Data & Model Lifecycle Management (ML Ops)

Advances in the lifecycle management of AI solutions ill be necessary to accelerate their development while maintaining efficiency and quality delivery. To the effect of accelerating advances in data and & model management, pricing is a critical process that has a direct effect on company profitability and when companies use algorithms they need to make sure that these algorithms are robust and operating like a diesel engine in operations.

The three main drivers for Data & Model Lifecycle Management (ML Ops) are:

  1. Model Monitoring & Management: Keeping an eye on model and data ‘drift’ (change from desired settings) as well as different measures of model performance against agreed-upon thresholds and benchmarks.
  2. Model Governance: Keeping audit trails for data and model changes, model access control, and transparency into how models use data and any regulatory or compliance needs for model usage.
  3. Model Security: Protecting models from being corrupted by tainted data, being overwhelmed by denial of service attacks, attacked through adversarial means, or being inappropriately accessed by unauthorized users.

Agile focuses on processes highlighting change while accelerating delivery. CI/CD focuses on software-defined life cycles highlighting tools that emphasize automation. DevOps focuses on culture highlighting roles that emphasize responsiveness.

Continuous Integration (CI) is a development practice where developers integrate codes to an orchestrator. The integration phase is the first step of the pipeline and the key benefit is to locate errors quickly. Continuous Deployment (CD) focuses on automatic deployment for models.

Components of CI/CD:

1. Plan: This is the part where we decide on which components of CI/CD are applicable to the way the pricing algorithm will be built.

2. Build: We combine the source code and it’s dependencies to build a runnable instance of our product that we can potentially ship to our end users. Programs are written in languages like Python.

3. Test: In this phase, unit testing and integration test are the most vital parts of the pipeline. We run automated tests to validate the correctness of our code and the behaviour of our product. The test stage acts as a safety net that prevents easily reproducible bugs from reaching the end-users.

4. Release: The process of planning releases and making the decision to launch a release to production.

5. Deploy: Once we have a built a runnable instance of our code that has passed all predefined tests, we’re ready to deploy it. There are usually multiple deploy environments, for example, a “beta” or “staging” environment which is used internally by the product team, and a “production” environment for end-users.

6. Monitor: Maintaining environments for different stages of a build such as a system integration.

CI/CD helps you control your inbound data because your inbound data defines its output of your pricing algorithm. CI/CD helps you control the way you set your pricing algorithm, through this you understand what the code does in the model. For example, when an expert makes a model, the application of the model to the data set becomes efficient and can be monitored on the basis of the desired outbound through the pricing algorithm.

Data & Model Management will become a key consideration of Pricing Officers as it is critical to ensure quality, speed, and reliability of AI-based pricing solutions in operations. If you think your company needs to make sure that the algorithms are robust and operating like a diesel engine in operations then contact us!