Top 5 Obstacles Preventing Your Machine Learning Model from Deployment
In today's data-driven world, the development of machine learning (ML) models by data scientists is crucial for businesses. However, getting these models prioritised for production can be a challenging task. Here are some key steps to ensure that ML models move from development to production:
Align ML projects with business impact and priorities
Focus on problems where ML can add measurable value or solve a critical pain point in the company. Prioritising areas with the most business impact helps justify production deployment.
Use a rigorous model selection and evaluation process
Choose and validate ML models carefully so they demonstrate strong performance and reliability on relevant metrics. Well-evaluated models build confidence among stakeholders and decision-makers.
Build AI-ready data infrastructure
Breaking down data silos and ensuring the availability of high-quality, accessible data can accelerate model development and deployment, increasing the chances models get productionised.
Pilot and validate models in small-scale production-like environments
Starting with experiments on limited production lines or processes shows practical feasibility and builds internal proof of concept, helping secure broader buy-in.
Integrate ML models seamlessly with existing production systems
Ensuring models can interface smoothly with current workflows or automation platforms encourages adoption and reduces operational friction.
Highlight potential operational gains
Demonstrating how ML models optimise scheduling, quality control, or product personalisation can provide tangible reasons to prioritise them in production.
Engage cross-functional stakeholders early and frequently
Collaboration between data scientists, engineers, product managers, and business leaders is critical to understanding requirements, managing expectations, and ensuring production readiness beyond just model accuracy.
Adopt a data-centric mindset
Improving data quality iteratively can lead to faster development of effective models, facilitating quicker prioritisation for production when models satisfy accuracy and robustness standards.
Collaborate with product managers and end-user stakeholders
Collaborating with product managers and end-user stakeholders on what is being built and how it will be used is essential for getting solutions to production.
Discuss with actual users
Discussion with actual users is crucial to ensure the model's deployment and utilization within the product.
Stakeholder management during development
Stakeholder management during the development of the model is crucial to get it included in the roadmap.
Transform common metrics into business impact
Transforming common metrics such as accuracy or AUC into actual business impact is important for deciding how a model will be used.
Communicate the impact of the model
A lift plot can help communicate the impact of using a model to set default probability thresholds, transforming the results into monetary values for discussion with the business.
Ensure deployment within the tool used by operational end-users
The machine learning model's deployment is not inside the tool that operational end-users use, potentially leading to its underutilization.
Address challenges in getting solutions to production
Many data scientists face challenges in getting their solutions to production. Implementing monitoring or CI/CD in this way may also be difficult. It is often better to pythonise and modularise the code into scripts with multiple functions.
High accuracy may not be meaningful
A model's high accuracy (99%) may not be meaningful to other data scientists or business stakeholders. A classification report, which gives precision and recall for positive and negative classes, can help identify a bad model.
Ensure model investment for deployment
Product managers' investment in the machine learning models can ensure their deployment.
Time estimates for data science projects can be difficult
Time estimates for data science projects can be difficult to make.
In summary, prioritisation depends on clearly demonstrating business value, rigorous evaluation, strong data infrastructure, incremental deployment, operational integration, and stakeholder collaboration to build trust and prove feasibility. This holistic approach increases the likelihood that ML models developed by data scientists move from development to prioritised production use.
Finance plays a significant role in allocating resources for the development and deployment of ML models, ensuring they have the necessary funding for successful production.
Businesses should employ technology, such as data-and-cloud-computing and artificial-intelligence, to improve data management and enable the scalability required for ML projects to thrive and contribute to the company's success.
