The rise of automation and governance in MLOps
MLOps can make many of an organization's operations more efficient, but only when its automation capabilities are paired with effective governance strategies.
With the increasing adoption of AI and machine learning across virtually all industries, the need for effective MLOps -- or, machine learning operations -- is more important than ever. MLOps is a field that focuses on the deployment, management and maintenance of machine learning models in production environments. It's rapidly evolving as researchers, engineers and practitioners work to address the various challenges that arise in ML production environments. New vendors, tools and frameworks are emerging all the time, offering novel solutions and best practices to common issues such as version control, model deployment, monitoring, performance optimization and governance.
With many organizations looking to scale their use of machine learning, staying up to date with the latest vendors and developments in MLOps is essential for anyone working in machine learning -- particularly those involved in deploying and managing models in production. It's important to understand the areas where MLOps is truly helping organizations improve. While MLOps is still relatively young, adoption is on the rise.
MLOps brings automation to the machine learning pipeline, from data collection and preprocessing, to feature extraction and feature engineering, to model training and testing and so on. Collaboration is improved through MLOps, with data scientists, programmers, developers and operations teams all encouraged to be on the same page and work together toward the same goals. For example, organizations can enhance model version control by keeping track of changes and rolling back to earlier iterations of models as needed.
MLOps can also enable continuous integration and continuous delivery (CI/CD) through instant testing, validation and distribution. Through monitoring and alerting, operations teams can ensure machine learning models are functioning as expected by identifying and resolving issues early on. Additionally, MLOps can enable stakeholders to right-size infrastructure based on real-time workload requirements throughout the pipeline.
While all the aforementioned areas are important to MLOps, I would argue the most important area as of late is the incorporation of governance into the machine learning pipeline. The governance connection to MLOps is all about overseeing the creation and deployment of machine learning using established processes, policies, procedures and controls. The goal is to ensure ethics, accuracy and security.
The policies, practices and controls established to oversee the application, creation and deployment of machine learning models are referred to as governance in connection to MLOps. To guarantee that machine learning models are developed and deployed ethically, accurately and securely, appropriate governance must be in place. While there are many dimensions of governance when applied to MLOps, I would argue these are the top five to consider:
- Ethics. Governance implemented correctly will all but guarantee that data is used ethically and that the model output is impartial and without prejudice. This is especially important in highly regulated industries, such as healthcare and finance, where sensitive data might be used in ML models.
- Compliance. Organizations must ensure models abide by the law and follow regulations, including data protection, intellectual property and ethical guidelines. This can be challenging for organizations that lack full transparency into black box models, such as neural networks.
- Security. Machine learning models experience the same vulnerabilities as traditional data repositories. They're prone to cyber attacks and breaches. Proper governance can ensure security measures are put in place to protect models and underlying data throughout the machine learning pipeline.
- Transparency and explainability. Governance can ensure stakeholders understand how ML models work. Implementing explainability into model usage is becoming the standard way to convey this kind of information.
- Accountability. Behind every model is a group of stakeholders that built it -- or, in some cases, another model that built it. Governance ensures that model behavior and performance can be audited and traced back to the responsible parties.
For the widespread adoption of machine learning, automation and governance are essential. When looking into MLOps platforms, these capabilities cannot be overlooked. They serve as foundational requirements that promote efficiency, trust and confidence throughout the machine learning pipeline. Many organizations already recognize this level of importance. But for organizations just getting started with machine learning, this might feel like overkill. Unfortunately, they do need it. Maybe not today, but once they see success from machine learning, the need to scale its use will be apparent. Without MLOps, they will hit an instant roadblock trying to manage and monitor everything at scale.