BIG DATA MANAGEMENT, BUSINESS STRATEGY, DATA SCIENCE
Arize AI | January 24, 2023
On January 23, 2023, Arize AI, an industry leader in machine learning observability, introduced a connection solution for BigQuery, Redshift, Delta Lake, and Snowflake data lakes. As a result, Arize customers with centrally managed inference stores can rapidly connect their ML table data to Arize via Data Lake Connectors for strong model observability.
Arize is the market leader in terms of both volumes of models and predictions tracked, surpassing billions of predictions daily. So far, ML observability platforms have struggled to simplify their deployments while simultaneously managing billions of predictions and complicated monitoring services, such as embedding drift. The latest Arize version for data connectors expands clients' connectivity options with the widely used data lakes.
The launch comes as the machine learning community converges on various MLOps designs. A modern machine learning architecture approach involves storing inference data in a data lake. ML teams are designing these ML data lakes to power feature stores for feature serving and an inference store for analysis.
Arize Data Lake Connectors are intended for smooth integration with today's data lake architectures. Among the benefits of connecting directly to the ML data store are the following:
Financial savings can be substantial as compared to alternative techniques of ML monitoring.
Teams can run off of an SSOT
Integration and onboarding are accelerated and simplified
Arize now connects with cloud storage providers (such as Google Cloud Platform, Microsoft Azure, and Amazon Web Services), Python pipelines through an SDK, and Kafka Streaming. With this release, users of data lakes may get real-time model insights more efficiently than ever before. In addition, the platform provides fully managed built-in connectors as part of its cloud and VPC platform, eliminating the need for users to build and maintain complex data pipelines or use a separate ETL tool and providing real-time model performance analysis and monitoring.
About Arize AI
Arize AI offers an ML observability platform that monitors models and provides insights for troubleshooting production AI. The firm was established in 2020 in Berkeley, California. It allows corporate clients to monitor the performance of AI models using software that searches for unanticipated biases in algorithms, does root-cause analysis when issues arise, and enhances overall model performance. Arize operates in the background, analyzing internal and external data to forecast demand and eliminate supply chain network errors and sales losses. Adobe, Chick-fil-A, and eBay are among the fifty clients of Arize AI.
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BIG DATA MANAGEMENT, DATA ARCHITECTURE, DATA SCIENCE
Volt Active Data | January 18, 2023
On January 17, 2023, Volt Active Data, an emerging provider of in-memory database platforms to support applications that require consistency, speed, and scale, announced the unveiling of Active Streaming Decisions (Active(SD)™, an ultra high-speed decision-making engine, enabling companies to use the power of the Volt Active Data Platform to issues that other Kafka ecosystem solutions cannot solve.
With its foundation in the already combat-tested and proven Volt Active Data architecture, Active(SD) quickly utilize event data directly from Kafka topic areas as the data is generated and where the data is generated, leveraging advanced analytics and machine learning to reveal insights and make instantaneous, mission-critical decisions, which are then reverted back to Kafka so an action can be taken while the data (and resulting action) still remains appropriate.
Volt CEO David Flower said, "This is a tremendous opportunity for companies to finally be able to easily add intelligent decisioning to Kafka data streams and thereby fully capitalize on the massive amount of valuable data that Kafka streams into systems."
(Source: PR NewsWire)
There are a number of ways to use data from Kafka for further processing down the line, such as loading it into a warehouse or data lake for offline processing and reporting. However, Active(SD) is the only product built specifically to optimize event-time, in-stream decisions in less than ten milliseconds to drive real-time actions consciously. This makes it the fastest way for Kafka-powered applications to go from event to action.
About Volt Active Data
Founded in 2009, The Volt Active Data is a developer of data platforms based in Bedford, MA. The platform lets businesses get the most out of their data and apps by allowing them to grow without compromising accuracy, speed, or consistency. Build on a simplified stack and an ingest-to-action layer that can make decisions in under ten milliseconds, Volt's unique, no-compromises foundation gives businesses the ability to maximize the return on investment (ROI) of their IoT, 5G, AI/ML, and other investments, ensure "five 9's" uptime, save on operational costs, deliver hyper-personalized customer engagement, and prevent fraud and intrusion.
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BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, DATA SCIENCE
DataRobot | January 11, 2023
On January 10, 2023, DataRobot, a pioneer in the artificial intelligence sector, announced the availability of DataRobot Notebooks, a notebooks solution completely integrated into the DataRobot AI platform that allows data scientists to collaborate across code-first workflows with single-click access to embedded notebooks.
Notebooks are essential for data scientists to swiftly experiment and share findings via fast environment creation, interactive computation, and code fragments. However, as the number of notebook users in a data science company rises, data science teams face issues such as managing notebooks at scale, maintaining extensive dependencies, and overwhelming and expensive libraries for data science teams.
Mike Leone, Senior Analyst at Enterprise Strategy Group, "We are entering a phase of AI governance where the collaboration and productivity gains of data science teams become increasingly important." He further mentioned, "With DataRobot Notebooks, the flexibility to develop in preferred environments, including open-source ML tooling or in the DataRobot AI platform, streamlines the code development experience and allows data scientists to better collaborate as a team in a unified environment."
(Source – Businesswire)
DataRobot Notebooks simplifies the process of code development experience for data science processes, emphasizing automation, scalability, reproducibility, and collaboration. In addition, this improved capacity provides the data science teams with unique values, including:
Interoperability: DataRobot Notebooks is compliant and interoperable with the Jupyter Notebook standard, accelerating the onboarding process for the DataRobot AI platform.
Centralized management: DataRobot Notebooks is a uniform environment with fine-grained access controls and centralized governance, allowing data scientists to swiftly collaborate, organize, and share notebooks and related assets across people and teams.
Native integration within DataRobot: DataRobot Notebooks is natively connected with the DataRobot ecosystem, enabling data scientists to run their codes directly on the platform with all the required libraries and tools.
Enhanced features: Users can now write and run custom code in cloud-based notebooks that provide access to scalable, private, and containerized computing environments.
About DataRobot
DataRobot is the pioneer in AI cloud, providing a uniform platform for all users, data types, and environments to expedite the delivery of AI into production. Trusted by worldwide clients across industries and verticals, including a third of the Fortune 50, and providing over a trillion forecasts to the world's best businesses.
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