When they work together, software development and operations teams can advance a company's business transformation. The integration of these teams, also known as DevOps, streamlines the legacy software development process.
However, with the growing emphasis on digital transformation, the pace of development and innovation has increased. Therefore, the need for optimal orchestration in DevOps is rising, which requires innovation and advanced tools and technologies.
Typically, companies face significant challenges when running and managing DevOps teams. It involves large volumes of continuous data flow, which leads to complexities and inefficient data management. The manual effort to absorb and channel DevOps teams' data and information becomes incompetent in a dynamic application environment.
Another challenge for developers in today's rapidly evolving digital era is the inability to quickly build and deliver applications to meet customers' needs. To address these challenges, ML/artificial intelligence (AI) is emerging as a promising technology to transform and optimize DevOps processes.
The analyst has assessed the impact of ML on DevOps; this research service covers the following topics:
An Introduction to Machine Learning in DevOps
A Technology Market Overview
The Transformation of DevOps with Machine Learning
The Intellectual property (IP) Landscape
The Key Market Participants
Key Topics Covered:
1.0 Strategic Imperatives 1.1 Factors creating Pressure on Growth in Machine Learning: The New Driving Force for DevOps 1.2 The Strategic Imperative 1.3 The Impact of the Top Three Strategic Imperatives on Machine Learning: The New Driving Force for DevOps 1.4 About The Growth Pipeline Engine 1.5 Growth Opportunities Fuel the Growth Pipeline Engine
2.0 Growth Environment 2.1 Research Scope 2.2 Research Methodology 2.3 Research Methodology Explained
3.0 Technology Overview of DevOps and Machine Learning 3.1 Introduction to DevOps 3.2 The Integration of Machine Learning into DevOps Practices 3.3 Machine Learning Advantages over DevOps Data Analysis
4.0 Transformation of DevOps Workflows with Machine Learning 4.1 How Machine Learning is Optimizing DevOps Workflows 4.2 Machine Learning Model Training to Optimize DevOps 4.3 Impact of Machine Learning on DevOps 4.4 Key Benefits Offered by Machine Learning in DevOps
5.0 Technology Innovation and Use-Cases 5.1 High Scalability and Rapid Training of ML Models 5.2 Enhancement of Integration and Security Capabilities
8.0 Patent Analysis 8.1 Patent Landscape - Machine Learning/AI-enabled DevOps 8.2 IBM and Microsoft are Leading Patent Activities across the World.
9.0 Growth Opportunities 9.1 Growth Opportunity 1: Machine Learning-based DevOps is Driving Value Streams across Scaled Agile Framework (SAFe) 9.2 Growth Opportunity 2: Machine Learning is Enhancing Privacy and Data Governance for Complex DevOps Workflows 9.3 Growth Opportunity 3: AI-powered ChatOps is Improving DevOps Communication and Collaboration