Optimize performance and accuracy by moving your machine learning models into production
75% of companies are either currently leveraging AI or getting ready to. It’s no longer an option. If you’re not utilizing Machine Learning (ML) in your day-to-day business, you’re falling behind. Because the bottom line is your competitors are. The numbers, as they say, don’t lie.
Companies want to harness powerful computational abilities for predictive analyses. They want to automate functions that better meet consumer needs.
Trust us, you want this too.
ML operationalization is the method companies use to bring their ML models to market. This guide will explain where operationalization fits into the ML life cycle. We'll also look at challenges to consider when building your model. Finally, we'll cover KPIs that will help you maximize its potential.
Key Takeaways
- Machine learning operationalization is critical for extracting real business value from AI investments because model deployment in a production environment allows organizations to automate workflows and make data-driven decisions in real time.
- Success depends on strategic planning around infrastructure, security, and performance metrics because challenges like data privacy, explainability, and reproducibility must be addressed to ensure sustainable, scalable AI operations.
- Automation platforms like DuploCloud accelerate operationalizing machine learning by simplifying DevOps workflows and ensuring compliance.
What Is ML Operationalization?
ML operationalization is the process of generating and deploying AI-based models. This data science is done in a production environment for use throughout an organization’s:
- Development processes
and
- Operational workflows
This includes consumer use. It also involves model monitoring and model training. Why? To guarantee the ability to enhance productivity and efficiency. Key metrics, ML initiatives, and performance indicators are considered when reviewing model performance.
However, the journey to operationalizing AI is more complex than subscribing to a platform like ChatGPT. It's not just generative AI. According to Gartner, operational AI involves:
“The governance and the full life cycle management of all AI and decision models.”
It means being responsible for the health and usefulness of the machine learning model. It demands we implement each one into real-world development scenarios. Then, we must iterate on the model ops to improve their decision-making ability further.
You want the best approach to operationalization in artificial intelligence? It helps to understand where it fits into the machine learning operations life cycle. According to AWS, there are six phases:
- Identifying the business goal of the ML model by asking questions like, “What is the problem that needs to be solved?” and “What will be gained by solving that problem?”
- Framing the ML problem by determining what the ML model will observe and predict. It will also look at what KPIs data teams should track to optimize performance.
- Processing available data in a way that can be used to train the machine learning algorithm.
- Developing the model by training on that data set, tuning it to improve its accuracy, and evaluating the results.
- Deploying the model into production. This is where operationalization in AI takes form, as the model can then fully mature and produce results based on real-world data.
- Monitoring the model to maximize its efficacy.
An important aspect of operational AI is ensuring that you can optimize the ROI. This is the investment you’ve put into developing the ML model. Automation through tools like Kubernetes is one of the most effective ways to do so. This is thanks to its ability to schedule workloads across containers to maximize efficiency. At the same time, it can scale up or down based on user demand.
In fact, many developers spend almost half of their workday on manual tasks. These are better off being automated. Machine learning models can enable teams to work more efficiently on high-level problems. For more information about the best ways to give more time back to your developers:
Download a free copy of our ebook, 7 Essential DevOps Automation Best Practices.
Challenges of ML Operationalization
You have a few roadblocks on the way to production. These include deciding what your model will do and allocating the necessary resources. Keep the following challenges in mind as you operationalize your AI model. They'll help you avoid potential pitfalls.
- Data security: Public ML models may be more widely available than private ones. But their very nature means your proprietary data will end up mingling with everyone else’s. This data includes like customer data or confidential information. It may potentially posing a significant data security risk. This data could even become part of the data set to be used in consumer-facing inquiries. It's then further exposed to the public. Even if you’re using a private model, you've got to stay vigilant. You need to ensure that end users aren’t exposed to the inner workings of your model by limiting its access to the edge.
- Effort to create: Relying on an internally developed ML model is more secure than a public one. However, building it requires vast resources. These include teams of data scientists and engineers to:
- Develop
- Test
- Operationalize
- Explainability: Some ML models act as a sort of “black box” when providing responses. That is, a user types in a query and receives an answer, but there’s no explanation for how the model derives that answer. While this might be acceptable for some requests, it might not be for others. Acceptable includes asking for the address of a business. Unacceptable includes making medical decisions. So data teams need to account for this when designing their models.
- Reproducible results: When multiple users make the same query, your model should provide the same result every time. Unreliable responses may cause people to stop using your model. They'll then look for one that offers more consistent results.
How to Measure the Effectiveness of ML Operationalization
Before operationalizing your AI model, decide which KPIs to measure to track efficacy. That way, you can make adjustments if the results don’t align with your expectations. The following KPIs will give you a starting point for areas to measure:
- Accuracy: An ML model is only as effective as the results it provides. Thus, you need to ensure that it gives accurate results that are as reliable as possible. Accuracy should be monitored continuously post-deployment. This ensures the model adapts well to real-world data and changing user behavior.
- Compute power: ML models require vast computational power to generate responses. You need to be able to balance those costs against the revenue your model brings in. So measuring how much energy your model requires is a must. Tracking cost per inference or training cycle helps identify inefficiencies early.
- Time to market: ML models only fully mature once they utilize real-world data. As such, it is essential to measure how long it takes to bring it to market. Then weigh this data against other metrics, like accuracy. A shorter time to market can help you gain a competitive edge by allowing you to iterate faster.
- Scalability and uptime: Let's go beyond technical metrics. Operational ML systems must also meet performance expectations under varying loads. Track:
- System uptime
- Latency
- How well your model handles increasing traffic without performance degradation
- User satisfaction and adoption: Finally, qualitative metrics offer insight. These include user satisfaction scores and adoption rates. From there, you can look into whether your model is delivering value where it matters most, the end user.
Uplevel Your ML Automation Capabilities With DuploCloud
Looking to optimize the performance of your AI/ML workloads? Partner with DuploCloud and bring your model to market faster and more reliably.
Our DevOps Automation Platform allows small teams and enterprises to:
- Streamline infrastructure orchestration
- Scale operations to meet demand
- Ensuring your product meets security and compliance frameworks like SOC 2 and GDPR
DuploCloud empowers teams to focus on innovation rather than infrastructure. We remove the heavy lifting of manual provisioning, deployment, and compliance enforcement. We offer built-in guardrails and a low-code interface. That way, even lean engineering teams can deploy secure, scalable ML workloads with confidence.
Request a free demo today, and find out why high-growth organizations like RE/MAX, Lily AI, Clearstep, and more trust DuploCloud to master their AI/ML workloads and accelerate their path to production.
FAQs for ML Operationalization
What is ML operationalization, and why is it important?
ML operationalization is the process of deploying machine learning models into production so they can be used in real-world applications. It's important because it allows organizations to turn AI research into tangible value by integrating models into development workflows, automating decisions, and improving operational efficiency.
What are the biggest challenges companies face when operationalizing ML models?
Key challenges include ensuring data security, allocating the necessary resources to build and maintain models, dealing with model explainability (especially in high-stakes applications), and maintaining consistent, reproducible results across queries.
How can I measure the success of my ML operationalization efforts?
Success can be measured using KPIs such as model accuracy, compute power usage, and time to market. These metrics help determine if the model is performing as expected and providing a return on investment.
How does DuploCloud help with ML operationalization?
DuploCloud streamlines the deployment process through DevOps automation, enabling faster time to market, scalable infrastructure management, and compliance with security standards like SOC 2 and GDPR, making it easier for teams to manage and optimize their AI/ML workloads.
