Building a Model Factory: The Future of Manufacturing with Connected Capabilities - Stefanini

Building A Model Factory: The Future Of Manufacturing With Connected Capabilities

The speed at which a manufacturer can develop, test and automate models is directly tied to how quickly a company can innovate and respond to the market. The Model Factory was created to automate mundane tasks by leveraging data insights, highlighting the emerging role of the data scientists in the manufacturing sector. How can a model Factory enhance smart production capabilities and Industry 4.0 benefits? Read on to learn more!

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What is a Model Factory?

Model factory is a software framework that is able to generate predictive maintenance and production models from raw data. This enables analytics and data science teams to move from a development model to a stable, reliable and auditable production model that works faster and with little guess work.

In the context of data science, this is a system or set of procedures that automatically generate predictive models with little or no human intervention. The framework can also help newer teams to formulate internal standards for measuring performance and releasing new model versions.

A model factory is not the result of a single software product but stems from a series of software that are linked together to support a recommended workflow with the larger goal of creating sustainable machine learning. However, while machine learning is a powerful tool, implementing these models for real-world applications can be difficult. According to IDC, over a fourth of AI and machine learning initiatives fail.

The challenges are wide ranging and can stem from lack of developer experience to poor data quality and difficulty implementing insights into operation. There is no shortage of options for machine learning tooling, processes, and frameworks and this is exactly why model deployments tend to be so difficult.

Further, the machine learning lifecycle is complex with several stages that their own set of challenges. Learning more about the core features that make the Model Factory a reality can help manufacturers anticipate and overcome these challenges in order to make this a reality.

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Model Factory Framework Components

Serving as an architectural pattern rather than a product, a Model Factory Framework is an open, modular solution that is agnostic of platform, tooling or framework which provides a cloud-based machine learning lifecycle management solution.

The framework architecture is designed to be flexible and allow companies to use software and servers that are already available and are supported by their IT departments, allowing integration with AWS services and other industry-standard automation tools.

Here are the four major components that compose the model factory framework:

1. Version Control: While Data scientists must collaborate in order to complete a project, the constant exchange of files can easily lead to lost work. If the latest iteration of the code is broke, it can mean hours of work to restore the previous version. Version Control systems are a common feature of developer cycles, but may be unfamiliar to most data scientists. This system is important because it allows data scientists to build new branches of code while optimizing and repairing older versions.

2. Orchestrator: While creating a production model, it is vital use a reliable scheduling tool that can pull the latest model version from a version control system, send notifications in case something gets broken and create some simple report about model run. Having these steps somewhat automated allows for a streamlined planning and collaboration.

3.  Compute Engine: Analytical services can be used as a compute engine within the Model Factory. Every computational process or job is given a unique session id that stores the results of each process in a unified way. This allows users to retrieve model and session id’s in order to review the version history and compare models from different time periods to each other.

Model scores, diverse model statistics such as variable importance and model quality metrics can be stored using the same identifier for the particular run so that data processes are stored together.

4. Storage: When operating with a data science or analytics team, it is necessary for them to create and agree on standard metrics or KPIs (i.e. Variable-Importance, False Positives, etc.) to measure a model’s stability, performance and input data quality. This ensures that every iteration of a model will be comparable to old versions, thus simplifying the definitive determination of whether an iteration is performance at a higher standard. Metrics should be calculated in the exact same way to ensure they remain comparable to previous versions.

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Emerging Roles that Enable Model Factories

The amalgamation of software, models, and data that make the model factory possible has prompted the emergence of new roles within the manufacturing sector. These significance of these roles are a product of the scoping, design, development, operations, and maintenance phases of the software lifecycle.

Here are some of these emerging roles:

  • Data Engineers: At the crossroads of software engineering and data management, Data Engineers serve to analyze and organize raw data in order to create reliable systems and pipelines that sort out and store process data. This is vital for converting data points into tools for interpreting trends and patterns across process models.
  • Data Scientist: This role has particular significance for the early phases of logistic regression model runs, having particular influence on model evolution. The core competencies in subject matter or domain expertise, mathematical and statistical analysis, and computer science all serve to create meaningful insights from the large quantities of data generated from model to model.
  • Machine Learning Engineers: The transition from generating initial logistical models to producing viable prediction models requires someone to scale and optimize the proof-of-concept models. ML Engineers take the models developed by Data Scientists and turn them into code. This enables models with predictive potential to be enacted in the production line.
  • Machine Learning Operation Specialists: As more Machine Learning models are developed, scaled, and deployed the task of preserving these models manually becomes impractical. As organizations move away from initial stages and toward the reality of a Model Factory, Operation Specialists serve to deploy and maintain Machine Learning systems. This ensures models are implemented into production a consistent and efficient manner.

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Benefits of the Model Factory Framework

The Model Factory Framework can help eliminated unnecessary steps in the machine learning lifecycle, accelerating and automating handoffs between the different teams and by enabling simplified troubleshooting. Further, the framework provides a model lineage for governance and regulatory compliance, improves time to insights, and accelerates ROI, while reducing effort to get ML models into production.

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