Turn Your Data into Dollars

The 21st century is the age of Big Data. Now that we have data warehouses with massive capacity, cloud-operated computers with virtually unlimited storage space, and sophisticated open-source machine learning software, the power of data is becoming more and more widespread. Everyone and their mother can learn to build a model online.
What’s much harder than building a model is building a model that can provide concrete business value. Even that’s not enough– today’s business world demands building tens or hundreds of models that can provide concrete business value in order to keep up with the competition. On top of that, you have to ensure transparency in each of these models, so you and your industry’s regulators can know what your models are doing and why. You also need clear  visualizations so you can communicate the value of your models to business shareholders.
Challenging though this objective is, feature stores are a new technology on the market that make it easy and effective to operationalize machine learning at scale. A Feature Store is a system made specifically to automate the input, tracking, and governance of data into machine learning models. Feature Stores compute and store features, enabling them to be registered, discovered, used, and shared across a company. Not only do feature stores provide data lineage, they also make it cheaper to produce models, as they improve data science efficiency so your team can get models to market quicker than ever before. This article will break down a few of the ways feature stores can help businesses use their data most effectively.
Improve Data Science Productivity
Data scientists are few and far between, and they don’t come cheap. Improving data science productivity by eliminating repetitive and unnecessary work means that you can produce more models in less time with your current staff.
In a typical data science silo, data scientists spend 80% of their time on data preparation, and only the remaining 20% is actually spent on deploying the machine learning model. Data prep work is manual, monotonous, and tedious: 76% of data scientists rated data prep as the least enjoyable part of their work. On top of that, many data scientists throughout a company end up slogging through the data to calculate the same features that another data scientist in the company has already created.
With a Feature Store, a data scientist can immediately start on a new problem by exploring the features that are already available. In many cases, someone in the past will have already created the relevant features, so the data scientist can easily produce a training set and start building models right away.
If the features they need aren’t there yet, they can always create their own features with data engineers, which will strengthen the Feature Store for the others in the future.
Enable Pipeline Integrity
Alongside the time and energy drain of unnecessary work, lacking a consistent way to calculate features can lead to models that vary wildly between data silos.
For example, in a retail company, one team may calculate “total customer revenue” by subtracting returns from sales, where another team calculates it just using sales. Both are valid metrics, but if they are both called “total customer revenue”, the result is inconsistently calculated metrics in different data pipelines. This is a problem because the model might be trained on features using one definition, but the deployed model is served features using another.
A Feature Store addresses this by adding traceability, visibility, and versioning into the data pipelines that feed features. In addition, naming constraints are built into feature stores that stop one team from overwriting the work of another; the second team must name their calculation something new to distinguish their work.
But Feature Stores go beyond making the lives of data scientists easier; they also allow for better predictions from machine learning models.
Enhance Data Freshness
If your machine learning model is trained on data that is inaccurate or outdated, your model is going to make mistakes that could cost you. Having the most recent data is absolutely essential in a business environment. If a customer bought a product from an ad they saw yesterday, but the advertising data doesn’t update until tomorrow, they could be shown a product today that they already own. Anyone who has been in this position knows how annoying it is– and if it continues to happen, they might be discouraged from supporting your company in the future.
With a Feature Store managing your data pipelines, you and your team can rest assured that the newest data is always retrieved. The pipeline is scheduled to run with the cadence of the data; monthly features are calculated monthly, daily metrics are calculated once a day, and real-time features are updated instantly, so your predictions are always based on the newest data.
Facilitate Time Consistency
Timing is everything for machine learning models. Human brains make decisions based on what we know in the moment and what we’ve learned from the past; we cannot make decisions based on information from the future. Machine learning models learn the same way.
When creating training data, it is extremely important to take this into account. The set of features used for training must be the values that were known at the time of the event.
A Feature Store solves this problem by producing training data sets with time-consistent feature values taken from each Feature Set’s history at the point in time of the events being modeled.
By keeping the historical values of all features, a Feature Store allows you to create accurate training sets, which in turn translate to accurate predictions.
Provide Model Explainability
One of the most powerful benefits of having time-consistent data is that it enables trust when checking machine learning models.
Let’s say you run a bank, and a bank regulator comes to audit your software’s performance. The regulator wants to check that your model’s process for granting a customer’s loan request is unbiased. Without a feature store, If you have a feature store with time-consistent data and transparent data lineage, it’s really easy for the regulator to check the underwriting process, and ensure that there is no discrimination innate in the data or software.
An even more powerful combination is linking your Feature Store with your machine learning workflow system. This strong link allows you to create a repository of all of the activities and notebook artifacts that went into training a model. You can examine the lineage of the model in question all the way back to the data that trained that model. Being able to analyze this data is crucial to ensure that your model is not built on biased data, so you can show your regulator why your model came to the conclusion it did.
Conclusion
So, why do you need a feature store? Not only does it save your data scientists time and energy, it allows machine learning models to make more accurate predictions that can increase your company’s revenue. On top of that, automating key parts of the machine learning pipeline allows models to be created more quickly and at a lower price, allowing you to scale enterprise AI 100x faster. Finally, keeping all of these steps clearly visible and open to scrutiny makes it easy to ensure regulatory compliance, which builds trust in your customers and critics alike.
ABOUT THE AUTHOR:
Monte Zweben is the CEO of Splice Machine
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