DEVOPSdigest asked DevOps experts — analysts and consultants, users and the top vendors — for their predictions on how DevOps and related technologies will evolve and impact business in 2019. Part 6, covers DevOps Analytics, including AI and Machine Learning.
Start with 2019 DevOps Predictions - Part 1
Start with 2019 DevOps Predictions - Part 2
Start with 2019 DevOps Predictions - Part 3
Start with 2019 DevOps Predictions - Part 4
Start with 2019 DevOps Predictions - Part 5
AI DRIVES DEVOPS
The rise of AIDev and AIOps with the use of analytics will continue to dominate the conversation around the DevOps 2019 landscape.
VP of Product Marketing Management, CollabNet VersionOne
AI in software development will gain mindshare. 2019 will see an exponential increase in the number of research projects and companies building solutions that leverage AI to increase developer productivity. We expect by 2020, all development will be assisted by AI co-developers that understand developer intent, suggest next best patterns and detect problems before applications go into production. This will enable companies to continuously improve their digital experiences and respond to market needs at a pace that was impossible before.
Head of AI, OutSystems
AI and automation will change the economics of IT. Much of DevOps is still driven by people, even if the infrastructure itself is becoming programmable. But data volumes are growing so fast and applications evolving so quickly, the infrastructure must be nimble enough that it doesn't become the bottleneck. We've already replaced many storage and network admins. In 2019, infrastructure will become increasingly programmable, and AI-based machines will predict storage and compute needs and allocate resources automatically based on network conditions, workloads and historical patterns.
Co-Founder and CEO, Portworx
MACHINE LEARNING BECOMES MAINSTREAM IN DEVOPS
Machine learning in DevOps will hit the mainstream in 2019. Capabilities like anomaly detection will become commoditized to the point that they are consumable by the DevOps masses, not just data scientists. Machine learning technology will mature and common use cases will be fleshed out enough that it will be common essential to any maturing DevOps toolbox.
CTO and Co-Founder, OverOps
AI AND ML CONVERGE WITH AUTOMATION
For the past several years, the role of automation in DevOps has continued to gain traction as an important aspect of the larger practice. Currently, the focus has mainly been on automating manual repeatable tasks that are process or event driven, but new advancements with artificial intelligence (AI) and machine learning (ML) show that change is on the horizon. Through the convergence of AI and ML, automation has the potential to showcase unprecedented intelligence as new systems will look at trends, as well as analyze and correlate across entire value streams to predict and prevent issues. As DevOps practices focus on increasing operational efficiency, this upcoming convergence of ML, AI and automation will present a significant advantage for companies utilizing DevOps. New and adaptive automation systems will give companies a competitive edge as teams following DevOps practices will be able to make real-time decisions based on real-time feedback.
Director, Product Marketing, Micro Focus
REDUCTION IN TOIL AND GUESSWORK
In 2019 we predict a significant reduction in toil and guesswork. Automation has done a tremendous amount to eliminate redundant repetitive tasks but there is still a great deal of toil that exists for DevOps practitioners. 2019 will see an increase in innovation to reduce guess work and toil that occurs when debugging issues. Hypotheses are educated guesses, but these aren't backed by data. The lack of data often results in a lot of missteps or trial and error. Improved visualizations and dynamic instrumentation of tracing data will help guide people to what issues, code-specific or IT ops-related, need to be addressed to resolve a problem. These innovations will help reduce the cognitive load and toil that occurs during incident resolution.
Director of Product and Solution Marketing, Catchpoint
In 2019, the pressure on IT to continually improve the business value of software will increase. As a result, organizations will look for ways to mine the large amounts of machine data created by their delivery process, expanding beyond postmortem analysis to include predictive DevOps. This approach will allow them to better anticipate problems in their delivery flow and infrastructure that could negatively impact application quality and security and slow down time to market.
VP of Product Marketing, XebiaLabs
DevOps tools will continue to incorporate more AI. This might be for analyzing large amounts of DevOps data, such as test results and log files. It can also be used to predict what next steps a DevOps user is most likely to take, and surface those options.
AI AND ML WILL FIX DEVOPS SYSTEM FAILURE
One of the biggest challenges today’s DevOps teams face is application and system failure. In 2019, we will see AI and ML enable computers to go beyond predictive analytics and begin directly taking corrective actions. This advancement has the potential to enable businesses to significantly reduce the need for human intervention to rectify frequently-occurring missteps and, furthermore, work toward a solution for this common DevOps problem.
Product Manager, Big Data Analytics Practice, Impetus Technologies
DataOps will be the new DevOps. As organizations have shifted toward self-service, data analysts now have the right tools to wrangle and analyze their own data instead of endlessly iterating with IT. But after this shift occurs, then the question becomes, how do you make such operations scalable, efficient, and repeatable? Enter DataOps. As an adaptation of the software development methodology DevOps, DataOps refers to the tools, methodology and organizational structures that businesses must adopt to improve the velocity, quality, and reliability of analytics. Data engineers fill the critical roles powering DataOps and, as these practices become commonplace, data engineers will become critical resources. In fact, 73% of organizations polled said they planned to invest in DataOps this year. In the same way that DevOps engineers are a highly sought-after role today, we predict that data engineers will be in the near future.
Co-Founder and CSO, Trifacta
All DevOps strategies have universal goals: agility, faster deployment, increased end-user experience, and “smart” operational decision-making. With DevOps transitioning from a hype to a standard practice in agile IT departments, technology and operations professionals striving to add value to their businesses should consider the next step in enhancing their departments: DataOps. In today's increasingly digital world, data cannot be excluded from the agile decision-making process. In fact, we predict that 2019 will be the year that data is recognized as a key business driver. “Data Culture” will become increasingly implemented into tech environments, and organizations will become data-driven and data-first. This shift will also give rise to DataOps as traditional admins start to understand that their days of tuning indexes are ending, one page at a time. Operations teams must adopt a “data mindset” to discern the type of data that exceeds their department and can be polished into something that adds value to the business overall. With DataOps, organizations can begin to transition their IT team into a data science team, as they adopt a data-first frame of mind. DataOps can help the C-suite operate their businesses more effectively by extracting and analyzing the most pertinent pieces of data and distilling and crafting them into a compelling and “business-digestible” narrative that can be easily understood across the organization. Companies will begin to actuate on this data, not just report and track in Excel — they will start using valuable data to make more informed decisions. The ability to share this actionable, business-digestible narrative may even earn tech pros a seat at the strategy table.
Head Geek, SolarWinds
CONTAINERIZED MACHINE LEARNING
Containers find a new use case: machine learning. Containers and machine learning are two of the hottest trends in tech, but they aren't often talked about together. In 2019, that will change as businesses start to recognize the benefits of running machine learning workloads in a containerized environment. There are several benefits to combining these technologies: The ability to cluster and schedule container workloads allows containerized machine learning applications to scale easily. And machine learning processes that exist inside of containers can be exposed as micro-services, allowing them to be easily reused across applications. As enterprises look to cognitive technologies to transform their businesses, they will see the benefits of bringing machine learning workloads to highly flexible and scalable cloud-native environments.
VP of Product Management, Portworx
AI AND ML SOLVE BIG DATA CHALLENGE
DevOps tools have made it easy to instrument, but it's getting harder for engineers to ensure they're making the right decisions with all of that data. Advanced statistical models, machine learning and other artificial intelligence capabilities will become even more critical to guiding engineers to the right problem at the right time and avoid data deluge.
Senior Product Marketing Manager, New Relic
MEASURING DEVOPS ROI
With greater investments being made in DevOps, management will demand more evidence of ROI. Teams will need to build their ability to measure DevOps ("DevOps Intelligence") based on a combination of global measurements (not just a single team or individual) and outcomes (delivery of software with speed and stability).
DevOps Advocate, XebiaLabs
AI AND ML HELP INCREASE VALUE
A top tech trend of 2019 will be the impact machine learning/AI have on the quality of software. In the past, we've designed delivery processes to be lean and reduce or eliminate waste but to me, that's an outdated, glass-half-empty way of viewing the process. In 2019, if we want to fully leverage ML/AI, we need to understand that the opposite of waste is value and take a glass-half-full view that becoming more efficient means increasing value, rather than reducing waste.
Read 2019 DevOps Predictions - Part 7, covering the Cloud.