This document discusses how telecom companies can leverage artificial intelligence and analytics to drive digital transformation. It identifies key opportunities for AI including improving the customer experience, fraud mitigation, and predictive maintenance. It then outlines the components of a telecom data lake that can support these advanced analytics initiatives. Examples of AI use cases for different telecom business functions like marketing, network operations, and security are also provided. The document argues that a data lake platform optimized for analytics can help telecom companies achieve business and innovation goals through improved operations, new revenue streams, and lower costs.
AI Microservices APIs and Business Automation as a Service Denis GagneDenis Gagné
My presentation at the BBC2019 conference.
While the current AI fascination is fueled by Machine Learning, the architecture and application landscape is being redesigned around Microservices and APIs. These technologies are combining forces to affect many facets of business, creating a paradigm shift all around you. Do you know how to take advantage of the tsunami created by these technologies?
In this session, we will explain these technologies and how to extract business value from them. We will demonstrate how line of business people can integrate machine learning into business decisions that are explainable, auditable, and traceable and how they can easily assemble business automations that orchestrate a series of microservices via modern API platforms. With this knowledge in hand, you will be ready to face the next wave of technologies that are hitting your organization.
Bhadale group of companies edge intelligence services catalogueVijayananda Mohire
This is our offering for the edge computing and edge intelligence using IoT devices, frameworks and related technologies to bring in better intelligence at the edge
To unlock the value from your Industrial IoT initiative, it’s paramount that operational insights are instantly gained from machine generated data that let you make critical decisions in real-time to the advantage of your business. Learn from practical use cases how seamless communications between assets from any corner of the globe, the machinery that analyses the data and the systems and people at the very heart of your business are a key element of successful IoT platforms that scale from initial pilots to global rollouts.
AI Microservices APIs and Business Automation as a Service Denis GagneDenis Gagné
My presentation at the BBC2019 conference.
While the current AI fascination is fueled by Machine Learning, the architecture and application landscape is being redesigned around Microservices and APIs. These technologies are combining forces to affect many facets of business, creating a paradigm shift all around you. Do you know how to take advantage of the tsunami created by these technologies?
In this session, we will explain these technologies and how to extract business value from them. We will demonstrate how line of business people can integrate machine learning into business decisions that are explainable, auditable, and traceable and how they can easily assemble business automations that orchestrate a series of microservices via modern API platforms. With this knowledge in hand, you will be ready to face the next wave of technologies that are hitting your organization.
Bhadale group of companies edge intelligence services catalogueVijayananda Mohire
This is our offering for the edge computing and edge intelligence using IoT devices, frameworks and related technologies to bring in better intelligence at the edge
To unlock the value from your Industrial IoT initiative, it’s paramount that operational insights are instantly gained from machine generated data that let you make critical decisions in real-time to the advantage of your business. Learn from practical use cases how seamless communications between assets from any corner of the globe, the machinery that analyses the data and the systems and people at the very heart of your business are a key element of successful IoT platforms that scale from initial pilots to global rollouts.
Vertex Perspectives | AI-optimized Chipsets | Part IVertex Holdings
Businesses are increasingly adopting AI to create new applications to transform existing operations, driving big data with the growth of IoT and 5G networks and increasing future process complexities for human operators. In this new environment, AI will be needed to write algorithms dynamically to automate the entire programming process. Fortunately, algorithms associated with deep learning are able to achieve enhanced performance with increasing data, unlike the rest associated with machine learning. To date, deep learning technology has primarily been a software play. Existing processors were not originally designed for these new applications. Hence the need to develop AI-optimized hardware.
A look at the end-to-end stack for Industrial IoT platforms, including some of the key issues and opportunities to manage at each layer of the solution. See https://Juxtology.com for more.
Mike McBride will provide a look at the Industrial IoT (IIoT) landscape and the OT/IT convergence. He will cover several use cases including healthcare, entertainment and smart buildings. He will cover the challenges IIoT networking faces with emerging technologies and how edge computing will provide increased performance, security and reliability. Mike will discuss the various Edge Computing standards & opensource forums along with proposed architectures. And Mike will present new solutions being proposed (ICN, slicing, Blockchain) to support the bandwidth, latency and security requirements within Industrial verticals.
About the speaker: As Sr. Director of Innovation & Strategy, within Huawei's IP Network BU, Mike leads Industrial IoT, Edge Computing and IP/SDN architecture, standardization, and strategy across product lines and industry forums. He leads architecture and standardization activities within the IIc and BBF and has served as an IETF Working Group chair for 15 years. Mike has led emerging technology projects within opensource communities and played a key role in the formation of OPEN-O (Now ONAP). He is an Ericsson alum where he developed and directed SDN/NFV network architectures. And for many years with Cisco, Mike supported customers, worked in development teams and managed mobility, wireless and video projects across BUs. Mike began his career supporting customers at Apple Computer. He resides in Orange County, CA
DELL Technologies - The IoT Value Chain - Solutions for the Smart World - Del...Smarter.World
In this presentation we will introduce various aspects of the Internet of Things, Industry 4.0 and the associated challenges in implementing new digital services.
We also refer to IoT / Industry 4.0 terminology, market developments, factors and drivers, IoT platform components, but also to the differentiation and similarities of the Internet of Things and Industry 4.0.
Using various application examples, we will outline the range of DELL Technologies offerings.
Here, however, we remain at an overview level for the first time without paying attention to the details of the deployable DELL-EMC products and solutions.
We would continue this in downstream discussions depending on the identified topic segment.
Internet of Things Stack - Presentation VersionPostscapes
What is in an Internet of Things Stack? A deep dive from Postscapes and Harbor Research
Infographic version can be found here:
http://www.slideshare.net/Postscapes/internet-of-things-stack
Full resolution can be found at: http://www.postscapes.com/internet-of-things-stack
Accelerating analytics on the Sensor and IoT Data. Keshav Murthy
Informix Warehouse Accelerator (IWA) has helped traditional
data warehousing performance to improve dramatically. Now,
IWA accelerates analytics over the sensor data stored in relational and timeseries data.
This is the first presentation of the Open Platforms portal (http://open-platforms.eu) a tentative to document existing platforms for IoT deployment, to foster reusability and facilitate technology choices. As part of the Internet of Things European Research Cluster (IERC) activity chain 1.
Vertex Perspectives | AI Optimized Chipsets | Part IIIVertex Holdings
In this instalment, we review the training and inference chipset markets, assess the dominance of tech giants, as well as the startups adopting cloud-first or edge-first approaches to AI-optimized chipsets.
Brian Isle: The Internet of Things: Manufacturing Panacea - or - Hacker's Dream?360mnbsu
The Internet of Things (IoT) has the potential to drive new innovation in products, services, and improve "how things are done" in manufacturing. However IoT also brings-to-light safety and security issues when purpose-built computing and network devices are exposed to the internet. This session will review case studies of IoT enabled exploits, explore some of the underlying cause of the vulnerabilities, and briefly review of steps vendors and end-users are taking to mitigate the risk.
From the 2014 Taking Shape Summit: The Internet of Things & the Future of Manufacturing.
IoT Architecture - are traditional architectures good enough?Guido Schmutz
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Dependent on the size and quantity of such events, this can quickly be in the range of Big Data. How can we efficiently collect and transmit these events? How can we make sure that we can always report over historical events? How can these new events be integrated into traditional infrastructure and application landscape?
Starting with a product and technology neutral reference architecture, we will then present different solutions using Open Source frameworks and the Oracle Stack both for on premises as well as the cloud.
Session about "Microsoft and Internet of Things" at #NuvolaRosa - Naples (Italy) 12 May 2016
http://www.nuvolarosa.eu/corsi-napoli/
Main Themes:
Internet of Things
Windows 10 IoT Core
Windows Azure Services
Windows IoT Hub
Stream Analytics
Azure Blob Storage
Power Bi
Powering the Internet of Things with Apache HadoopCloudera, Inc.
Without the right data management strategy, investments in Internet of Things (IoT) can yield limited results. Apache Hadoop has emerged as a key architectural component that can help make sense of IoT data, enabling never before seen data products and solutions.
Overcoming the AIoT Obstacles through Smart Component IntegrationInnodisk Corporation
Enterprises in every industry are gearing up for AI’s integration with IoT at the edge. Analytics and cloud-based applications are crucial foundations for the AIoT infrastructure. But even more importantly, AIoT requires complete, real-time access to the data in fulfill the needs of highly responsive edge computing applications.
In our experience, many customers are facing the same difficulties with regards to cyber level and physical level device integration in the new AI era. As the world's leading industrial storage and memory provider, Innodisk has a solid track record with more than 2000 customers, and expertise built on more than a decade of integration of hardware, firmware and software solutions.
Attend this webinar to learn about:
- Preparing your business for the new Internet of Things (IoT) an AI era
- How do we Overcome the Current Architectural Issues?
- Increasing process efficiency and delivering a better customer experience
- Facilitating new platforms that enable rapid development of next generation intelligent IoT systems
- Trends and technology in AIoT intelligent storage/ data optimization
Industry pundits are predicting up to 50 billion connected devices by 2020, generating more data than in all of human history to date and connected via ubiquitous, connectivity such as 5G, Sigfox and NBIoT. With this comes the promise of business opportunities to deploy your Internet of Things solution. Ganga will walk you through the trends in computing that you need to be aware of, how you can get started and how working with Intel can accelerate your development and time to market.
Speaker: Ganga Varatharajan, IoT & New Technologies Manager, Intel
Vertex Perspectives | AI-optimized Chipsets | Part IVertex Holdings
Businesses are increasingly adopting AI to create new applications to transform existing operations, driving big data with the growth of IoT and 5G networks and increasing future process complexities for human operators. In this new environment, AI will be needed to write algorithms dynamically to automate the entire programming process. Fortunately, algorithms associated with deep learning are able to achieve enhanced performance with increasing data, unlike the rest associated with machine learning. To date, deep learning technology has primarily been a software play. Existing processors were not originally designed for these new applications. Hence the need to develop AI-optimized hardware.
A look at the end-to-end stack for Industrial IoT platforms, including some of the key issues and opportunities to manage at each layer of the solution. See https://Juxtology.com for more.
Mike McBride will provide a look at the Industrial IoT (IIoT) landscape and the OT/IT convergence. He will cover several use cases including healthcare, entertainment and smart buildings. He will cover the challenges IIoT networking faces with emerging technologies and how edge computing will provide increased performance, security and reliability. Mike will discuss the various Edge Computing standards & opensource forums along with proposed architectures. And Mike will present new solutions being proposed (ICN, slicing, Blockchain) to support the bandwidth, latency and security requirements within Industrial verticals.
About the speaker: As Sr. Director of Innovation & Strategy, within Huawei's IP Network BU, Mike leads Industrial IoT, Edge Computing and IP/SDN architecture, standardization, and strategy across product lines and industry forums. He leads architecture and standardization activities within the IIc and BBF and has served as an IETF Working Group chair for 15 years. Mike has led emerging technology projects within opensource communities and played a key role in the formation of OPEN-O (Now ONAP). He is an Ericsson alum where he developed and directed SDN/NFV network architectures. And for many years with Cisco, Mike supported customers, worked in development teams and managed mobility, wireless and video projects across BUs. Mike began his career supporting customers at Apple Computer. He resides in Orange County, CA
DELL Technologies - The IoT Value Chain - Solutions for the Smart World - Del...Smarter.World
In this presentation we will introduce various aspects of the Internet of Things, Industry 4.0 and the associated challenges in implementing new digital services.
We also refer to IoT / Industry 4.0 terminology, market developments, factors and drivers, IoT platform components, but also to the differentiation and similarities of the Internet of Things and Industry 4.0.
Using various application examples, we will outline the range of DELL Technologies offerings.
Here, however, we remain at an overview level for the first time without paying attention to the details of the deployable DELL-EMC products and solutions.
We would continue this in downstream discussions depending on the identified topic segment.
Internet of Things Stack - Presentation VersionPostscapes
What is in an Internet of Things Stack? A deep dive from Postscapes and Harbor Research
Infographic version can be found here:
http://www.slideshare.net/Postscapes/internet-of-things-stack
Full resolution can be found at: http://www.postscapes.com/internet-of-things-stack
Accelerating analytics on the Sensor and IoT Data. Keshav Murthy
Informix Warehouse Accelerator (IWA) has helped traditional
data warehousing performance to improve dramatically. Now,
IWA accelerates analytics over the sensor data stored in relational and timeseries data.
This is the first presentation of the Open Platforms portal (http://open-platforms.eu) a tentative to document existing platforms for IoT deployment, to foster reusability and facilitate technology choices. As part of the Internet of Things European Research Cluster (IERC) activity chain 1.
Vertex Perspectives | AI Optimized Chipsets | Part IIIVertex Holdings
In this instalment, we review the training and inference chipset markets, assess the dominance of tech giants, as well as the startups adopting cloud-first or edge-first approaches to AI-optimized chipsets.
Brian Isle: The Internet of Things: Manufacturing Panacea - or - Hacker's Dream?360mnbsu
The Internet of Things (IoT) has the potential to drive new innovation in products, services, and improve "how things are done" in manufacturing. However IoT also brings-to-light safety and security issues when purpose-built computing and network devices are exposed to the internet. This session will review case studies of IoT enabled exploits, explore some of the underlying cause of the vulnerabilities, and briefly review of steps vendors and end-users are taking to mitigate the risk.
From the 2014 Taking Shape Summit: The Internet of Things & the Future of Manufacturing.
IoT Architecture - are traditional architectures good enough?Guido Schmutz
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Dependent on the size and quantity of such events, this can quickly be in the range of Big Data. How can we efficiently collect and transmit these events? How can we make sure that we can always report over historical events? How can these new events be integrated into traditional infrastructure and application landscape?
Starting with a product and technology neutral reference architecture, we will then present different solutions using Open Source frameworks and the Oracle Stack both for on premises as well as the cloud.
Session about "Microsoft and Internet of Things" at #NuvolaRosa - Naples (Italy) 12 May 2016
http://www.nuvolarosa.eu/corsi-napoli/
Main Themes:
Internet of Things
Windows 10 IoT Core
Windows Azure Services
Windows IoT Hub
Stream Analytics
Azure Blob Storage
Power Bi
Powering the Internet of Things with Apache HadoopCloudera, Inc.
Without the right data management strategy, investments in Internet of Things (IoT) can yield limited results. Apache Hadoop has emerged as a key architectural component that can help make sense of IoT data, enabling never before seen data products and solutions.
Overcoming the AIoT Obstacles through Smart Component IntegrationInnodisk Corporation
Enterprises in every industry are gearing up for AI’s integration with IoT at the edge. Analytics and cloud-based applications are crucial foundations for the AIoT infrastructure. But even more importantly, AIoT requires complete, real-time access to the data in fulfill the needs of highly responsive edge computing applications.
In our experience, many customers are facing the same difficulties with regards to cyber level and physical level device integration in the new AI era. As the world's leading industrial storage and memory provider, Innodisk has a solid track record with more than 2000 customers, and expertise built on more than a decade of integration of hardware, firmware and software solutions.
Attend this webinar to learn about:
- Preparing your business for the new Internet of Things (IoT) an AI era
- How do we Overcome the Current Architectural Issues?
- Increasing process efficiency and delivering a better customer experience
- Facilitating new platforms that enable rapid development of next generation intelligent IoT systems
- Trends and technology in AIoT intelligent storage/ data optimization
Industry pundits are predicting up to 50 billion connected devices by 2020, generating more data than in all of human history to date and connected via ubiquitous, connectivity such as 5G, Sigfox and NBIoT. With this comes the promise of business opportunities to deploy your Internet of Things solution. Ganga will walk you through the trends in computing that you need to be aware of, how you can get started and how working with Intel can accelerate your development and time to market.
Speaker: Ganga Varatharajan, IoT & New Technologies Manager, Intel
Vertex perspectives ai optimized chipsets (part i)Yanai Oron
Businesses are increasingly adopting AI to create new applications to transform existing operations, driving big data with the growth of IoT and 5G networks and increasing future process complexities for human operators. In this new environment, AI will be needed to write algorithms dynamically to automate the entire programming process. Fortunately, algorithms associated with deep learning are able to achieve enhanced performance with increasing data, unlike the rest associated with machine learning.
Building a reliable and scalable IoT platform with MongoDB and HiveMQDominik Obermaier
Today’s Internet of Things (IoT) is enabling companies to blend together the physical and digital worlds, creating new business models and generating insights that increase productivity at once unimaginable levels. However, managing the ever growing volume of heterogeneous IoT data from disparate devices, systems and applications both on premise and in the cloud can be a challenging endeavour without a scalable and reliable IoT platform.
In this webinar, we will explore why and how companies are leveraging HiveMQ and MongoDB to build exactly that: a scalable and reliable IoT platform. Based upon a sample fleet management scenario, we will explain how telematics data can be routed via MQTT and efficiently stored to provide analytics and insights into the data.
Key Learnings
- Common challenges and pitfalls of IoT projects
- Required components for effectively handling data with an IoT platform
- HiveMQ for MQTT to enable bi-directional device communication over unstable networks
- MongoDB as the flexible and scalable modern data platform combining data from different sources and powering your applications
- Why MongoDB and HiveMQ is such a great combination
Arocom is a consulting and solution engineering company with expertise in providing engineering services for AI & Machine Learning, Data Operations & Analytics, MLOps and Cloud Computing.
Our clients include companies within biotech, drug discovery, therapeutics, manufacturing, retail and startups. Our consultants are best in their skills and offer hands-on talent to our clients in achieving their goals.
IBM in Surveillance: Solutions that Deliver InnovationPaula Koziol
Video surveillance has a growing significance as organizations seek to safeguard their physical and capital assets. Simultaneously, the requirement to detect more places, people, and things together with a desire to draw out more useful information from video data is rousing new demands for capacities, capabilities, and scalability. IBM Storage offers a broad spectrum of offerings which are ideally suited to help organizations store, manage and secure increasingly large volumes of video surveillance footage. Hear about the evolving DVS space and how IBM Storage offerings -- such as FlashSystem, Storwize Family, Elastic Storage Server, Spectrum Scale and Spectrum Archive -- can deliver higher value for digital video surveillance solutions.
Advanced Analytics and Artificial Intelligence - Transforming Your Business T...David J Rosenthal
Recent advances in AI have incredible potential and they are already fundamentally changing our lives in ways we couldn’t have imagined even five years ago. And yet, AI is also probably one of the least understood technological breakthroughs in modern times. Come to this event to learn about breakthrough advances in AI and the power of the cloud, and how Microsoft provides a flexible platform for you to infuse intelligence into your own products and services. Microsoft empowers you to transform your business, uniquely combining AI innovation with a proven Enterprise platform, deriving intelligence from a wide range of data relevant to your business no matter where it lives.
We are a top web design and website development company in Toronto, Canada specialized in offering custom website development solutions for enterprises,
Phoenix Data Conference - Big Data Analytics for IoT 11/4/17Mark Goldstein
“Big Data for IoT: Analytics from Descriptive to Predictive to Prescriptive” was presented to the Phoenix Data Conference on 11/4/17 at Grand Canyon University.
As the Internet of Things (IoT) floods data lakes and fills data oceans with sensor and real-world data, analytic tools and real-time responsiveness will require improved platforms and applications to deal with the data flow and move from descriptive to predictive to prescriptive analysis and outcomes.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
1. 1
Digital Transformation Through Analytics
AI and Telecom Transformation
Bill Wong
Artificial Intelligence and Big Data Practice Leader
Dell Technologies
2. 2
Telecom – Key Business and Innovation Drivers
Improve Network
Operations Monitoring
and Management to
deliver efficient, timely
and reliable
management
operations
Grow revenue by
enhancing the
customer experience
and improving fraud
mitigation
Improve cybersecurity
capabilities to reduce
threats to the network
and services
Lower costs of
operations using
predictive maintenance
3. 3
AI Opportunities
Customer Experience
Chatbots can use advanced image
recognition and social data to
personalize sales conversation
Customer Acquisition
Classify customer wallets into micro-
segments to establish finely-tuned
marketing campaigns and provide AI-driven
insights on the next best offers
Network Intrusion / Detection
Analyze data such as IP addresses,
geographic data, email domains, mobile device
types, operating systems, browser agents,
phone prefixes, and more to prevent or
remediate account takeovers
Fraud Mitigation
Real-time analysis to identify and detect
and prevent fraud in all avenues of
commerce including online and in-person
transactions
Industry Application Examples
Analyze Consumers’ Behavior
Campaign And Conversion Analysis
Credit Card Application Approval
Customer service chatbots/routing
Claim Fraud Detection
Evaluate Create Worthiness
Fraud And Credit Risk Analysis
Fraud Detection and more…
Predictive Maintenance
Proactive and predictive maintenance
IOT Analytics
Detect interference in cell
towers and reconfigure to
optimize performance
4. 4
Telecommunications Data Lake
Supporting Digital Transformation through Advanced Analytics
Consumption
Zone /
Data Analytics
Raw /
Landing/
Secure Zone/
Data Ingestion
Documents and
Emails
Web logs,
Click
Streams,
Newsfeeds,
IOT/Sensor
data
Self-Service Dashboards
Advanced Analytics
Sales
Analysts
Consumer Dashboards
Operational Analytics
Data
Scientists
Customers
Marketing
Analysts
Data Governance | Security and Compliance
Enriched /
Discovery Zone /
Data
Transformation
Data Sources
Common Services
Optimized Infrastructure for Advanced Analytics
Chat data
Personas
Tools /
Applications
Data Lake Capabilities
• Provide support for a variety of analytical applications, including self-service, operational, and data science analytics
• Data preparation and integration capabilities to ingest structured and unstructured data, move and transform raw data to
enriched data, and enable data access to for the target user base
• An infrastructure platform optimized for advanced analytics that can perform and scale
OLTP, ERP,
CRP Data
Social Networks
Machine
Generated
Data
5. 5
Expectations
Plateau of
Productivity
Peak of Slope of EnlightenmentInnovationTrigger Trough of Disillusionment
Inflated Expectations
Hype Cycle for Artificial Intelligence
“Narrow" AI is becoming
better than humans at
defined tasks. "General" AI
is still a long way off.”
Time
Plateau will be reached
less than 2 years
2 to 5 years
5 to 10 years
more than 10 years
Deep Learning
Infrastructure Transformation
Autonomous Vehicles
“AI, one of the most
disruptive classes of
technologies, will become
more widely available due to
cloud computing, open
source and the “maker”
(developers, data scientists
and AI architects) community.
While early adopters will
benefit from continued
evolution of the technology,
the notable change will be its
availability to the masses.
As of July 2019
AI PaaS
Artificial General Intelligence
Machine Learning
NLP
FPGA Accelerators
GPU Accelerators
DNN ASICs
Quantum Computing
Neuromorphic Hardware
Computer Vision
Speech Recognition
6. 6
Top 10 Types of Hardware for AI Delivery*
1. Processors (CPU, GPU, FPGA, ASIC)
2. HPC / Supercomputer Infrastructure
3. Communication Network
4. Personal Devices
5. Connected Home Devices
6. AR / VR Head-Mounted Displays (HMD)
7. Drones
8. Robotics
9. Automotive
10.Sensors and Application Components (audio, camera, LiDAR, etc.)
*The Business Impact and Use Cases for Artificial Intelligence, Gartner, 2017
Accelerate
computational
performance
AI-enabled endpoints
AI-enabled autonomous endpoints
11. 11
AI Magic Quadrants
Data Science and Machine Learning Platforms Cloud AI Developer Services
Data science and machine-learning platforms are defined as:
• A cohesive software application that offers a mixture of basic building blocks
essential both for creating many kinds of data science solution and incorporating
such solutions into business processes, surrounding infrastructure and products.
Cloud AI developer services are defined as:
• Cloud-hosted services/models that allow development teams to
leverage AI models via APIs without requiring deep data
science expertise
The Marketplace
Continues To
Evolve
12. 12
Data Analytics and AI Use Cases – Partner Solutions
IOT / Streaming /
Machine Data Analytics
Deliver Near Real-Time
Analytics
• Analyze IOT / Streaming
data
• Improve IT operations and
security leveraging Machine
Data
• Computer vision
applications
Machine / Deep Learning
Transform the business
with analytical insights
• Data Science / Machine
Learning Platform
• Industry-focused AI
platforms
Data Lake/Unstructured
Data Infrastructure
Improving Data Access
and Agility
• Create an enterprise data
platform for structured and
unstructured data
• ETL offload to lower costs
• On-demand deployment of
container-based
environments
Augmented Analytics
and Data Warehouse
Improve Decision
Making
• Support augmented
business analytics
• Create an enterprise data
platform to support
analytics
• Data integration and
Master Data Management
13. 13
H2O.ai DataRobot
AutoML offerings H2O Driverless AI (commercial) and H2O-3 (open source)
• Good adoption of its open source offering
• Machine Learning Interpretability generates the constructs for the data
scientist to use and explain the results of the models
AutoML offerings enables business users and the Citizen Data Scientist
• Easy to use, you do not need to be a data scientist
• Prediction Explanation: Highlights the features that impact each
model’s decision
14. 14
Accelerate Time From Research To Production With An AI ML Platform
• Micro-services based and full stack data science platform. Decouple
infrastructure from the data pipeline microservices. A code-first
platform ready to integrate any containerized tools and open source
• Accelerate AI development with reusable ML components, and
production-ready infrastructure with native Kubernetes cluster
orchestration and meta-scheduler.
Iguazio
Open and High Performance Data Science PaaS
• Managed & hardened open-source plus 3rd party services and apps
• Secure real-time data sharing enabling collaboration & parallelism
• Minimize CPU, mem, and ops overhead
Cnvrg.io
15. 15
Customer and Employee Health and Safety Solutions
• Detection of persons/objects
• Display showing temperature differences accurate
to 0.1°C
• Alarm in case of exceeding or falling below defined
temperature ranges
• Event Triggers (alarm, network message, activation
of a switching output)
• Temperature range from -40 to +550 °C
•Face Redaction for privacy
Dell Workstation
with NVIDIA
Dell Technologies Surveillance Solutions
- Open Data Lake Platform
- Scalable Infrastructure
- Analytics-ready
Image, Video and Thermal-based AI Applications
Applications
- Fraud Detection
- Loss Prevention
- Workplace Accident Reduction
- Customer Insight
- Public Safety
- Counter Terrorism
16. 16
• Eliminate inefficient islands of storage
– Infrastructure consolidation for both clinical and non-clinical workloads
• Scales as data growth and number of instruments,
modalities, and digital clinical applications
increases
• Enable better information sharing
• Accelerate data analytics to gain new insight
• Extends into the cloud
• Prepared for next generation analytics
Dell EMC
Data Lake
Caffe2
Data Lake Storage Platform
17. 17
The Digital Future Demands a New Perspective
Cloud First Data First
Infrastructure-centric Business-centric
Takes into consideration:
• Data gravity
• Data velocity
• Data control
• Data privacy and compliance
Driven by:
• Lower infrastructure CapEx
• Offload infrastructure maintenance
• Improve time to market (deployment
time for infrastructure)
Evolve to a Data-Driven Business
19. 19
• Design and build systems for HPC and
Deep Learning workloads
• Systems include compute, storage,
network, software, services, support
• Integration with factory, software, services
• Power and performance analysis, tuning,
best practices, trade-offs
• Focus on application performance
• Vertical solutions
• Research and proof of concept studies
• Publish white papers, blogs, conference
papers
• Access to the systems in the lab delltechnologies.com/innovationlab
Dell Technologies HPC and AI Innovation Lab
20. 20
The Value of Dell for AI Infrastructure
- Comprehensive and Scalable AI/Analytics Platform Portfolio
- Workstations, Servers, Clusters, Storage, Networking
- Infrastructure and Data Science and Analytics Expertise
- HPC and AI Innovation Lab
- IoT / Intelligent Video Analytics Lab
- Solution-based Offerings
- Pre-configured AI Ready Offerings
- IoT / Safety and Security and
Thermal Vision Solutions
- GPU Virtualization
- ML Platforms
Infrastructure
Scalability
Reduce
Complexity
Address
Demand
Partner
Ecosystem
Cost
Effective