Iguazio is adding a production-ready feature store to its data science platform.  It will allow users to more quickly and easily develop, deploy and manage AI apps, according to execs.

Iguazio is adding a production-ready feature store to its data science platform. The offering aims to bring off-the-shelf technology to the growing area of data science while also significantly reduce the skills barrier. 

Ignazio’s integrated feature store is designed to lower the skills required to work on AI applications, so that even firms without professional data scientists can participate.

In specific, Iguazio’s approach lets users catalog, store and share features centrally, making it easier for teams to collaborate on development, deployment and management of AI apps – even across hybrid, cloud and multi-cloud environments, according to Asaf Somekh, Iguazio Co-Founder and CEO.  

“For companies that don’t have hundreds of data scientists and data engineers, building a feature store from scratch, in-house, is not feasible,” Somekh said, adding “We wanted to bring this functionality to our customers, and provide them with an off-the-shelf solution for feature engineering across training, serving and monitoring in hybrid environments.”

Architecturally, Iguazio’s feature store is built on its open source MLOps framework, MLRun, enabling contributors to add data sources and contribute additional functionality.

Iguazio’s feature store offers a “unified” approach, as it is integrated within its data science platform. This unique design means It plugs seamlessly into the data ingestion, model training, model serving, and model monitoring components. This reduces significant development and operations overhead while also boosting performance, Somekh added. 

Iguazio provides “next-level automation of model monitoring and drift detection,” Somekh added, to support model training at scale and to run continuous integration and continuous delivery (CI/CD) of machine learning (ML), he added.

Also notable, Iguazio’s “unified” feature store is available online and offline. 

In a recent post which also appeared on Medium, Ignazio’s VP Product Adi Hirschtein explained the need for a modern “unified” feature store:

In detail, Iguazio’s integrated feature store provides users these important advantages:

Ability To Build Features Once and Plug Them Anywhere, Seamlessly:  Because the Iguazio feature store is a centralized and versioned catalog, everyone can engineer and store features (along with metadata and statistics), as well as share and reuse them, and analyze impacts on existing models. Users can collect many independent features into vectors and use those from their jobs or real-time services. Iguazio’s high-performance engines automatically join and accurately compute all features.

Real-Time Features and Drift Detection: Iguazio can detect model drift and inaccuracies automatically. Upon such discoveries, Iguazio can alert the users or initiate automated re-training workflows.

Robust Data Transformation: Users can create complex feature engineering processes with Iguazio’s built-in robust data transformation service. This service includes feature aggregations with sliding windows, dozens of pre-built transformations, or support for custom logic in native Python code. With a simple API and SDK, data scientists can easily create features without requiring long data engineering cycles.

Feature Catalog: To let users share, search and collaborate on features, evaluate features with detailed statistics and analysis, and see how features correlate to both data sources and models with an easy-to-use user interface.

Integrated Data and Model Monitoring: Iguazio captures the feature statistics in real-time, enabling drift detection based on actual data drift. Thanks to the Iguazio feature store’s integration, capabilities such as concept drift monitoring and feature monitoring are available out-of-the-box.

Real-Time Feature Engineering:  Users develop features once. The feature transformation pipeline calculates features in real-time based on incoming events or streams and serves the results at millisecond-level latency or pushes them directly into a stream.

Data Governance: With strict governance, Iguazio users can also keep the data lineage of a feature, with the tracking information capturing how the feature was generated, critical for regulatory compliance.

Iguazio customers include Payoneer, Quadient, and Tulipan for various use cases such as fraud prediction and real-time recommendations. 

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Iguazio, the data science platform for real-time machine learning applications, announced a strategic partnership with NetApp that provides enterprises with a simple, end-to-end solution for developing, deploying and managing AI applications at scale and in real-time on top of the ONTAP AI framework.

Despite the great promise of AI for business applications, many data science projects fail to create business value. In fact, according to Gartner, 85 percent of data science projects fall short of expectations.

One of the reasons is that model creation is just the first step, while moving working models into a production environment introduces a whole set of complexities, such as handling data at scale, working in hybrid environments, and harnessing real-time data for predictive applications.

Businesses can overcome these challenges by working in a production-ready environment, with a simplified infrastructure that enables them to focus on creating business value.

Iguazio’s data science platform provides end-to-end machine learning pipeline automation, coupled with performance & scale, enabling real-time machine learning (ML) applications. It introduces a fresh approach to simplifying MLOps and enabling enterprises to deploy their AI projects quickly and seamlessly.

Iguazio’s integration with NetApp ONTAP AI leverages enterprise grade data management, data versioning and NetApp Cloud Volumes for a seamless hybrid cloud experience. It is also fully compatible with KubeFlow 1.0, offering a managed KubeFlow solution for enterprises.

The platform tightly integrates with NVIDIA DGX, allowing customers to utilize GPU-as-a-Service and NGC containers, making more efficient use of computational resources, saving costs and reducing infrastructure complexities.

The solution automates pipelines across machine learning, deep learning and data analytics. The end result is a scalable way of processing data and computation.

“Iguazio’s solution enables our customers to benefit from an end-to-end data science platform that supports real-time ML applications at peak performance,” said Santosh Rao, Head of AI/ML/DL Platform at NetApp. “NetApp is committed to simplifying MLOps for enterprises, allowing data scientists to run AI at scale with a simple, one-click deployment methodology.”

Asaf Somekh, Co-Founder and CEO of Iguazio, added, “We are delighted to partner with NetApp, and to be working together on some of the most exciting and cutting edge AI projects. We’re looking forward to bringing our joint solution to enterprises that are currently struggling to bring their data science to life.”

Insight Enterprises, a Fortune 500-ranked global provider of Digital Innovation, Cloud + Data Center Transformation, Connected Workforce, and Supply Chain Optimization solutions and services, has partnered with NetApp and Iguazio to provide their joint solution to their customers worldwide.

“Platform modernization is foundational to business transformation,” said Juan Orandini, Chief Architect at Insight, Cloud + Data Center Transformation.

“The end-to-end fusion of compute, storage, networking, and software platforms required for AI workloads dictates the need to assess best-fit platforms. Iguazio’s integration with NetApp ONTAP AI, coupled with Insight’s platform transformation services, simplifies time to production and overall business value.”

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Iguazio has announced that it is bringing its operations to Microsoft-owned Azure cloud and Azure Stack on-premises platform. The Data Science platform enables data scientists to take machine-learning models from data assimilation to testing, training, and production. The company has accumulated approximately $48 million in funding until now with nearly 80 employees. The company works towards making it easier for data scientists to do their work.

Iguazio says that a lot of the work that data scientists do relates to handling integration and the management of infrastructure instead of the construction of machine learning models. In order to simplify the process, the platform is relying on open source. Henry Jerez, Principal Group Product Manager at Microsoft’s Intelligent Edge Solutions Platform Group, has said that the partnership will allow them to offer other options for AI applications in the cloud for it to run on the edge. Jerez has also mentioned that this new market option will create an alternative for their customers to bring intelligence close to the data sources for applications like predictive maintenance and recommendation engines. The platform employs standard tools and API to extract data from a wide array of sources that is later stored in its real-time in-memory database. The database is compatible with data streaming along with time series data, files, and tables. It uses standard Jupyter notebooks rather than its proprietary format. Interestingly, the company has also developed an open platform for building data science pipelines. Iguazio also utilizes KubeFlow to build models, which is a machine learning toolkit for the Kubernetes container platform. Iguazio will be able to run its software in both the cloud and on-premises as Azure and Azure Stack are basically the same platform in terms of API.

A plan to introduce its solutions to Microsoft’s Azure Data Box Edge is also underway. The Data Box Edge is a hybrid cloud platform that is utilized for storing and analyzing data at the edge that can be infused with FPGAs for the deployment of machine-learning models. The Azure solutions will be included in Iguazio’s current options for the implementation of its services above AWS and Google Cloud Platform.

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