Agile DevOps Overview – Incorporate AI (MLOps & Automation)

Measurements that Matter

A decade into the DevOps movement, organizations still struggle to scale improvements across teams. Our approach not only eliminates waste and empowers teams, but also extends to integrating AI solutions into your operations. In an age of cloud and machine learning, DevOps plus AI-driven automation is key to staying competitive.

DevOps Pre-Flight Checklist

Case Study

Synuma is a small company experiencing rapid growth. With that growth came the need to scale in a way that protected their competitive advantage. Delivering value to their clients was a key aspect of their product growth. Because of the time, impact to others, and the potential risks, the team was releasing about once a month. This significantly slowed the delivery of value to clients. Synuma’s leadership suspected that more automation (and eventually AI) would increase their release frequency and client value – would their small company realize the return on investment?

Download DevOps Small-to-Mid Case Study

ClearlyAgile provides insight into DevOps Maturity using our 4 step process

 
4 Steps Only.png

  • Evaluate infrastructure, release cycle and branching strategies

  • Set a baseline for DevOps maturity to measure improvements for Leadership

  • Implementing solutions based on recommendations to improve the process including code security, stability and automation

  • Integrate the DevOps team into the Agile development process

Continuous Delivery for AI

Just as we automate software delivery, we also help automate the lifecycle of AI models (MLOps). This includes setting up data pipelines, model training workflows, and deployment automation so your AI innovations move from the lab to production smoothly. Our DevOps assessments now cover AI readiness – ensuring your infrastructure can handle model deployments, data versioning, and the unique monitoring challenges AI brings.

APPLICATION REFACTORING

Application Refactoring is an approach focused at evaluating an application and architecting/redesigning for a microservices pattern or hybrid implementation that incudes microservices.

By refactoring into a modular architecture, you make it easier to plug in new capabilities – like AI services – without disrupting the whole system.

There are three (3) high level strategies to refactoring:

  1. Incremental – A piece by piece approach to refactoring.

  2. Large to Small – A stepped approach where the application is segmented into large chunks then reduced to smaller chunks over time.

  3. Wholesale Replacement – A complete refactoring of the entire application at once.

Each strategy should follow the basic methodology for refactoring projects:

  1. Preparation

  2. Microservice Design (Domains)

  3. Infrastructure & Deployment Design

  4. Refactoring

  5. Testing

Let’s Work Together

It all begins with an idea or a need – maybe you want to increase deployment frequency, reduce errors, or infuse intelligence into your apps. Whatever your goal, an Agile approach can make it a reality. Let’s discuss how to streamline your pipeline and incorporate AI for continuous innovation

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