Hitachi Vantara Labs
Introduces Machine Learning Model Management To Accelerate Model Deployment and
Reduce Business Risk
Introduces Machine Learning Model Management To Accelerate Model Deployment and
Reduce Business Risk
New Capabilities
Monitor, Test, Retrain and Redeploy Predictive Models, Enabling Greater
Algorithmic Transparency and Faster Business Outcomes
Monitor, Test, Retrain and Redeploy Predictive Models, Enabling Greater
Algorithmic Transparency and Faster Business Outcomes
Singapore – March 7,
2018 – Hitachi Vantara, a wholly owned subsidiary of
Hitachi, Ltd. (TSE:6501), today announced additional capabilities
for machine learning orchestration to help data scientists monitor, test,
retrain and redeploy supervised models in production. An innovation from Hitachi
Vantara Labs, collectively known
as “machine learning model management,” can use these new tools in a data
pipeline built in Pentaho to help improve business outcomes and reduce risk by
making it easier to update models in response to continual change. Improved
transparency gives people inside organizations better insights and confidence
in their algorithms. Hitachi Vantara Labs is making machine learning model
management available as a plug-in through the Pentaho Marketplace.
2018 – Hitachi Vantara, a wholly owned subsidiary of
Hitachi, Ltd. (TSE:6501), today announced additional capabilities
for machine learning orchestration to help data scientists monitor, test,
retrain and redeploy supervised models in production. An innovation from Hitachi
Vantara Labs, collectively known
as “machine learning model management,” can use these new tools in a data
pipeline built in Pentaho to help improve business outcomes and reduce risk by
making it easier to update models in response to continual change. Improved
transparency gives people inside organizations better insights and confidence
in their algorithms. Hitachi Vantara Labs is making machine learning model
management available as a plug-in through the Pentaho Marketplace.
As organizations
transform digitally, their algorithms become a key competitive advantage – and
potentially a risk. Once a model is in production, it must be monitored, tested
and retrained continually in response to changing conditions, then redeployed.
Today this work involves considerable manual effort and, consequently, is often
done infrequently. When this happens, prediction accuracy will deteriorate and
impact the profitability of data-driven businesses.
transform digitally, their algorithms become a key competitive advantage – and
potentially a risk. Once a model is in production, it must be monitored, tested
and retrained continually in response to changing conditions, then redeployed.
Today this work involves considerable manual effort and, consequently, is often
done infrequently. When this happens, prediction accuracy will deteriorate and
impact the profitability of data-driven businesses.
David Menninger, SVP
& Research Director, Ventana Research, said, “According to our research,
two-thirds of organizations do not have an automated process to seamlessly
update their predictive analytics models. As a result, less than one-quarter of
machine learning models are updated daily, approximately one-third are updated
weekly and just over half are updated monthly. Out-of-date models can create
significant risk to organizations.”
& Research Director, Ventana Research, said, “According to our research,
two-thirds of organizations do not have an automated process to seamlessly
update their predictive analytics models. As a result, less than one-quarter of
machine learning models are updated daily, approximately one-third are updated
weekly and just over half are updated monthly. Out-of-date models can create
significant risk to organizations.”
New data science model
management improves the process of machine learning deployments in three areas:
management improves the process of machine learning deployments in three areas:
Get models into production faster: New machine
learning orchestration steps support data and feature engineering. These steps
evaluate models and improve their accuracy using real production data before
going live. For further model tuning and to avoid overfitting, data
operations teams can generalize models against production test data using a
choice of cross-validation and holdout evaluation techniques.
Algorithm-specific data preparation and cleaning tasks – also referred to as
“last mile data prep” – are now automated. Operations teams can adjust model
parameters using a simple GUI instead of writing and maintaining code, which
frees data scientists to develop new models.
learning orchestration steps support data and feature engineering. These steps
evaluate models and improve their accuracy using real production data before
going live. For further model tuning and to avoid overfitting, data
operations teams can generalize models against production test data using a
choice of cross-validation and holdout evaluation techniques.
Algorithm-specific data preparation and cleaning tasks – also referred to as
“last mile data prep” – are now automated. Operations teams can adjust model
parameters using a simple GUI instead of writing and maintaining code, which
frees data scientists to develop new models.
Maximize model accuracy, while in production: Once a model is in
production, its accuracy typically degrades as new production data runs through
it. To avoid this, a new range of evaluation statistics helps to identify
degraded models. Rich visualizations and reports make it easier to analyze
model performance and uncover errors. When updates or changes occur, new
“challenger” models can be easily A/B-tested against the current “champion”
models. Since test results are returned faster the model can be adjusted
sooner.
Collaborate and govern model operations at
scale: More organizations are demanding visibility into how
algorithms make decisions. Lack of transparency often leads to poor
collaboration in groups deploying and maintaining models including operations
teams, data scientists, data engineers, developers and application architects.
These new capabilities from Hitachi Vantara promote collaboration, providing
data lineage of model steps and visibility of data sources and features that
feed the model. This greater transparency allows data and data pipelines to be
easily shared, standardized and reused across teams allowing new machine
learning applications to be built faster. Benefiting from an enterprise-grade
platform, the machine learning model steps are embedded into data pipelines and
can run large data volumes in a highly available and secure environment.
scale: More organizations are demanding visibility into how
algorithms make decisions. Lack of transparency often leads to poor
collaboration in groups deploying and maintaining models including operations
teams, data scientists, data engineers, developers and application architects.
These new capabilities from Hitachi Vantara promote collaboration, providing
data lineage of model steps and visibility of data sources and features that
feed the model. This greater transparency allows data and data pipelines to be
easily shared, standardized and reused across teams allowing new machine
learning applications to be built faster. Benefiting from an enterprise-grade
platform, the machine learning model steps are embedded into data pipelines and
can run large data volumes in a highly available and secure environment.
“Machine learning and artificial
intelligence (AI) are optimizing everything from customer interactions to
enterprise operations. As these applications evolve, data scientist and IT
operation teams will need to move newly trained models into production faster
than ever before, which can jeopardize model accuracy, collaboration and
governance,” said John Magee, VP, product marketing, Hitachi Vantara. “Hitachi
Vantara Labs’ machine learning model management provides improved algorithmic
transparency and automation so application teams can focus their efforts on
innovating rapidly without risking model deterioration.”
intelligence (AI) are optimizing everything from customer interactions to
enterprise operations. As these applications evolve, data scientist and IT
operation teams will need to move newly trained models into production faster
than ever before, which can jeopardize model accuracy, collaboration and
governance,” said John Magee, VP, product marketing, Hitachi Vantara. “Hitachi
Vantara Labs’ machine learning model management provides improved algorithmic
transparency and automation so application teams can focus their efforts on
innovating rapidly without risking model deterioration.”
Product
Availability and Resources
Availability and Resources
· Model management capabilities can be accessed
in the Pentaho Marketplace beginning March 6, 2018.
These plug-ins are currently unsupported and will be available for testing. In
future versions, they may be integrated into Pentaho Data Integration (PDI). To
learn more, visit Pentaho Labs.
in the Pentaho Marketplace beginning March 6, 2018.
These plug-ins are currently unsupported and will be available for testing. In
future versions, they may be integrated into Pentaho Data Integration (PDI). To
learn more, visit Pentaho Labs.
About Hitachi Vantara
Hitachi
Vantara, a wholly owned subsidiary of Hitachi, Ltd., helps data-driven leaders
find and use the value in their data to innovate intelligently and reach
outcomes that matter for business and society. We combine technology,
intellectual property and industry knowledge to deliver data-managing solutions
that help enterprises improve their customers’ experiences, develop new revenue
streams, and lower the costs of business. Only Hitachi Vantara elevates your
innovation advantage by combining deep information technology (IT), operational
technology (OT) and domain expertise. We work with organizations everywhere to
drive data to meaningful outcomes. Visit us at www.HitachiVantara.com.
Vantara, a wholly owned subsidiary of Hitachi, Ltd., helps data-driven leaders
find and use the value in their data to innovate intelligently and reach
outcomes that matter for business and society. We combine technology,
intellectual property and industry knowledge to deliver data-managing solutions
that help enterprises improve their customers’ experiences, develop new revenue
streams, and lower the costs of business. Only Hitachi Vantara elevates your
innovation advantage by combining deep information technology (IT), operational
technology (OT) and domain expertise. We work with organizations everywhere to
drive data to meaningful outcomes. Visit us at www.HitachiVantara.com.
Connect
with Hitachi Vantara
with Hitachi Vantara
· Twitter
· LinkedIn
· Facebook
About Hitachi, Ltd.
Hitachi,
Ltd. (TSE: 6501), headquartered in Tokyo, Japan, delivers innovations that
answer society’s challenges. The company’s consolidated revenues for fiscal
2016 (ended March 31, 2017) totaled 9,162.2 billion yen ($81.8 billion). The
Hitachi Group is a global leader in Social Innovation and has approximately
304,000 employees worldwide. Through collaborative creation, Hitachi is
providing solutions to customers in a broad range of sectors, including Power /
Energy, Industry / Distribution / Water, Urban Development, and Finance /
Government & Public / Healthcare. For more information, please visit http://www.hitachi.com.
Ltd. (TSE: 6501), headquartered in Tokyo, Japan, delivers innovations that
answer society’s challenges. The company’s consolidated revenues for fiscal
2016 (ended March 31, 2017) totaled 9,162.2 billion yen ($81.8 billion). The
Hitachi Group is a global leader in Social Innovation and has approximately
304,000 employees worldwide. Through collaborative creation, Hitachi is
providing solutions to customers in a broad range of sectors, including Power /
Energy, Industry / Distribution / Water, Urban Development, and Finance /
Government & Public / Healthcare. For more information, please visit http://www.hitachi.com.
HITACHI
is a trademark or registered trademark of Hitachi, Ltd. All other trademarks,
service marks, and company names are properties of their respective owners.
is a trademark or registered trademark of Hitachi, Ltd. All other trademarks,
service marks, and company names are properties of their respective owners.
For the LATEST tech updates,
FOLLOW us on our Twitter
LIKE us on our FaceBook
SUBSCRIBE to us on our YouTube Channel!
