Spectro Cloud announced Palette EdgeAI to simplify how organizations deploy and manage AI workloads at scale across simple to complex edge locations, such as retail, healthcare, industrial automation, oil and gas, automotive/connected cars, and more.
With companies of all sizes right from upcoming startups to well-established enterprises incorporating DevOps practices into their workflows, it is intriguing to uncover what the future of DevOps is going to look like.
3. Shift in DevOps metrics
According to a Forrester survey, currently, the DevOps teams measure velocity as the primary metric which is then followed by business value. As we move forward to 2020, the business-focused metrics would take precedence over velocity as the primary metric for measuring DevOps success.
DevOps would continue to bridge the gap between the IT and business teams, moving both out of their respective silos. Business value would overtake velocity as the key metric to track the efficiency of the development process.
4. Artificial intelligence in DevOps
The number of AI-driven applications being built is on a steep rise which is propelling the data science teams to include DevOps practices in their workflows. DevOps creates an ideal environment for artificial intelligence and machine learning. Teams are looking to AI/ML for discovering possibilities for DevOps automation within their workflow streams.
An extensive amount of data is being constantly produced which allows analytical technologies like AI and ML to detect patterns and make accurate predictions for a smoothly running CI/CD pipeline. The more data that is produced, the more accurate the predictions get thus facilitating pipeline automation and testing of multiple deployed models in the production chain.
By analyzing the log reports and performance flow data of the DevOps process, AI can introduce intelligent automation in your DevOps pipeline contributing towards the improvement of the existing DevOps pipeline and accurately predicting faults beforehand. In the future, DevOps engineers may not need to write the code as AI would take actions based on its processing of log reports and have the ability to generate code through an automated service.
5. Surge towards DataOps
DataOps aims at enhancing the collaboration between the developers, data scientists, data engineers and operations teams by removing silos between them. The key benefits that DevOps provides in the field of software development remain constant in DataOps which is applied in the field of data analytics.
The current approach to data management is outdated but DataOps offers a solution to the business’ data woes. By application of Lean, Agile and DevOps principles to the data management process and architecture, DataOps promises to expedite the time to value while improving the quality of data available and ensuring data governance.
Business both large and small are realizing the value of data and the importance of making data-driven decisions. As 2020 rolls around, we are likely to see a surge in DataOps adoptions across the industry. As data becomes the chief driver of applications through collaboration of the stakeholders involved, businesses would leverage the same and optimally utilize their potential for more rewarding outcomes.
Out of our list of the upcoming DevOps predictions for 2020 which seems likely to leave the most impact? What do you think would be the most promising DevOps trend in the coming year?