(calm music)
- HPE ML Ops extends the capabilities of the BlueData
EPIC Platform the entire machine learning lifecycle,
leveraging the power of containers to create secure,
scalable compute clusters for large-scale data prep
to model, build, training, and deployment.
In addition, HPE ML Ops has added functionality
to ease collaboration between data science teams
and monitor deployed models in production.
The app store provides application images
to rapidly create sandbox, training, or deployment clusters
for a wide variety of data science tools.
Users can spin up containers to host notebooks
with their choice of standard Python based machine learning
and deep learning libraries.
In addition, with our app workbench, data science teams
can bring their own tools or third-party tools,
such as H2O Driverless AI, RStudio, and many more.
HPE ML Ops eliminates the need to copy data
into the sandbox environment.
Project administrators configure shared data sources
up front and enable users to access them
per enterprise access control definitions.
This reduces data duplication and improves data security.
Standard machine and deep learning libraries
are added into the notebook.
Users have the flexibility to add
any additional libraries they need.
Users can explore the data sources and run exploratory
data analysis within the notebooks.
Most build environments need access to large-scale
computing environments, such as Spark,
distributed TensorFlow for training on big data,
and for activities, such as hyper-parameter tuning.
This would typically require enterprises to manage
two different clusters or work with two different vendors
to provide these capabilities.
HPE ML Ops pre-packages these images
and provides one-click deployment of clusters
with security and data access controls.
In addition, clusters have the ability to auto-scale,
and auto-scaling policy can be configured
through a simple JSON descriptor file.
Users can conveniently launch a training job
on a distributed cluster with a simple reference call
to the training cluster within the notebook.
This job will run on the distributed training cluster
we just set up and return the results when done.
Limited collaboration is one of the main challenges
to operationalizing machine learning models.
HPE ML Ops includes project repository,
source control, and model registry capabilities
that organizations can utilize to standardize
their ML workflows.
Source control is a critical component that allows
the implementation of standardized coding processes.
HPE ML Ops provides integration with GitHub
for implementation of source code controls.
Once source control is set up, users can conveniently
check in and commit code to GitHub
right from their notebooks.
HPE ML Ops provides a native project repository
that stores multiple versions of source code,
training data, and trained model.
Typically, the project admin sets up the project repository
for the entire team.
A simple call from the notebook loads data
from the project repository.
Once the model is trained, it can be saved
back to the project repository with a simple save command.
A model registry stores and tracks multiple
model versions and deployments.
Most models have a model file and additional metadata
that supports the model lifecycle.
Metadata includes scoring script, data used
in the model creation, model build code, and more.
HPE ML Ops provides an ability to store multiple versions
of model files and metadata for tracking
and lineage purposes.
When a user is ready to move a model to deployment,
they can check-in the model into the model registry.
Once your machine learning model is trained
and checked into the model registry,
it's native runtime image can be deployed as-is
to a secure HTTP REST endpoint
without additional coding required.
As with a training or sandbox cluster,
you can define the runtime engine, type of instance,
and number of nodes.
HPE ML Ops provides complete end-to-end visibility
into all infrastructure resources with drill down
into any specific training or deployment cluster
to view metrics around CPU, GPU, and memory utilization,
number of scoring requests, and more.
Integrations with 3rd party tools
provide model performance monitoring and interpretability.
HPE ML Ops brings DevOps like agility
to ML workflows and accelerates the time to value of ML projects.