It doesn’t take an industry expert to notice that machine learning is everywhere these days. Whether it's the latest Bing/GPT hijinks or crucial discussions on mitigating bias in AI, you’d be hard-pressed to consume the news or social media without encountering insightful conversations about the capabilities and potential of data-driven AI.
But realizing that potential takes more than just developing and training models. Securing the buy-in and resources for a machine learning project also takes a clear understanding of how the project aligns with and supports business objectives. After all, companies hire data scientists and machine learning engineers to get results; so the business case for your model will always, naturally, focus on delivering these results.
Ready to marry your next model with tangible business results? Here’s how to maintain a clear focus on value during every step of the MLOps pipeline.
1. Identify Your Business Objectives
It sounds obvious, but this is worth saying: the key to driving business value with a machine learning project is to understand what your organization wants to achieve in the first place. So, ask yourself:
- What are your company’s goals and challenges, and how can your model solve for these use cases?
- What does success look like, and what KPIs will you use to measure your progress?
Whenever possible, pull in the company CEO and other high-level leaders to seek their input – including their business objectives, the data and tech they (and you) have available, and the key players involved. This lets you pinpoint the areas where machine learning can have the biggest impact, so you can reinforce your model’s role in supporting broader organizational goals.
By the time you’ve had these conversations, you’ll likely have a list of several potential projects. For example, here are just a few of the tasks a machine learning algorithm might handle at a large ecommerce company:
- Fraud detection
- Customer segmentation and personalization
- Predictive downtime for maintenance
- Sentiment analysis
- Product or content recommendations
- Image and video recognition
- Supply chain optimization
- Price optimization
Focus on the machine learning projects that have the greatest potential to deliver results. By prioritizing your projects, you can identify dependencies and streamline the implementation process, making it more effective and efficient.
2. Prepare the Data
Understanding your data landscape is key to making sure your model is well-fed. Now that you know what you want your model to achieve, identify the data sources that will help it achieve optimal performance. For example, if you’re building a customer segmentation and personalization algorithm, you’ll probably need access to internal data sources like:
- The company CRM – For contact information, email preferences, and data points like Customer Lifetime Value
- Sales information from your PoS – To identify patterns in purchase history
- Google Analytics – To connect product preferences with different conversion paths and types of content
- The data from social media tools, email marketing software, heatmap software, and other apps – For additional insights into customer preferences and behavior
Public datasets are also helpful for certain goals. If you’re training an NLP algorithm to answer customer support questions, for example, you’ll probably want to seed your training with a publicly available language dataset, then fine-tune your model with actual customer support queries from the company website.
The better the quality and relevance of your data, the more accurate and effective your model will be. And the more effective your model, the more business value you’ll deliver through solutions that support the company’s goals.
3. Productionize Your ML Workflow
When you’re building and training your model, you can create business by keeping your workflow lean and efficient. Take our Full-Stack Machine Learning With Metaflow to learn how! In this course, we use Metaflow because the open source platform makes it easy to operationalize machine learning projects and scale your in-house data infrastructure to the cloud; but there are a number of great MLOps tools available. The key is to lean on at least one of these, because manual processes don’t scale.
As you evaluate the model’s early performance, keep the focus tight on your desired results as they relate to the company’s objectives. For example, if you’re designing a customer segmentation and personalization algorithm, your company’s objective is to earn more revenue through product upsells and a better, more personalized customer experience. How does that connect to your model’s performance?
4. Deploy the Model to Production
Whether you’re predicting customer behavior, improving supply chain efficiency, or automating manual processes, deploying your model is the crucial step that takes your project from a theoretical solution to a tangible business asset.
Once you have an MVP, integrate it into your company’s existing workflows and systems so you can monitor the model’s performance in real-world conditions. Production ML involves all layers of the stack – data, compute, versioning, etc – so it’s a good idea to run MVP production deployments as early as you can.
Monitoring your model’s performance isn’t just about making sure it stays on course; it’s also about measuring its success based on the organizational goals and KPIs you identified in Step 1. By regularly evaluating the performance of the machine learning model, you can identify areas for improvement and make adjustments to get even better results.
5. Maximize Business Value
There’s just one last step: are you getting the most out of machine learning at your organization?
Measuring success isn’t just about fixing what's broken. Ongoing reporting can also help you discover new ways to drive business value through machine learning. You may discover that your model can be used to solve additional problems or integrated with other systems to provide new insights – or its performance might spark an idea for a completely different ML project to add to the pipeline.
Maximizing the business value of machine learning means staying proactive and continuously finding new and creative ways to use it to drive results. Whether you're trying to streamline processes, improve customer satisfaction, or reduce costs, a well-executed machine learning project can help you get there.
Ready To Learn More?
If you’re ready for a hands-on, practical look at the How behind all of this – including a deep dive into productionizing your workflow – then you can’t miss Full Stack Machine Learning With Metaflow. This action-packed four-week course will teach you how to effectively deploy, monitor, and refine your own strategic, value-driven models.
With the increasing demand for data-driven decision making, it has never been more important for data scientists and machine learning engineers to situate their work in a business context so they can stand out in the job market. If you’re ready to make a real impact in your current or future role, you can’t miss this course!