Covid-19 Response Capability with Power Systems

© 2020 IBM Corporation
IBM Cognitive
Infrastructure
Relevant Contribution to
the COVID-19 Fight
Improve time to results.
Impact more lives.
April 2020
1
IBM Power Systems | IBM Storage

Collaborators
2
Frank Lee
Terry Leatherland
Linton Ward
Clarisse Taaffe-Hedglin
Ruzhu Chen
Mohamed El-Shanawany
Bill Josko
Steven Meserve
Marisa de Peralta
Jeff Hong
Mike Kowolenko, Novisystems
+
Many Partners, Clients,
Researchers
IT Builders

3
Infrastructure delivers faster insights with greater
efﬁciency to – change the flow of work.
High-performance Data & AI deployed against this problem at massive scale
reduces time spent delivering insights through unique load balancing and
model optimization technologies

IBM Accelerates Medical Research Tasks
GENOMICS
Biomarkers detection,
biodata modeling and
statistics data
visualization
DIAG N O STIC S
Image classification using
AI with flexible, targeted
models in open
frameworks
MOLECULAR
SIMULATION
Drug discovery via
modeling of
macromolecule
receptors and small-
molecule ligands
DATA FU SIO N
Synthesize and model
diverse data using data
fusion, natural language
processing, and machine
learning
BIOMOLECULAR
STRUCTURE
Cryo-EM image
restoration and
refinement analysis
for drug design and
discovery
QU A LIT Y
IN SP E C TIO N
Quality control and
regulatory compliance
in medical device or
pharmaceutical
manufacturing
4

R&D pipeline
5
Manufacturing
Adapted from https://www.nimh.nih.gov/about/directors/thomas-insel/blog/2012/experimental-medicine.shtml
GEN O M ICS
MOLECULAR
SIMULATION
BIOMOLECULAR
STRUCTURE
DIAG N O STIC S
DATA FU SIO N
QUALITY
INSPECTION

IBM Systems at Supercomputing 2019 / © 2019 IBM Corporation
Data
Overload
Oceans of data
arise from rapid
digitization and
instrumentation
of healthcare.
App Chaos
Thousands of
applications,
workflows and
models are not
all following the
same rules.
Adoption
Vertically
integrated
toolsets with
heavy
customization
and vendor lock-
in create work
silos.
Performance
When scaling up
or out, most
institutions
cannot diagnose
or analyze the
performance
problems they
face.
Cost
Demanding
workloads
require well-
orchestrated
infrastructure to
manage, monitor
and control
costs.
Five key challenges to progress remain despite advances
6

A framework for designing, deploying, growing and optimizing infrastructure for HPC, AI and Cloud, created in collaboration with world’s leading
healthcare and life sciences institutions, and using Red Hat OpenShift, IBM Power Systems, IBM Storage and open API endpoints.
From Data to Insight with IBM HPDA Reference Architecture
DATAHUB
High Performance Data Fabric & Catalog Capable of
Handling Exabytes of Data and Trillions of Objects
OR C H E ST R ATI
ON
High Performance Computing & AI Platform Capable of
Orchestrating Thousands of Servers and GPUs
APPS & MODELS
Large-scale and high-throughput workloads such as
HPC, AI and Cloud computing
ME D IC A L
TA S K S
Genomics, molecular simulation, structural analysis,
diagnostics, data fusion, manufacturing quality
inspection.
7

8
Orchestrator
Carrier & Engine for Jungles of App
Datahub
Dams & maps for Ocean of Data
Disk TapeFlash
Compute
& Storage
Software-
Defined
Infrastructure
Applications
& Tools
CPU GPU
Framework
& Libraries
Clinical RWE
High Performance Data & AI (HPDA) Architecture
ImagingGenomics

InfiniBand and Ethernet
switches & UFM
(Mellanox) shared
Elastic Storage
Server (ESS) per System
100TB – xx PB
v High performance & scalability
v Linux RHEL7.6, CUDA, ESSL,xl
compilers, SMT, GPFS, NVMe …
v Optimized ML/DL frameworks and
tools, e.g., Watson Machine Learning-
CE (Pytorch, TensorFlow, Caffe),
v Life Science Apps: GATK, Relion,
NAMD, Amber, Gromacs, etc
v Value-add tools:
v IBM Visual Insights, IBM Visual
Inspector , 3D data labeling prep
v Application interoperability &
manageability:
v Openshift, Docker, Kubernetes, LSF ..
v Anaconda, python, Jupyter …
v MPI, XLC/C++, GCC, AT …
v Data management & governance
v Elastic Storage /Spectrum
Discover
Accelerated Computing Platform (ACP)
Network
IBM TOR Switch
Enet TOR Switch
Network Manager
Compute Nodes
• Deep Learning
• Inference
• Computation
Management Nodes
ESS Mgmt. Node
ESS* Storage
1-18 Deep Learning
nodes for model training
2+ compute nodes for
inferencing, computation
1-5 Mgmt. nodes
Per rack
Power9
AC922
Power9
IC922
Hardware Building Blocks So3ware Building Blocks
OpenPOWER
9

To provide enhanced user experiences at enterprise scale
Researcher
Auto ML with
Driverless AI
Instance #2
Data Engineer/Analyst
ETL / Reporting
Instance #4
Risk Analytics
LOB
Instance #3
Administrator
Compute Nodes
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Linux
Data scientist
Customer behavior...
Trend analysis...
Instance #1
Broader use, better business value
ü Logical view - each group has their own environment
ü Right resources allocated to each user based on their
workload characteristics and SLA priority
ü Multitenancy ensures data and application isolation
Existing Hadoop Data Lakes
Spark AI Grid
Data from
lake and
other
sources
x86

Journey to Power Analytics Grid
Comput
e
YARN
HDFS
Comput
e
YARN
HDFS
Linux
Hive, HBase, Spark,…
Hadoop Workloads
Cloudera Hadoop
With WMLA at the Edge
Compute
YARN
Compute
YARN
Linux on Power
Hive, HBase, Spark,…
Shared Storage
POSI
X
HDF
S
HDF
S
Secondar
y Storage
Tier
Data
Ingest
Compute
Spectrum
Conductor
Linux on Power
GPU
Spark, TensorFlow, Caffe,..
ML/DL AI Workloads
with IBM Power WMLA
POSI
X
Compute
Spectrum
Conductor
Hadoop Workloads
With WMLA
Phase I: De-Couple Compute Phase II: Shared Storage Tier Phase III, AI Hub
Benefits
De-Coupled Compute from
Storage migrating workloads
from Cloudera, Reduce
dependence on Static/Batch ETL
Faster ingest with POSIX, Reduce storage
footprint and copies of data, Grow
storage independently of compute
leveraging in-memory Spark vs ETL
Complexity of managing
Hadoop stack, Analytics
Performance, Resource
utilization
Improved resource uPlizaPon, MulP-
tenancy, Improved AnalyPcs Performance,
Spark GPU acceleraPon & AI ML/DL
frameworks on the same cluster
Cloudera
Data
Lake
Server
Nodes
Read Only
Hybrid-Cloud ready, evoluPon to
AI ML/DL analyPcs
Replace Cloudera with
x86/Power and evolve to
migraPon off of Hadoop
MapReduce to Spark centric
plaVorm
11

reduce “Time To Answer”
Biomolecular StructureMolecular Fit SimulationGenomics
Data Fusion and AIMedical Diagnostics AI Medical Manufacturing Quality
Accelerate drug discovery by accelerated
modeling of macromolecule receptors and
their small-molecule ligands. Applications are
optimized and accelerated (>2x) on IBM
Power9 with advanced CPU and GPU
technology, as well as IBM ESS storage, hybrid
cloud solutions.
Applications: NAMD, Gromacs, AMBER
Cryo-EM image restruction and reﬁnement
analysis is accelerated with IBM Power9 and
ESS storage solutions by more than 2 Jmes,
comparing to its peers. High resolution protein
receptors and ligands can be applied in drug
design and discovery studies.
Application: RELION, CrYolo
Accelerate diagnostics with accelerated AI for
image (classification…) with flexible, targeted
models in open frameworks. An accelerated
computing platform accelerates time to results.
systems management and storage solutions
provide efficient and trusted curation of models,
images, metadata and inferences.
Applications: Visual Insights, Watson Machine
Learning + Accelerator, (Tensorflow, Pytorch)
Rapidly synthesize and model diverse data
using data fusion, natural language
processing, and machine learning. Fact
extraction from papers and EMR (Meddra-
based dictionaries). Text, structured lab
values and images can be included in AI
models and statistical methods in real time..
Partners: Novi Systems H2O, Spark, Hadoop
High performance and efﬁciency (10x) of
software tools and applications for genomic
variants and biomarkers detection, biodata
modeling and statistics data visualization. High
throughput and optimized workload pipelines
to accelerate biodata analysis with highly
optimal and parallel I/O, memory, CPU and
GPU computations
Applications: GATK 4, BWA, ParaBricks
Rapidly Leverage data fusion and AI for
quality control and regulatory compliance in
medical device manufacturing or in
pharmaceutical manufacturing. Make
product accept / reject decisions and
maintain compliance records.
Application: IBM Visual Inspector
Partner: Novi Systems
The Solutions
12

Optimizing Precision Medicine
Reduced time-to-completion for long-running
jobs while increasing resource utilization
Using IBM, Sidra has completed hundreds of
thousands of computing tasks comprising
millions of files and directories, without
experiencing system downtime.
100,000+

Biomolecular Structure: Cryogenic Electron Microscope
Image
Bucket
5TB per day
“Time to Answer” is now paramount
IBM IC922 = 46min. vs 77 min on x86
• Storage growth escalating each day = 1PB per year.
• Image rendering is compute intensive

• IBM is deploying AI techniques to automate the pipeline for High Resolution screening
and re-imaging using IBM Visual Insights.
• Molecules images are frozen in ice. Question: is it feasible to magnify on 100x resolution?
• Each grid image is a data file. Manual selection intensive to determine High Res candidates
• AI can automate the High Resolution re-image pipeline
AI Image Processing

Molecular Simulations. - Standard Codes
NAMD
Amber
Gromacs
IBM Performance is xx faster than Intel.
16© 2020 IBM Corporation

NAMD , VMD 25X performance boost with IBM

18

Medical Imaging
19

IBM Visual Insights Radiology Use Case
Using an automated Deep Learning
model built with IBM Visual Insights,
Radiologists can process lung X-rays to
detect Covid-19 induced pneumonia
faster and with more accuracy.

Optimizing Medical Imaging
Enhance image identiﬁcation with deep learning
to assist physicians and beneﬁt patients
1300 MRI images trained by IBM Power
Systems and IBM Storage in just two hours,
compared to forty hours on traditional
architectures
20x faster

• A Solution – Not a Bag of Tools – targeting the domain expert
• We provide solutions that use AI, not just AI tools
• Targets subject matter experts
• Intuitive interfaces; Excel level skills
• Easy to Understand - Data integration, filtering, and analysis on
steroids
• Allow users to focus on the problem, not the technology
• Overcome internal and external issues with data fusion
• Provide the ability to explore data to identify issues and
opportunities
• Simple to Use
• Abstract ML and NLP to identify important features or isolate
facts
from structured and unstructured data
• Delivered as a subscription (including
h/w, support, consulting, training)
• Provide ROI in minutes, not months
*Patents pending on the methods employed by NoviLens
Data Fusion and AI: NoviLens© Data AI Appliance
• Easy data import and automation
• Data fusion
• Enhance problem solving while de-
risking decisions.
• Internal data is your unique position
• supplement it with external data
• Visual Analytics and Filtering
• Natural Language Processing
• Machine Learning
• Deployed in the IBM Cloud or On-Premise
Easy-to-use Data AI Appliance for Decision Makers.
** TechData IBM Reseller in
Research Triangle Park 22
© 2020 Novi Systems

Uses of Data Fusion AI and advanced analytics
Clinical trial:
– Patient selection - read through EMRs to find patients with acceptable criteria
(shorten enrollment period)
– Monitor trial - pick up adverse events by reading through EMRs then grouping symptoms as well as lab data then
doing regression analysis (sample size probably too small for ML)
Production:
– Capacity: Monitor process stream for process control. Biggest issue will be supply. Know when to "kill" a batch
to free up production. Done through monitoring process (quantitative data as well a batch records).
– Product Release: Can integrate with LIMS and if they have electronic lab notebooks, treat the notebooks like
EMRs. Will accelerate failure investigations and provide insight into design space shortening release times (did
this with ThermoFisher)
– Regulatory: Assist in Post Market Surveillance by reviewing Phase IV or Medline records automatically
Distribution:
– Depending on the sophistication of their ERP systems, can coordinate specifications with regulatory
requirements (may have different specs/country).
Late stage drug development and Product Release

Data Fusion Example: Manufactured Product Disposition
product disposition
manufacturing
supply chain Production planning
BOM
vendor qualification/certification
material specifications
processing
cleaning air
water
surfaces
facilities air
water
surfaces
in process monitor
process trending
SPC
process documentation
engineering
maintenance
PM
event
shutdown
facilities
surfaces
mechanical
air
water
change control
training
quality
lab testings
oos
investigation
Control charting
product
process
materials
doc review
facility testingwater
air
surfaces
equipment
training
change control
audits deviation management
investigation product review
lab review
facilities review
training
development data
process development process capability
product attributes
R&D
preclinical
clinical
Manufacturing
Quality
Investigation
Product
Disposition
Deviation
Management
Data Fusion Example: Manufactured Product Disposition
product disposition
manufacturing
supply chain Production planning
BOM
vendor qualification/certification
material specifications
processing
cleaning air
water
surfaces
facilities air
water
surfaces
in process monitor
process trending
SPC
process documentation
engineering
maintenance
PM
event
shutdown
facilities
surfaces
mechanical
air
water
change control
training
quality
lab testings
oos
investigation
Control charting
product
process
materials
doc review
facility testingwater
air
surfaces
equipment
training
change control
audits deviation management
investigation product review
lab review
facilities review
training
development data
process development process capability
product attributes
R&D
preclinical
clinical
Manufacturing
Quality
Investigation
Product
Disposition
Deviation
Management
Decision

25
https://novi.systems/covid-19/

Ac#onable
Decisions
Image
Sensor
Database
Text
Data
Fusion
Sources
Fact
Tables
Ques#ons
AI &
Analytics
Language
Analytics
Classification
Detection
Time
Series
Descriptive
Statistics
Ω
?
NoviLens AI Appliance
End to End Workflow

TopicsH2O Driverless AI – Simple, Fast, Accurate, Interpretable
Easy Deployment for
Low Latency Models
• Production-ready, stand-
alone scoring pipelines that
are easy for IT to deploy and
manage
• Python and Java
• Streamlined scoring code to
deploy on any device: on the
edge, mobile, …
• Very fast (milliseconds) to
satisfy today’s real-time apps
Fast and
Accurate Results
• “Data Scientist in a Box”
• Simple interface
• Automatic feature
engineering to increase
accuracy
• Automatic recipes for solving
wide variety of use-cases
• Automatic tuning to
ﬁnd and tune the right
ensemble of models
Industry Leading
Interpretability
• Trusted results with
explainability and
transparency
• Interpretability for debugging,
not just for regulators
• Get reason codes and model
interpretability in plain
English
• K-Lime, LOCO, partial
dependence and more
Automatic Data
Visualization
• Automatic generation of
visualizations and graphs to
explore your data before the
model-building process
• Most relevant graphs shown
for the given data set
• Identify outliers and
missing values
H2O Driverless AI Overview
27

Extending Support for Edge Devices
IBM Visual Inspector for Healthcare
Manufacturing, Pharmaceuticals
AC922/IC922/x86
NVIDIA Jetson
TX2/Nano
iOS Device
TensorRT
CoreMLPower9 IC922/AC922 & x86 (coming soon)
28

Executive Summary
• IBM Visual Inspector is a standalone iOS
application that can execute AI models
trained on PowerAI Vision
• Available on the Apple App Store
• Devices can be handheld or ﬁxed
(mounted)
• Works in network connected or
disconnected mode
• Provides model and device management
capability to support an end-to-end AI
Computer Vision environment
• Built-in demonstration models
• Custom model development requires
Visual Insights
IBM Visual Inspector
29

Capture data to build model Inference
(disconnected or connected)
Remote management
Visual Inspector App Functions
Multiple Ways to Utilize One Application
Where does this fit? Healthcare “things” manufacturing, Pharma drug manufacturing
30

OpenPOWER Servers
IBM Power AC922
TRAIN
Powering the Fastest Supercomputer
DATA
IBM Power IC922
INFERENCE
IBM Power IC922
Deploy AI into ProductionStorage Dense Server
31
• NVMe dense server with IO rich
architecture for superior throughput1
• Enterprise ready cloud deployment
with RH OpenShift and Power
Systems reliability
• 2.35x superior price/performance for
containerized cloud deployments
• Best training platform with 4x faster
model iteration
• ~6x data throughput with NVLink
to GPUs
• Synergistic HW/SW offerings for ease
of use and leadership performance
• Superior density (33%) and through-
put to inference accelerators
• Open design for accelerator diversity
• Deploy inference at scale with HW
and SW solution offerings
31

INSIGHTS
Ingest
Prepare | Train | Inference
• Single name space
• Global collaboration / hybrid cloud
• Software RAID / erasure coding
• Multi-protocol support
Throughput-oriented,
software-defined
temporary landing zone
Transient storage
High throughput
performance tier
Fast ingest /
Real-time analytics High volume, index &
auto-tagging zone
Classification &
metadata tagging
Organize
Analyze
ML/DL
ETL
Large-scale Runtime Environment
Watson Machine Learning
Accelerator
WML CE
SnapML
Spectrum Computing
IBM Spectrum Storage for the
AI Data Pipeline
The fastest path from ingest to insights
Putting it all together
High scalability,
large/sequential
I/O capacity tier
Archive
DATA

IBM Elastic Storage Server
| 33
Spectrum Scale
ESS
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
7 - 32 GB/s
Models GL1S, GL2S, GL4S, GL5S, GL6S
1-6, 84 disk drive enclosures
0.25 – 6.8 PB usable
0.33 – 8.9 PB raw
GLxS
Disk
High perfomance,
capacity
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
60 TB – 1.1 PB usable
90 TB to 1.5 PB raw
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
9 - 37 GB/s
Models GS1S, GS2S, GS4S
1-4 SSD enclosures
High perf, IOPS,
random
I/O
GSxS
Flash
Disk: 0.5 - 2.5 PB usable
SSD: 60 - 530 TB usable
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
D1 D2 D3 D4 D5 D6 D7 D8
S822L
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
GHxx
Hybrid
Disk: 14 to 29 GB/s
SSD: 13 to 26 GB/s
Max: to 36 GB/s*
Models GH12, GH14, GH22, GH24
2-4, 84 drive HDD enclosures
1-2, 24 drive SSD enclosures
Combined high
perfomance,
capacity, IOPS,
random
I/O
*Maximum combined Disk&SSD Perf per ESS unit
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
D1 D2 D3 D4 D5 D6 D7 D8
S822L
Models GL1C, GL2C, GL4C, GL5C, GL6C, GL8C
1-8, 106 disk drive enclosures
7 - 32 GB/s
0.78 – 9.1 PB usable
1 – 11.8 PB raw
GLxC
Disk
High
performance,
capacity, density
9 PB
per
rack

8 May 2020/ © 2018 IBM Corporation
• Who, what, when, where, and why of account, container, object, stream, dir, ﬁle
• Perfect for indexing and searching
• Metadata may be separate from the data, stored with the data, or derived from the data
• Posix inode plus extended attributes
• Standard document headers (doc, ppt, mp3, dicom, pdf, jpeg, GeoTIFF)
• Custom metadata tags
• AI derived metadata
Age, Biomarkers, Developmental Stage, Cell
Surface, Markers, Cell Type/Cell Line,
Disease State, Extract Molecule, Genetic
Characteristics, Immunoprecipitation,
antibody, Organism,
Biomedical
Natural Language
Processing
Image
Location
Size
Owner
Group
Permissions
Last-Modified
...
System
Metadata
Metadata: Key to Unlocking Data Value & Improving Management
Spectrum Discover

IBM Spectrum Archive:
Policy-based Cost
Optimization
35
Small files last
accessed > 30
days
last accessed
> 60days
Silver pool is >60%
full Drain it to 20%
accessed
today and
file size is
<1G
Send it back to
Silver pool when
accessed
System pool
(Flash)
Gold pool
(SSD)
Silver pool
( NL SAS)
TS4500
Spectrum Archive
Automation
• Powerful policy engine
- Example: File Heat measures how often
the file is accessed.
- As the file gets “cold” move it
automatically to a lower cost storage
pool
- Information Lifecycle Management
- Fast metadata ‘scanning’ and data
movement
- Automated data migration to based on
threshold
• Users not affected by data migration
- Single namespace
- Persistent view of the data
• Tape as the external pool of Spectrum Scale

Watson Machine Learning
Community Edition
Deep Learning Impact
(DLI) Module
Data & Model
Management, ETL,
Visualize, Advise
IBM Spectrum Conductor with Spark
Cluster Virtualization,
Dynamic Resource Orchestration,
Multiple Frameworks, Distributed Execution Engine
WML CE: Open Source ML Frameworks
Large Model Support (LMS)
Distributed Deep Learning
(DDL – 1000s of nodes)
Auto Hyper-parameter
Tuning
Watson Machine
Learning Accelerator
IBM Visual Insights
Auto-DL for Images & Video
Label Train Deploy
Accelerated
Infrastructure Accelerated Servers Storage
AI for
Data Scientists and
non-Data Scientists
H2O Driverless AI
Auto-ML for Text & Numeric
Data, NLP
Import
Experi
ment
Deploy
Distributed Deep Learning (up
to 4 nodes)
Cognitive Systems Software Offerings
Visual Inspector
iOS Inferencing
Application
Capture Inference

37
Call to Action
• Research organizations are leading the way to introduce new
methods and new approaches.

Covid-19 Response Capability with Power Systems

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