DataStax announced Astra Streaming, a scalable, multi-cloud messaging and event streaming platform built on Apache Pulsar. Astra Streaming is integrated with DataStax’s marquee serverless database, Astra DB, to deliver a multi-cloud solution for managing both data in motion and data at rest. With the introduction of Astra Streaming, DataStax aims to deliver on its vision of an open data stack for today’s multi-cloud applications that require massive scale, zero-downtime availability, and high performance. Astra Streaming Features:
- Global scale, cloud-native streaming, powered by Apache Pulsar without the complexity of self-managed solutions
- Compatible with Apache Kafka and Java Messaging Service
- Simple developer APIs for streaming
- Handles high-volume queuing and pub-sub messaging and more complex messaging patterns
- Pay-as-you-go pricing
Astra Streaming is available today in a beta version. To get started with Astra Streaming, create a free account.
In its second major release of the year, DataRobot announced several product upgrades to its Augmented Intelligence platform designed to further democratize AI. The 7.1 release introduces:
- MLOps Management Agents – DataRobot’s MLOps Management Agents provide advanced lifecycle management for an organization’s remote models. Management Agents understand the state of any remote model regardless of how they were created or where they are running, and can automate various tasks.
- Feature Discovery Push-Down Integration for Snowflake – Joint DataRobot and Snowflake customers can benefit from the automatic discovery and computation of new features for their models directly in the Snowflake Data Cloud.
- Time Series Eureqa Model Enhancements – DataRobot Automated Time Series now runs its unique Eureqa forecasting models as part of the regular Autopilot process. Eureqa models are based on the idea that a genetic algorithm can fit different analytic expressions to trained data and return a mathematical formula as a machine learning model.
- No-Code AI App Builder – the No-Code AI App Builder allows customers to quickly turn any deployed model into a rich AI application without a single line of code.
Additional product upgrades: Data Prep for Time Series, Nowcasting for Time-Aware Models, Automated AI Reports, and Prediction Jobs and Scheduling UI.
LivePerson, Inc., a provider of conversational AI, and Adobe announced an integration to help brands transform digital customer experience by seamlessly extending personalization from digital experiences into messaging channels and one-to-one conversations at massive scale.
The LivePerson-Adobe combination focuses on the seamless integration of LivePerson’s Conversational Cloud with Adobe Experience Cloud to help brands serve highly personalized and contextualized messages, recommendations, and offers on their customers’ favorite messaging channels. The integration helps brands to:
- Enrich customer intelligence, track attribution, and drive long-term customer value by easily sharing intent data from customer conversations on the Conversational Cloud with insights on that customer’s activity and history from Adobe Analytics
- Capture more qualified leads and accelerate net-new conversions by integrating the Conversational Cloud with Adobe Marketo Engage for proactive, automated conversations
- Increase sale conversions and decrease checkout abandonment by offering relevant assistance at any point in the buying journey
- Apply LivePerson’s Natural Language Understanding, built on over 20 years of goal-based customer conversation data, to these engagements
The Decentralized Identifier Working Group has just published a second Candidate Recommendation Snapshot for the Decentralized Identifiers (DIDs) v1.0.
This document defines Decentralized identifiers (DIDs), a new type of identifier that enables verifiable, decentralized digital identity. A DID identifies any subject (e.g., a person, organization, thing, data model, abstract entity, etc.) that the controller of the DID decides that it identifies. In contrast to typical, federated identifiers, DIDs have been designed so that they may be decoupled from centralized registries, identity providers, and certificate authorities. DIDs are URIs that associate a DID subject with a DID document allowing trustable interactions associated with that subject. Each DID document can express cryptographic material, verification methods, or services, which provide a set of mechanisms enabling a DID controller to prove control of the DID.
Candidate Recommendation means that the Working Group considers the technical design to be complete, and is seeking implementation feedback on the document. The group is keen to get comments and implementation experiences on this specification as issues raised in the document’s Github repository. The group expects to satisfy the implementation goals (i.e., at least two, independent implementations for each of the test cases) by July 17, 2021.