DevOps for Machine Learning

Stabilize and streamline model release pipeline, through Continuous Integration and Deployment

Why DevOps for Machine Learning?

DevOps places an emphasis on process, automation, and fosters a culture that encourages new ways of working together across teams. It application-centric paradigm that focuses on the platform, instrumentation, and process to support applications. AI/ML teams use an approach unique to machine learning projects where there are frequent, small iterations to refine the data features, the model, and the analytics question.

Model Iteration

The ML process relies on the iteration of models and it can take hours or days for a model to train. Carve out a separate workflow to accommodate the timelines and artifacts for a model build and test cycle.

Model Lifecycle

For ML teams, models have an expectation to deliver value over time rather than a one-time construction of the model. DevOps helps in adopting practices and processes that plan for and allow a model lifecycle and evolution.

Consistency

DevOps is often considered as bringing together development, release, and operational expertise to deliver. DevOps ensure that ML is included throughout the design and development sessions.

Challenges in Machine Learning Projects

Model Training

Ability to retrain or even recreate the model from scratch to make it sustain once it is integrated into a customer facing process.

Data Management

Properly defining security rules allowing the learning application to access the private and sensitive data through access controls for training.

Testing models

To ensure that the changes in ML models are not negatively impacting the way system behaves in deployment environments.

PWSLab to the rescue

Easy model training

PWSLab DevOps is capable of training models with minimal to no human intervention, dependencies and code can be packaged in a standard way.

Secured Data Access

Secured access to the confidential data can be achieved by running a PWSLab CI/CD runners inside private networks for training models.

Accuracy tests

An accuracy test exercises the inference code for a test data sample for a model to verify that the output is above an expected threshold.

Benefits of PWSLab in Machine Learning

DevOps for AI/ML has the potential to stabilize and streamline the model release process. It is often paired with the practice and toolset to support Continuous Integration/Continuous Deployment (CI/CD).
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Fast Model Training

Fastest time to train, with as much data and as accurately as possible. End-to-end pipelines for publishing changes from Development to Production.

Faster inference

Fastest time to inference, with the ability to rapidly retrain. With built-in monitoring and config management, environments are consistent throughout the delivery.

Safe Deployments

Safe and reliable deployments to observe model behavior in the real world. Automated pipelines distribute code, data-sets and models through a customized workflow.

Version Controlling

Version control of all code and models allows to concurrently run multiple versions of models to optimize results. Multiple environments can run different revisions of code with consistency.

Steps involved in Machine Learning Pipeline

Using our methodology and philosophy of implementing DevOps an organization can migrate to DevOps in six simple steps.
  • 1.

    Data preparation

    There are two (equally consequential) inbound components: code and data. Data preparation, part of the data management process, involves collecting raw data from multiple sources and consolidating it into a file or database for analysis.

  • 2.

    Model training and testing

    The process of training an ML model involves providing an ML algorithm (that is, the learning algorithm) with training data to learn from. The term ML model refers to the model artifact that is created by the training process.

  • 3.

    Model evaluation

    Model Evaluation helps to find the best model that represents our data. Evaluating model performance with the data used for training is not acceptable because it can easily generate overoptimistic and overfitted models.

  • 4.

    Deployment and versioning

    Continuous deployment is the latest automation that can really save you a lot of time when developing, testing and pushing to production your code. Instead of manually building your application and uploading to a server.

  • 5.

    Production and monitoring

    Production machine learning systems require constant monitoring, not just to keep the system online but also to ensure the model inference results are correct.

  • 6.

    Continuous training

    Sequential and continuous learning by injecting new knowledge without forgetting previous knowledge is a domain of machine learning which tries to mimic the human cognitive system.

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