DevOps for AI: Make AI Operationalization a Core Business Objective!
Catalyzed by the pandemic, the speed of the shift to the cloud has moved from years to months or even days. Out of the blue, it has become imperative for every company to migrate to the cloud.
The speed, quality, scalability, and flexibility offered by the cloud have made it a haven for businesses, amid the crisis. And to buttress this rapid migration, AI implementations and intelligent automation are burgeoning.
Artificial intelligence solutions have dramatically transformed the traditional ways of transforming business. As a result, AI has fundamentally changed how businesses function today, impacting everything from customer interactions via virtual assistants to process automation and network security monitoring.
Due to the exponential rise of diverse tools and technologies that underpin modern applications, there are more components to manage. Managing more resources may be extremely tedious and challenging, mainly manually, for the DevOps team. DevOps is becoming increasingly dependent on artificial intelligence, and machine learning as more automation and handling of new technologies are needed.
DevOps team may reduce technical debt and improve project requirements using AI. For instance, test coverage technical debt can reduce with the aid of AI-based testing. DevOps AI teams will be able to run tests on their systems and services without having to allocate resources that are not required.
The capacity of AI and machine learning to help manage alerts from applications or infrastructure is another reason DevOps needs them. For instance, AI can assist in prioritizing alert responses based on data obtained from past behavior, the signal’s strength, and its source.
DevOps team can then enhance their alerting systems to automatically differentiate between unimportant alerts and issues that demand quick action. Furthermore, as DevOps automation would spend less time combing through logged data captured from monitoring services to pinpoint the issue’s root cause, machine learning DevOps algorithms that are trained to monitor services can aid in the remediation of critical issues more quickly.
However, here comes the trouble in paradise. How can you power up your AI delivery model? How can you integrate AI into your organization’s core business processes? Chillax! We have DevOps.
DevOps for AI
Just as DevOps brings speed, scalability, and flexibility to software development, one can leverage DevOps principles to ignite AI model delivery. When applied to AI, a DevOps environment ensures that optimal AI delivery processes are deployed.
As computing has grown more affordable and the amount of data in the network has exploded with the introduction of social media and mobile, digital AI has recently come to attention. Leveraging data is a goal of AI development. All social media platforms and search engines currently incorporate AI in one way or another.
AI DevOps is used extensively in the robotics, automotive, and manufacturing industries to produce new products, including driverless automobiles, drones, robots, and more. DevOps AI will accelerate product releases with quality and effectiveness.
It enables AI to scale by integrating machine learning models from design to production. DevOps for AI facilitates flexibility and adaptability, advantages which are imperative during these times of uncertainty.
Moreover, DevOps trends enable continuous delivery, deployment, and monitoring of AI models with:
- Speed: Accelerate time-to-market by axing non-value-added activities in AI delivery
- Quality: Cultivate the culture of ‘fail fast and learn fast’ approach to enhance AI model quality
- Scalability: Anticipate scalability requirements and make sure AI models can scale on demand
- Reliability: Continuously monitor deployed AI models to keep them robust and reliable
How to Implement AI in DevOps
When incorporating AI into DevOps, the following considerations are crucial:
1) Disruption Possibilities
AI systems may change present corporate procedures and paradigms as they proliferate. For instance, if a company employs AI to automate customer care tasks, it may need to review its workforce and business model. Additionally, when corporations contemplate the implications of AI-generated judgments, AI systems may give rise to legal issues.
2) Data Management
Data management is often the most expensive and challenging part of using AI. DevOps AI teams need access to datasets, which can be expensive and time-consuming to collect and organize, to train AI models. Furthermore, digital AI systems require ongoing maintenance and advancements as new data is gathered and new problems are discovered.
3) Quality of the Data
Effective AI requires high-quality data to operate. But conversely, accurate data can prevent AI systems from making incorrect decisions.
4) Ethical Issues
AI systems will make more decisions that impact people’s lives as they become more sophisticated. For instance, AI systems may use to determine who is eligible for a loan or who is a strong candidate for a post. AI DevOps systems’ morality must be ensured because their decisions may significantly impact people’s lives.
Benefits of DevOps with Artificial Intelligence
1) Improving Customer Engagement
AI can help the DevOps team improve customer satisfaction by providing insights into how customers utilize a product or service. For instance, AI can identify consumer pain points and propose changes to improve the customer experience.
2) Optimization of Workflow
AI can help the DevOps team optimize workflow by identifying inefficiencies and bottlenecks. For instance, if a given task takes longer than usual, AI can analyze the process and suggest solutions that might increase efficiency.
3) Checking the Functioning of the System
Artificial intelligence (AI) makes it feasible to track system performance in real time and identify potential issues before they arise. Using AI, the DevOps team may stop problems or resolve them before they affect clients.
4) Automating Repetitive Operations
Artificial intelligence (AI) can help the DevOps team automate repetitive operations such as provisioning and configuring resources, deploying applications, and monitoring infrastructure. As a result, the DevOps team might have more time to devote to strategic tasks.
5) Cost Reduction
With the automation of tasks and the optimization of workflows, AI may help the DevOps team reduce costs. For instance, when a task is automated using AI, fewer workers may be needed to do it, which can reduce costs.
How DevOps Helps in the Operationalization of AI?
Meeting the demand for AI implementations is a tough row to hoe, for the best practices around AI delivery are constantly evolving. However, this challenge can be addressed with the principles of DevOps. DevOps environment facilitates a repeatable and adaptable methodology to improve the maturity of AI delivery.
Here are four stages of how DevOps enables AI operationalization:
1) Preparing Data
As AI model accuracy depends on the quality of datasets, preparing the right datasets is a crucial step in acing AI models. A typical data preparation process includes data extraction, data filtering, data classification, and data validation. However, this is a manual and tiring task typically takes almost 70% of a data scientist’s time. And here comes the future of DevOps.
DevOps transformation for AI automates the manual process of data preparation. It enables data pipelines to handle bulk amounts of data with ease. This ultimately improves the quality and quantity of the training datasets. Moreover, it relieves data scientists from the herculean task of data preparation and lets them focus on other crucial tasks.
2) Developing AI Model
Developing an AI model is an iterative and time-consuming process. Typically, it involves three crucial activities: feature engineering, algorithm selection, and dataset training. Achieving optimal model development escalates the need for multiple rounds of model training. Traditionally, this process takes place in the local workstations of data scientists without much collaboration among various AI teams.
DevOps accelerates AI model development by facilitating the room for simultaneous development, testing, and model versioning. This axes the time and effort required to achieve an optimal AI model.
3) Deploying AI Model
Many organizations experience severe headwinds while deploying an AI model in production. Typically, challenges surface when individual data scientists deploy an AI model developed in their local machines. AI models perform well in production only if they can handle a high influx of data in real time on highly scalable and distributed platforms.
DevOps practices make AI models portable and modular, paving the path for operationalizing AI.
4) Monitoring AI Model
Typically, AI models are based on historical datasets. However, as time passes, the data and the model become obsolete. This leads to a decrease in model accuracy, resulting in ‘model drift.’
DevOps environment for AI enables continuous monitoring of data and model performance indicators to ensure the model stays relevant for a longer time. This enables more robust and responsive AI solutions.
Use Cases of AI in DevOps
Complete automation throughout the project lifecycle is the ultimate goal of DevOps. While total automation is still a long way off, we may work towards as much automation as possible, not just inside one phase or tool.
We employ AI to break down data silos inside the toolchain, facilitating automated analysis, logging, and metric data collection—for instance, the correlation of all pertinent data throughout a toolchain. More speed, more accurate root-cause analysis, and predictive insights obtained from the entire toolchain as opposed to just one particular tool or data source are all advantages of automation.
2) Monitor the Delivery of the Application
Visibility into the entire application delivery process is made possible by activity data from DevOps technologies (such as JIRA, Git, Jenkins, SonarQube, Puppet, Ansible, etc.). To find many of the software development’s “wastes,” you can use machine learning to find anomalies in the data, such as high code volumes, long build times, poor release rates, and late code check-ins.
3) Increased Collaboration
The DevOps paradigm is based on the principle of collaboration, which is a crucial one. The IT, engineering, and operations teams must be able to freely exchange knowledge on the most effective ways to run applications and systems. Consistent communication and cooperation follow from this.
By giving all project stakeholders access to a single perspective from which pertinent toolchain data can retrieve, we can leverage AI to improve cooperation within a DevOps team. AI also records information about how systems and applications should function as it is generated. When necessary, such as when alerts or abnormalities are discovered, ML algorithms then make this knowledge visible.
4) Pattern Discovery
A highly efficient and automatic method of finding new information in logs is provided by pattern discovery, which finally transforms boring and routine data into actions. For instance, a log file may contain a pattern of connections coming from multiple source IP addresses that look random at first. However, when shown, this pattern can be perceived as a new exploitation tool being discreetly tested on a corporate network. The security team or the entire community may then take a series of actions.
5) Analysis of Trends and Summary
Trends and summaries are frequent outcomes of log analysis. For instance, a lengthy log file could condense into a brief list of the “Top 10 Attacks” or “Top Suspect IP Addresses,” among countless more valuable summaries. Moreover, such a concise assessment frequently leads to a response. It can be clear from a “Top Bandwidth Users” report, for instance, that the top three users in the organization use 90% of the bandwidth that is available.
If such bandwidth is used to download unrelated content or to exchange files via P2P, this might swiftly result in disciplinary action. Similarly, monitoring a router’s CPU usage record over an extended period may indicate unusually high activity, which could prompt an investigation to find evidence of attacker communication with a compromised system.
DevOps transformation for AI is gaining momentum as the optimal solution for organizations looking to embrace AI integration, AI-driven innovation, and intelligent automation. It brings in standardized processes from data preparation to model development to make AI operationalization a reality.
Despite clear benefits, many organizations often fail to address AI operationalization. This is where Veritis comes in place. Now is the right time to make your AI operationalization a core business objective with Veritis solutions.
Reach out to learn how Veritis can help you drive your AI journey. With over a decade-long industry presence, our experts can help you assess and execute your end-to-end AI lifecycle management.