Get to Know Molecula FeatureBase


What if Making Real-Time Data Machine-Ready was Easy?

Molecula FeatureBase simplifies the journey to real-time analytics and AI

Checkout out our on-demand webinar “In a Complex Data Stack, Simplicity Matters” 

Forget reading – let me speak with a Solution Engineer

For years now, organizations have been drowning in data and struggling to prove business value. New technologies have emerged with the promise of making your data accessible and operational via transfer to the cloud — but, often these platforms end up moving your problems instead of fixing them. Each new addition to the advanced analytics and ML/AI stack has created one more step in the journey towards achieving positive business outcomes, and each new step requires heavy resources — investment, team, etc. Instead of inventing something next-gen, these platforms have focused on optimizing legacy technologies, but even optimized legacy technologies cannot keep up with current, machine-scale innovation.

Molecula was born out of this experience. Our team originally formed within another company — a marketing and analytics segmentation platform for media, sports and entertainment companies. With each new customer we signed, we needed to ingest from hundreds of sources, millions of customer profiles, each with millions of data points. We rapidly broke our stack — current technologies could not support our needs. We regrouped to determine if there was a new approach that could handle the volumes of data we were ingesting without sacrificing speed, latency, or data quality. The new data format that we invented (and hold several patents on) is what we now refer to as a “feature-oriented format.” We soon realized that our feature-oriented format was a new way of representing and storing data that automated its preparation for AI and machine learning, and we knew we had to share this with the world, so we created Molecula. We want other disruptive-leaning companies like ours to see the benefits of a feature-first approach, and we invite executives, data engineers, and data scientists to learn how FeatureBase can revolutionize their AI capabilities. .

“What if you could have a new technology that automated the process of preparing your data for real-time analytics and AI, while allowing your raw data to remain at the source, and being able to then leverage this across all use cases, teams and functions?”

Data Preparation Today

Data preparation is essential to any successful machine-scale endeavor. In the past, preparing data for machine learning has been an arduous process: managing manual, one-off IT requests, exploring the resulting datasets, outlining and reviewing the expectations of a machine learning algorithm, selecting the specific data needed for the desired outcome, and deciding on the most appropriate data preparation techniques to transform that data into a machine-ready format, all based on the single task at-hand. This is a slow and expensive process.

The first step in the feature engineering process is feature extraction. Feature extraction prepares your data for machines by abstracting complex schemas and their data into basic objects and attributes to distill a highly computable representation optimized for machine-scale analytics and applications. Once the data is in this format, it is far less expensive, requires fewer resources to process, is unimaginably fast, and opens up new opportunities across organizations to take advantage of all of their data.

What is Molecula FeatureBase?

FeatureBase flips the script on data preparation and automates feature extraction as the first step of data preparation, effectively eliminating the most costly, time-consuming parts of the process. This feature extraction and storage technology enables real-time analytics and AI initiatives, making model-ready data instantly accessible and reusable across the organization, without the need to copy or pre-process data. Molecula leaves data at its source and continuously extracts and updates only features in real time. All of an organization’s data can be converted to reusable features and analyzed with full fidelity, regardless of format or source location, across any cloud, for immediate, millisecond analytics performance.

FeatureBase is not built on any existing architectures — it is an entirely original technology, based on a new data format that can scale in never-before-seen ways without sacrificing speed or latency, and while reducing costs and data footprint. It does not disrupt existing infrastructures, instead overlaying on top of the intricate architectures that enterprises have built over decades of attempting to deal with big data, and bringing immediate value to that data. It does not create another silo, but instead eliminates existing silos, unifying access to all data for all teams. It has been described as the “easy button” for accelerating real-time analytics and AI within enterprises, including those within the life sciences, technology, financial services, and healthcare industries. With FeatureBase, these industries are able to personalize customer experiences, predict anomalies and fraud, diagnose patients more accurately, predict staffing needs — the possibilities are endless.

FeatureBase is deployed as an overlay that enables feature sharing and securely powers all of your projects without disrupting your existing stack. Insights can be driven with millisecond updates from raw data sources. With Molecula’s FeatureBase, you can nail data access and data readiness and create feature sets, reuse features, and optimize machine learning lifecycles.

Our binarized format stores the relationship between the attribute and the object. The FeatureBase then serves feature vectors that contain whatever features you have selected for training and production purposes and allows for reuse and sharing of features inside and outside of an organization. In the FeatureBase itself, we maintain a feature map, which is essentially the metadata that we need to translate features in and out of these entities.

We apply homomorphic compression to the data and since we’re storing features, not values, as long as you keep the feature map secure, your feature vectors are indecipherable due to the nature of the compression. Features are perfect for hybrid or cloud environments because of their secure and confidential nature.

With a more simple implementation process, Molecula begins to help businesses see value almost immediately. That value is not isolated to a single team, but instead serves all relevant departments, unlocking new opportunities to capitalize on real-time, model-ready data.

I’ve finished reading – I’m ready to speak with a Solution Engineer