SAN MATEO, Calif., March 10, 2020 /PRNewswire/ -- Hazelcast,
the leading in-memory computing platform, today announced the
easiest way to deploy machine learning (ML) models into ultra-low
latency production with its support for running native Python- or
Java-based models at real-time speeds. The latest release of the
event stream processing engine, Hazelcast Jet, now helps
enterprises unlock profit potential faster by accelerating and
simplifying ML and artificial intelligence (AI) deployments for
mission-critical applications.
Recent research1 shows that 33% of IT
decision-makers see ML and AI as the greatest opportunity to unlock
profits, however, 86% of organizations are having difficulty in
managing the advances in technology. From its recent integration as
an Apache Beam Runner to the new features announced today,
Hazelcast Jet continues to simplify how enterprises can deploy
ultra-fast stream processing to support time-sensitive applications
and operations pertaining to ML, edge computing and more.
Fast-moving enterprises, such as financial services
organizations, often rely on resource-heavy frameworks to create ML
and AI applications that analyze transactions, process information
and serve customers. These organizations are burdened with
infrastructural complexity that not only inhibits their ability to
get value from ML and AI, but introduces additional latency
throughout the application. With its new capabilities, Hazelcast
Jet significantly reduces time-to-deployment through new inference
runners for any native Python- and Java-based models. The new Jet
release also includes expanded database support and other updates
focused on data integrity.
"With machine learning inferencing in Hazelcast Jet, customers
can take models from their data scientists unchanged and deploy
within a streaming pipeline," said Greg
Luck, CTO of Hazelcast. "This approach completely eliminates
the impedance mismatch between the data scientist and data engineer
since Hazelcast Jet can handle the data ingestion, transformation,
scoring and post-processing."
A High-Performance Platform for Real-Time Machine Learning
Inference
Since Python is the leading programming language
used by data scientists to develop ML models, businesses need a
fast and easy way to deploy Python models into production. However,
companies often struggle as they lack the optimal infrastructure to
efficiently operationalize those models. Enterprises must either
convert the models to another language to run within their
infrastructure or bolt-on a separate subsystem, both of which
result in low performance. To address that challenge, Hazelcast Jet
now features an "inference runner" that allows models to be
natively plugged into the stream processing pipeline.
In a major leap forward for the industry and customers, Jet
allows developers to deploy Python models in a stream processing
architecture which enables enterprises to feed real-time streaming
data directly into the model. This is a stark contrast compared to
other frameworks that call out to external services via REST, which
not only adds significant round-trip network latency but also
requires administrative overhead in maintaining the external
services, especially for ensuring business continuity. This
challenge is compounded as more and more ML models are
operationalized. In Jet, the Python models are run locally to the
processing jobs, eliminating unnecessary latency and leveraging the
built-in resilience to support mission-critical deployments.
This major advancement adds significantly to the benefits of the
industry-leading, real-time performance from Hazelcast. ML
Inference jobs can be scaled to the number of cores per Jet node
and then scaled linearly by adding more Jet nodes to the job. When
combined with fault tolerance, security and scale, Hazelcast Jet
provides enterprises with a platform for executing high-speed,
real-time ML deployments in production.
"Last year we simplified streaming by delivering the industry's
only all-in-one processing system, eliminating the need for complex
IT designs built from many independent components. Now we're moving
the industry forward again by simplifying how enterprises can
deploy ultra-low latency machine learning models within that
efficient system," said Kelly
Herrell, CEO of Hazelcast. "There are millions of dollars to
be won when microseconds count and Hazelcast Jet makes that a
reality faster than any alternative, especially for applications
leveraging artificial intelligence and machine learning."
Expansion of Stream Processing Guarantees
Hazelcast
Jet now incorporates new logic that runs a two-phase commit to
ensure consistency across a broader set of data sources and sinks.
This new logic expands upon the "exactly once" guarantee by
tracking reads and writes at the source and sink levels and ensures
no data is lost or duplicately processed when a failure or outage
occurs. Customers can, for example, read data from a Java Message
Service (JMS) topic, process the data and write it to an Apache
Kafka topic with an "exactly once" guarantee. This guarantee is
critical in systems where lost or duplicate data can be costly,
such as in payment processing or e-commerce transaction
systems.
Change Data Capture Integration
To allow databases to
act as streaming sources, Hazelcast Jet now includes a change data
capture (CDC) integration with the open source project Debezium.
The CDC integration adds support for a number of popular databases
including MySQL, PostgreSQL, MongoDB and SQL Server. Since CDC
effectively creates a stream out of database updates, Hazelcast Jet
is a natural fit to efficiently process the updates at high-speed
for the applications that depend on the latest data.
Availability
Hazelcast Jet 4.0 is available today for
download.
Additional Resources
- Hazelcast Jet 4.0 is Released [Blog]
- Machine Learning Inference at Scale [Blog]
- Hazelcast Jet: The Ultra-Fast Streaming Framework [Web]
- New Opportunities for Simplified
Stream Processing [Web]
Footnotes
(1) "Infinity Data" by Hazelcast and
Intel, November 2019
About Hazelcast, Inc.
Hazelcast delivers the in-memory
computing platform that empowers Global 2000 enterprises to achieve
ultra-fast application performance - at any scale. Built for
low-latency data processing, Hazelcast's cloud-native in-memory
data store and event stream processing software technologies are
trusted by leading companies such as JPMorgan Chase, Charter
Communications, Ellie Mae, UBS and
National Australia Bank to accelerate data-centric
applications.
Hazelcast is headquartered in San
Mateo, CA, with offices across the globe. To learn more
about Hazelcast, visit https://hazelcast.com.
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SOURCE Hazelcast, Inc.