How Artificial Intelligence and Machine Learning can optimize DevOps?

How Artificial Intelligence and Machine Learning can optimize DevOps?

DevOps boosted by AI/ML

Seven out of ten customers use DevOps and IT Services. Vendors are under tremendous pressure to fulfill clients’ evolving needs to create a self-healing system and increase automation. Sun Technologies uses AI/ML solutions to improve the efficiency of the DevOps Pipeline. With our tried-and-tested AI Solution, we help our customers create a self-healing system, reduce time-to-market, and improve efficiency using DevOps powering it with AI/ML solutions. 

In a few cases, it may be hard for some organizations to use AI and ML due to the complexities. Adapting to AI/ML solutions within DevOps is a cultural shift. 

With Machine Learning, several models can be created to analyze the DevOps metrics that include:

Our trained models help customers in: 

  • Analyze metrics
  • A deep-dive analysis of repeated failure
  • Executing automation sets
  • Predicting failure points before the occurrence

Based on the result-sets aggregated from the model, AI helps make automated prediction-based decisions to avoid failures.

Recent research states that around 85% of C-level officials trust the AI/ML can offer considerable value concerning accuracy and decision-making, prompting improved organizational productivity.

AI/ML on each phase of DevOps

There is a wide misconception across the industry in understanding DevOps. Automation is not the entire DevOps world, but just a yield. DevOps is a cultural shift for Developers, Business Users, Infrastructure engineers, and a few other key stakeholders. 

DevOps has various features that include Continuous Integration, Continues Delivery, Continuous Monitoring, Continuous Testing, and Continuous Security. AI/ML has its role in each feature sets. 

Sun Technologies helps customers build a cloud-agnostic and cloud-native DevOps pipeline and improve efficiency throughput using AI/ML solutions. We bring prediction, Learning, and Automation together. 

Few use-cases AI/ML within DevOps pipeline implemented by Sun Technologies includes: 

  • Automated Code rollback for wrong check-ins 
  • Automated Log analyzer to identify security threats such as intrusion, DDoS, DoS
  • Self-Healing Web-Application System
  • Alerting mechanism for potential (Futuristic Failures) 
  • Chatbot / Voice Automated Deploy Assistant
  • DevOps Advisor suggesting automation of repeatable tasks

DevOps optimization using AI/ML

  • The three well-defined capabilities AI brings are prediction, self-learning, and automation
  • AI and ML dispatch data with self-learning capabilities, making AI and ML techniques exceptionally advantageous if imbibed into the DevOps Pipelines
  • During SDLC, AI/ML can monitor and track production performance to which the end-user experience is being labeled by simulating different possible scenarios
  • With AI/ML incorporated into the DevOps process, the DevOps teams can know how the code is performing
  • AI allows managing the growing volumes of data in DevOps environments
  • With Chatbot/Voice assisted DevOps, Developers can check-in the code and make deployments with a single command 

Tangible benefits

  • Faster Time to Market using DevOps while AI/ML boosting it further to make it more efficient
  • Proactive decision making than reactive
  • Satisfied Business users
  • Low/No human intervention
  • Realistic Instant RoI
  • Adaptable and Maintainable DevOps pipeline
  • Huge Time Savings
  • Increased Efficiency

Conclusion

Enterprises can apply AI and ML to enhance their DevOps condition. AI can help predict complex data pipelines and make models that can enhance the application development process. However, implementing AI and ML for DevOps likewise exhibits various difficulties for enterprises.

Organizations envisioning DevOps have to set up a well-defined DevOps roadmap before full-fledged implementation. When the establishment is made, AI/ML should be viewed only as a booster to increase efficiency and effectiveness. AI/ML helps DevOps teams to concentrate on inventiveness and innovation by taking out negative aspects over the operational life cycle. It brings about automated improvement and expansion in the DevOps team’s effectiveness.

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Redefine Rebate Processing, Read Receipt Information Automatically for a Leading Retailer across USA

Case Study

Redefine Rebate Processing, Read Receipt Information Automatically for a Leading Retailer across USA

Whom we worked with

Our customer is a successful product and service company with a respected and established brand in the mobility and barcode solution industry, with over 28 years of experience.

Our Solution

  • Parse receipts and send the desired result in a web service to the customer
  • A framework was created with information on the various customer retail stores to facilitate accessibility and to determine the receipt of a particular store
  • Proposed a “MVC” architecture by enhancing the performance of the system
  • Automated test cases that compared the parsed receipts with the actual receipts to determine the accuracy of the developed application
  • The OCR (Optical Character Recognition) technology was used to convert receipts to a text format for parsing

Challenges

  • A faster process where receipts are read more quickly and accurately since the processing and approval of rebates has been slower due to manual intervention
  • To reduce the cost of the reading receipts process
  • To overcome the challenges of various format and data receipts

Impact

  • Customer was provided with the first phase of the application work to understand the technology used behind the application
  • Customer feedback and approval for the application developed

How we helped

  • Understand the challenges involved to read the receipts in accurate format irrespective of store and data
  • Created a process to define which receipts belongs to which store and parse them accordingly
  • Dedicated offshore team to support the application developed
  • Check the compatibility throughout with standardizing JAVA as the development language