Best Practices for Succeeding with MLOps Webinar ft. Noah Gift author of 'Practical MLOps' - May 24th at 12pm ET
Easily engineer online and offline features, share them across teams and ML applications with minimal development and integration effort
Features are properties that are used as inputs to a machine learning model. For instance, a recommendation application might use the total amount per purchase or product category as one of its many features. Generating a new feature, called feature engineering, takes a tremendous amount of work. The same features must be used both for training, based on historical data, and for the model prediction based on the online or real-time data. This creates a significant additional engineering effort, and leads to model inaccuracy when the online and offline features do not match. Furthermore, monitoring solutions must be built to track features and results and send alerts of data or model drift.
The Iguazio integrated feature store, at the heart of its data science and MLOps platform, solves those challenges. Accelerate the development and deployment of AI applications with automated feature engineering, improved accuracy, feature sharing and glueless integration with training, serving and monitoring frameworks.
The Iguazio feature store is the first commercially available production-ready feature store which is part of an integrated and glueless data science and engineering solution. The Iguazio feature store automates and simplifies the way features are engineered, with a single implementation for both real-time and batch. High-level transformation logic is automatically converted to real-time serverless processing engines which can read from any online or offline source, handle any type of structures or unstructured data, run complex computation graphs and native user code. Iguazio’s solution uses a unique multi-model database, serving the computed features consistently through many different APIs and formats (like files, SQL queries, pandas, real-time REST APIs, time-series, streaming), resulting in better accuracy and simpler integration.
The Iguazio feature store is a centralized and versioned catalog where everyone can engineer and store features along with their metadata and statistics, share them and reuse them, and analyze their impact on existing models. Iguazio’s integrated feature store plugs seamlessly into the data ingestion, model training, model serving, and model monitoring components, eliminating significant development and operations overhead, and delivering exceptional performance. Users can simply collect a bunch of independent features into vectors, and use those from their jobs or real-time services. Iguazio’s high performance engines take care of automatically joining and accurately computing the features.
The unified online and offline feature store provides next-level automation of model monitoring and drift detection, training at scale, and running continuous integration and continuous delivery (CI/CD) of ML. Features are stored along with their data quality policies and auto generated online and offline statistics, to automatically detect model drift, inaccuracy, and alert the users or initiate automated re-training workflows.
Create complex feature engineering processes with a built-in robust data transformation service, including feature aggregations with sliding windows, dozens of pre-built transformations, or your custom logic in native Python code. With a simple API and SDK, data scientists can easily create features without requiring long data engineering cycles.
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.
Capture the feature statistics in real time, enabling drift detection based on actual data drift. The Iguazio feature store is fully integrated with the rest of the MLOps Platform, with features like concept drift monitoring and feature monitoring out of the box.
Features are developed once for offline and real-time; no extra work is needed. 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.
Keep the data lineage of a feature, with the tracking information capturing how the feature was generated, critical for regulatory compliance.
Build models using the integrated feature store and deploy them in hybrid multi-cloud environments, on-prem or at the intelligent edge: anywhere your application lives.
“Using Iguazio, we are revolutionizing the way we use data, by unifying real-time and historic data from different sources and rapidly deploying and monitoring complex AI models to improve patient outcomes and the City of Health’s efficiency”
Nathalie Bloch, MD
Head of Big Data & AI at Sheba Medical Center’s ARC innovation complex
“Joint customers of NetApp and Iguazio manage data at scale and run increasingly complex algorithms on that data. By making critical features available within their data science platform, adding ‘smart data’ with NetApp AI is simple. With easy access to features that are ready for use across the enterprise on any AI project, customers can develop and deploy these projects efficiently and accurately.”
Head of Global AI and Analytics Business Development
“With Iguazio’s feature store, MongoDB Atlas, our fully managed cloud database, can easily store features that are ready to use in machine and deep learning, making MLOps a reality. This refines the experience for both our advanced users, who are scaling AI, and those just starting out on their AI journey to innovate on top of an already powerful database.”
Global Head of Enterprise Modernization at MongoDB
“At Tredence, we serve leading Fortune 500 clients, who always expect rapid business value. Iguazio’s MLOps solution and built-in feature store enables the rapid development, deployment and management of AI applications across the enterprise, providing a competitive edge to clients looking to deploy innovative AI solutions in today’s cutthroat market”
CTO and CO-Founder at Tredence
“Real time AI is critical to ad performance...Working with Iguazio enabled us to get our AI application up and running in a matter of weeks.”
CEO of PadSquad
“At Tulipan, we use novel techniques to convert information into knowledge, taking a holistic approach to data. That’s why we were so happy when we came across Iguazio - Iguazio’s data science platform enables the unification of data from all types and sources, along with the development and rapid deployment of AI applications across use cases and environments. Now, Iguazio’s integrated feature store takes real-time data engineering to the next level of automation”
CEO at Tulipan
"Iguazio allowed us to unify and combine any data type to create real-time machine learning models with an out of the box data science toolkit. That to us was worth its weight in gold."
Director of DXP Innovation
“With Iguazio’s Data Science Platform, we built a scalable and reliable system which adapts to new threats and enables us to prevent fraud with minimum false positives”.
VP Corporate Security and Global IT Operations
Eliminate silos, automate complex online and offline feature engineering and share features across teams and projects with the Iguazio feature store