves
24Kubeflow's spot in this world
25How to get going
26AI in DevOps- Use Instances
27Review software application testing performance
28How to start AIOPS in the existing setup?
29The Next Generation of DevOps: ML Ops
30The Rise of ML Ops
31Using ML Ops to Your Company
32DevOps for Machine Learning
33ML for Application: development course as well as a data science course.
34Using ML to DevOps
35Applying Machine Learning to DevOps
36In the method, some key examples of applying ML to DevOps consist of:
37How Machine Learning Can Assist
38The Present State of Machine Learning in DevOps
39Barrier # 1-- The Machine Learning Skills Space
40Obstacle # 2-- Business Challenges
41Considering the Future
42Exactly How to Enhance DevOps with Machine Learning
43ML as a rescue ranger for DevOps
44Machine Learning Use Cases in DevOps
45Application tracking
46Application high-quality assurance
47User actions patterns
48Operation administration
49Alert monitoring
50Troubleshooting and analytics
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