Expanding the possibilities of machine learning with MLOps

In the Defense community where data has been identified as a strategic asset, machine learning presents exciting possibilities, from streamlining logistics to predicting adversarial activities. However, machine learning’s biggest immediate gains for the defense community will be in the democratization of ML capabilities that improve the efficiency and reliability of daily activities.

When ML insights are combined with DevOps practices, the power of emerging algorithms can be amplified for cross-governmental impact. DevOps practices allow agile software communities to collaborate on dynamic goals by creating repeatable, test-driven microservices that can reduce repetitive efforts and the coding errors that come with them. Machine learning now has its own version of DevOps — MLOps—with open source and customer-focused or contractor-specific variations.

MLOps can empower the Defense community to rapidly create trustworthy and reliable solutions in a resource constrained environment. MLOps architectures help organizations build models collaboratively through feature engineering, model creation and model deployment tools that support broad code re-use while capturing powerful metrics and visualizations throughout the modeling lifecycle.

An MLOps centralized code repository reaches beyond individual projects, democratizing access to re-usable state-of-the-art data wrangling and ML architectures. MLOps propagates the knowledge of focused research engineers and allows organizations to broadly improve the reliability and performance of their modeling solutions through automated data and model optimization and accuracy analyses. Model trustworthiness is improved as engineers choose between models based on clear model explainability reports that tie model predictions to their most relevant input features.

The choice of an MLOps platform is a critical first step toward an organization’s rapid proliferation of transparent, trustworthy and effective modeling solutions. Platforms vary significantly in their initial feature and model engineering options and in the ability to customize solutions. It’s critical to identify the MLOps functionality that aligns with each organization’s needs.

Plan an approach based on transparency and trust

All MLOps solutions should help users clean and understand their incoming data sets, automatically create and tune appropriate ML models — called AutoML — and then retain a history of model training experiments.

A modeling platform should aid engineers in a full exploration of their training data sets, noting and addressing correlations, imbalances, inconsistencies and sufficiency issues. User-friendly interfaces that support insights into and methods addressing data set improvements may not be the norm across MLOps solutions, and feature engineering functionality warrants careful evaluation by potential end-users.

MLOps software suites are designed to interact with engineers’ ML experimentation efforts, retaining a history of the most effective modeling experiments. The best MLOps interfaces will provide a clear record of an engineer’s experimentation and help the engineer to create and identify a model that has consistent accuracy across critical domains and explainable performance rationale. Here, the differentiator between platforms may be visualization tools that enable performance and explainability explorations across data classes or subdomains.

The right MLOps solution can even protect future uses of an ML application through cloud-based model deployment options. Alerts can identify when models are used to create predictions based on data outside of high confidence areas. They can identify model drift — when an abundance of model evaluations occurs outside of initial training — so teams are made aware of the need for updated training data sets.

Ideally, user experience experts are leveraged to create an MLOps environment that alerts engineers to rough spots and opportunities within their model training efforts. The right visualizations and alerts embedded into the flow of an MLOps experiment can ensure that an engineer rushing to meet a deadline does not miss important considerations that undermine a solution’s efficacy. User-friendly tools capture insights that ensure newcomers to ML are guided through data preparation and model interpretation, with guardrails that lead to a more trustworthy product. Ease and clarity of use in the eyes of junior engineers is paramount in choosing an MLOps solution.

Tailor the MLOps approach

An organization’s secret sauce may lie in the machine learning architectures that solve their unique problems. While some data and modeling needs are ubiquitous, each organization has proprietary data sets and challenges, and associated modeling needs. A one-size-fits-all tool may offer the most appealing functionality at the beginning of an MLOps journey but may fall short of an ideal MLOps solution that has been customized over time. It may also be important that proprietary solutions can be shared collaboratively, perhaps with access control, as part of the MLOps solution.

Organizations with unique challenges must adopt an MLOps platform that enables their lead engineers to easily test, develop and persist new feature engineering opportunities and ML architectures that are responsive to emerging research. An organization’s modeling environment should pave a path over the hurdles and pitfalls that are unique to their needs. For example, an evolving MLOps environment might arm engineers against problems such as ML brittleness, where two almost equivalent data records yield radically different model predictions, or data leakage, where hidden information within training and validation data sets lead to results that are not generalizable to a deployed model. In both cases, top engineers can bake in remediation approaches geared towards its proprietary data and modeling goals, benefitting solutions created throughout the organization.

Commercial off-the-shelf products can provide quick solutions populated with a wide array of standard models, but tailored solutions will empower your organization to work best with its proprietary needs and allow engineers to tackle issues within a focused domain.

As the defense community works to transform expansive sets of data into actionable insights, MLOps allows organizations to proliferate the knowledge of top engineers and support the rapid development of trustworthy solutions. A well-chosen MLOps platform creates an accessible entry to inexperienced engineers while providing state-of-the-art, customizable solutions as defined by experienced subject matter experts. Starting the MLOps journey with a tool that can grow and adapt to meet each organizations’ specific needs will allow teams to evolve beyond the relatively simple models that automate our daily work, in turn allowing the focus to remain on the significant challenges of the future.

Elsa Schaefer is a corporate data scientist at LinQuest.

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