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The Great Model Bake-off: Finding the Best Performing ML Models for Your Business

Robot backoff | Wallaroo.AI

Machine learning (ML) is the sharpest competitive edge organizations can have today. It’s why 76% of enterprises are prioritizing AI and machine learning (ML) over all other IT initiatives in 2021, where most view it as a game-changer for their business.

The need has never been greater for data scientists to develop efficient ML models that deliver real business value. Similarly, the cost of running bad models has never been higher. With the average ML model taking up to three months to reach production, organizations simply can’t afford to drain their time and resources on dead-end ML.

One way to ensure you’re using the best-performing models for the highest ROI is by running an ML model bake-off. 

This post gives you a quick introduction to model bake-offs, what challenges you can expect while running one, and what you can use to make every bake-off a worthwhile investment.

What exactly is a model bake-off?

Let’s start from the top: a bake-off is defined as “a research process or proof of concept in which competing technologies are compared and the best product or service is selected.”

In the same way, you’d bake several cakes using slightly different recipes to find the best tasting one, an ML model bake-off pits different models against each other to find the one that would perform the best in production.

Essentially, bake-offs help you deploy the most effective models for your business, and are particularly useful in high-stakes scenarios (e.g. healthcare, security, or military applications), where the wrong model could have costly consequences.

The trick is choosing the model that balances the most important business needs with technical requirements. For example, in a model bake-off for fraud detection, you’d think the most accurate model would automatically win — but you also need to consider model size since a large model (no matter how accurate) would consume too many resources on the user’s machine to be feasible in the real world.  

What can you compare in a bake-off?

The answer is an irritating “it depends,” but we can tell you the most common model variations you’d consider in a bake-off.

Modeling algorithm: The main differentiator is usually the type of model you want to compare for your particular use case. For example, you could run a random forest classifier, gradient-boosted decision tree, and a deep learning model. 

Model inputs: This plays around with different inputs and ways of feature engineering. For example, when testing rolling averages of time series data, you could vary how wide a rolling window should be, and how many rolling windows to use.

Hyperparameters: There are infinite variations on all the different parameters you could test, and no data scientist wants to go through the torture of actually testing them all. A bake-off makes it easier to test these variations in parallel rather than one at a time.

Training data: This is like the flour for your cake in an actual bake-off. Your test data sets are one of the most critical ingredients, and you can test important specifics like how much data a model needs to perform and/or how recent the data needs to be.  

Winning Egg.jpeg

What criteria decide the winner?

This is another one that depends on your particular business needs, but here are some common criteria to keep in mind. 

Accuracy: This is typically one of the primary deciding factors, but it’s not as simple as how many decisions the model gets right or wrong. It also delves into what kind of mistakes the model makes — like how many false positives it identified or how many true positives it missed. It’s up to the data scientist to reach a definition of “accuracy” that best serves the problem at hand.

Latency/query time: For real-time applications like fraud detection, predictive maintenance, and cybersecurity, you’ll want to choose the model with the quickest response time. Although if you’re planning to run models in batch, this criteria will likely be low on your checklist.

Model size: Nobody wants a resource-heavy model, especially if it’s meant to run on edge devices (like drones) or in small environments (like mobile apps). In almost every scenario, you’ll want to make the size of the model one of the leading criteria to cut down on the number of computing resources needed to run it.

Training time: If you know you’ll need to train a model often, you’ll want it to have minimal training time. For example, ML models used for dynamic pricing would need to be trained quickly and frequently to keep up with changing market and consumer trends. 

Training data: What amount of training data does a model need? And, if you’re buying external data to use in a model, is that additional data worth the expense of a subscription? These are good questions to ask when the objective is selecting an effective model that’s also affordable to deploy.

What are the challenges of a model bake-off?

As with everything in this field, there are a few bumps while running a model bake-off, which can go over a lot smoother if you already know about them beforehand. Here are a few challenges to consider:

Keeping track of the multiple variations: It can be tricky to follow all the different models in the running and all their unique aspects. This can include what features come in and out of the model, the different kinds of feature engineering or data treatment, and different modeling algorithms or variations of those modeling algorithms.

Tracking which models you’re running or have run: It can also be tough to maintain oversight on which models you’ve already eliminated, and which models are still in the running.

Deploying all the variations: Without the right tech stack, you’ll likely have a hard time deploying all your different models, monitoring how they’re performing in real-time, testing and redeploying improved models, and successfully scaling your operations. 

It seems like a lot could go wrong with a bake-off, but we know of an AI/ML platform that can help with all these challenges and much more.

Run showstopping bake-offs with Wallaroo

Meet Wallaroo — an enterprise platform for production AI with the mission of making it simple for organizations to efficiently deploy, measure, and scale their ML. Here’s how you can use Wallaroo to make every bake-off a win: 

  • Keep track of all your models: With the architectural framework you need to easily track all your models, you can tag, organize, share and redeploy your models from a Python SDK or the web dashboard. This means you can spend less time tracking each and every model and more time tailoring your models to deliver better business value.
  • Monitor model performance: You can rely on comprehensive audit logs of all model inferences with their inputs and outputs — in an easy-to-use JSON format. You can also drop these logs directly into any model evaluation or business intelligence system. 
  • Manage model pipelines: Create model chains without the headache so you can build and reuse new workflows faster and easier. You can then deploy your model chains to your Wallaroo cluster at the touch of a button. 
  • Click to deploy in seconds: Wallaroo gives you all the features you need to swiftly upload, deploy, test, and redeploy ML models using the open-source frameworks you already know. No more waiting weeks or months to see how your model performs. Just one click and it’s off to the races. 
  • Scale at lower cost: With the ability to run multiple models on a single server, you can cut infrastructure and maintenance costs by up to 80%. So whenever you’re ready to scale your bake-offs, you can make it happen at a much lower investment.

Wallaroo is the ideal bake-off partner with its flexible setup, real-time data processing, and robust model management. Ready to run your best ML model bake-offs? Get in touch to learn more about Wallaroo.

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