Unlocking the Mystery of Machine Learning and Big Data Analytics Robert Barton Jerome Henry Distinguished Architect Principal Engineer @MrRobbarto Office of the CTAO CCIE #6660 @wirelessccie CCDE #2013::6 CCIE #24750 CWNE #45 BRKIOT-2394 Cisco Webex Teams
Questions? Use Cisco Webex Teams to chat with the speaker after the session How 1 Find this session in the Cisco Events Mobile App 2 Click “Join the Discussion” 3 Install Webex Teams or go directly to the team space 4 Enter messages/questions in the team space
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 3 Tuesday, Jan. 28th Monday, Jan. 27th Wednesday, Jan. 29th
BRKIOT-2600 BRKIOT-2213 16:45 Enabling OT-IT collaboration by 17:00 From Zero to IOx Hero transforming traditional industrial TECIOT-2400 networks to modern IoT Architectures IoT Fundamentals 08:45 BRKIOT-1618 Bootcamp 14:45 Industrial IoT Network Management PSOIOT-1156 16:00 using Cisco Industrial Network Director Securing Industrial – A Deep Dive. Networks: Introduction to Cisco Cyber Vision PSOIOT-2155 Enhancing the Commuter 13:30 BRKIOT-1775 Experience - Service Wireless technologies and 14:30 BRKIOT-2698 BRKIOT-1520 Provider WiFi at the Use Cases in Industrial IOT Industrial IoT Routing – Connectivity 12:15 Cisco Remote & Mobile Asset speed of Trains and Beyond Solutions PSOIOT-2197 Cisco Innovates Autonomous 14:00 TECIOT-2000 Vehicles & Roadways w/ IoT BRKIOT-2497 BRKIOT-2900 Understanding Cisco's 14:30 IoT Solutions for Smart Cities and 11:00 Automating the Network of Internet Of Things (IOT) BRKIOT-2108 Communities Industrial Automation Solutions Connected Factory Architecture Theory and 11:00 Practice PSOIOT-2100 BRKIOT-1291 Unlock New Market 16:15 Opening Keynote 09:00 08:30 Opportunities with LoRaWAN for IOT Enterprises Embedded Cisco services Technologies IOT IOT IOT Track #CLEMEA www.ciscolive.com/emea/learn/technology-tracks.html Cisco Live Thursday, Jan. 30th Celebration 18:30 Friday, Jan. 31st
Guest Keynote 17:00
BRKIOT-2548 BRKIOT-2100 Cisco Distributed 08:30 IoT and Intent-Based Networking Automation Solutions Solutions for Smart Cities and Connected Roadways 11:30 BRKIOT-2225 BRKIOT-3511 BRKIOT-2003 A security design for enabling IoT gateway scalable 09:45 Digital Building Theory & Practice deployment with Cisco Industry 4.0 Kinetic Gateway Management Module (GMM)
BRKIOT-2204 BRKIOT-2394 Leveraging industrial BRKIOT-2526 Unlocking the Mystery of Machine 09:00 device visibility and 11:15 Wi-Fi Technology in Learning and Big Data operational intent to 14:45 Industrial IoT inform security policies and controls PSOIOT-2400 Bringing IT and OT together PSOIOT-1151 to drive business benefits Achieving business 13:15 outcomes using IoT 13:30 solutions IOT IOT IOT Track #CLEMEA www.ciscolive.com/emea/learn/technology-tracks.html Agenda
1. An introduction to Data Analytics and AI/ML • Principles
2. Fundamentals of Machine Learning • Principles • Supervised learning • Unsupervised learning • Deep learning (neural networks)
3. Applications of AI/ML at Cisco – IoT and Beyond • Data Analytics for IoT • Data Center • Collaboration • Security
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 6 The Purpose of This Presentation
• AI/ML and Big Data are among the most hyped technologies today . . . • This presentation will teach you how AI/ML and Big Data Analytics really work • This presentation will give network engineers a foundation in these technologies to build network architectures for data analysis . . . especially in the world of IoT
© 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public An Introduction to Big Data Analytics and Machine Learning Data Analytics Enables Insights
• Big data and analytics entails not just a change in the volume of data, but also a change in the way we work with the data. • Traditional data warehouse tools do not uncover connections within those data sets – we need data analytics with Machine Learning!
Automation for Faster Reveal Make Data Focus on Results Hidden Patterns Driven Decisions Important Things
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 9 The Magic of Machine Learning Data is the Rocket Fuel for Machine Learning – Andrew Ng
Machine Learning Definitions Traditional, Rules-based
Arthur Samuel (1959) Live data Field of study that gives computers the Output ability to learn without being explicitly Program programmed
Tom Mitchell (1997) Machine Learning / AI
A computer program is said to learn if Training data Program its performance at a task T, as (Model) measured by a performance P, Output improves with experience E
© 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public So, What is Big Data ? (per Wikipedia definition)
Big Data is the term for a collection of data sets so large and complex that it becomes difficult to process using traditional data processing tools
© 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public Comparing the Function of Traditional Data Management Systems (Structured Data)
OLTP
Business OLTP ETL EDW Intelligence Extract, OLTP Transform, Enterprise Load Data Online Transactional Warehouse Processing Systems
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 12 Comparing Structured and Unstructured Data
Customer Name ID Phone Email Irene 14213 408-923- irene@gmail.com 1242 Walter 62342 514-231- [email protected] 2315 Miyuki 12344 416-231- [email protected] 2341
Bank Account Account Balance Transactions Number Credit Debit 234123 $1,232 $123 423142 $5,231 $611 521231 $50 $512
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 13 Unstructured vs. Structured Data
• Unstructured data is a poor fit for traditional relational databases
10,000
•More than 90% is unstructured data UNSTRUCTURED DATA •Quantity doubles every 2 years •Most unstructured data is neither
stored nor analyzed!
(IN (IN BILLIONS) GB of GB Data
STRUCTURED DATA
0
2005 2010 2015 Source: Cloudera BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 14 3Vs of Big Data
Big Data refers to distributed computing architectures specifically aimed at the “3 V’s” of data: Volume, Velocity, and Variety
VOLUME VELOCITY VARIETY
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 15 Big Data Management Systems
NoSQL (Not Only SQL) Massive Parallel Fast key-value Processing (MPP) store/retrieve in real time Shared-nothing Structured and Unstructured Structured Data (SQL)
Hadoop Distributed batch, query, and processing Unstructured Data
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 16 The Hadoop Stack
Top-Level Interfaces ETLs Viz Tools BI Tools
Mid-Level ZOO Many PIG HIVE Abstractions KEEPER more . . . (Projects)
Distributed Data MapReduce / YARN Processing
The foundational Hadoop File System (HDFS) file system
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 17 Basics of the Hadoop File System (HDFS)
Master Node
Name Node Scalability of data management and processing Data is linear with the addition of new slave nodes
Data
Data
Slave Nodes . . . Data Node Data Node Data Node
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 18 Basics of the Hadoop File System (HDFS)
Master Node
Name Node
Data
Data
Data
. . . Slave Nodes Data Node Data Node Data Node
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 19 Analyzing the Data (MapReduce)
Master Node Batch processing of Name Node Java API Application the application Job job (the job sits Job Tracker in the queue)
Task Tracker Task Tracker Task Tracker Slave Nodes . . . Data Node Data Node Data Node
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 20 MapReduce: Job Trackers and Task Trackers
Master Node Batch processing of Name Node Java API Application the application Job job (the job sits Job Tracker in the queue)
Task Tracker Task Tracker Task Tracker Slave Nodes . . . Data Node Data Node Data Node
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 21 MapReduce: Job Trackers and Task Trackers
Master Node
Name Node Application Job Job Tracker Output
Cisco Cloudera CVD: https://www.cisco.com/c/en/us/td/docs/u nified_computing/ucs/UCS_CVDs/Cisco _UCS_Integrated_Infrastructure_for_Big _Data_with_Cloudera_28node.html
Task Tracker Task Tracker Task Tracker . . . Data Node Data Node Data Node
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 22 Making Sense of Data – Fundamental Concepts of Machine Learning Machine learning will decide which IoT ‘things’ survive - Jaques Touillon, Airboxlab
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 24 Machine Learning Map
Artificial Intelligence
Any technology that allows a computing system to mimic human thinking functions Example 1 Example 2
If XYZ: do blah else: do blih
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 25 Machine Learning Map
Artificial Intelligence Machine Learning
An AI technology where all the rules are not set in the program, but are learned Example while the program is used
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 26 ML Applications
Lip Reading Deep Blue Watson Deep Mind Playing Music WaveNet (TTS)
Counting people Recognition NLP Translation Self-driving Computer Networks
And Many more: CRM, Healthcare, Personal Assistants, ….
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 27 McCullogh & Pitts - 1943
Artificial Intelligence
Binary threshold neuron
Von Neumann used this logic when designing the “universal computer”
If sum > threshold, output 1 Send weighted inputs Output 0 otherwise
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 28 In the 1950s, ML Research Expanded
• 1962, Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Frank Rosenblatt
When I show these shapes to the camera
This IBM 704 computaah can say “it’s a triangle”
(you will see why this works soon)
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 29 But then, in 1969, Marvin Minsky and Seymour Papert published the book “Perceptrons”…
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 30 …with mathematical proofs that Perceptrons can’t really “learn”.
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 31 “Perceptrons” Limitations – More words
• Minsky and Papert did not write that Perceptrons could not learn at all • But the promises of Rosenblatt’s work brought many researchers to use perceptrons for everything and anything • Many claimed that they “could learn anything”, like a human brain • “Perceptrons” proved that the model had mathematical limitations • And that therefore many claims were simply false • Big embarrassments for many researchers and their sources of fund…
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 32 In the 1970s, ML Lost It’s Appeal
Your career in Machine Learning in 1975
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 33 Until the 1990s, When Two Things Happened
Computer Sciences and GPUs appear Statistics merge Graphical Processing Units are ML is seen as a statistical tool, not very good at processing as a way to emulate the human images, which is what brain “neurons” look like
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 34 In the 2000s
Big Data Changed Everything
ML is more and more often seen as an efficient way to process large data sets
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 35 That was the story, now back to the main statement…
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 36 Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. ... Let’s look deeper.
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 37 Comparing AI, ML and Deep Learning
An AI technology where all the rules are not set in the Artificial Intelligence program, but are learned while the program is used
Machine Learning
Deep Learning A form of ML that uses Neural Learning Nets
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 38 It is a Complex Landscape
Supervised Naïve Nearest Classification SVM Learning Bayes Neighbor Develop predictive Linear Neural Decision models Regression Machine Regression Networks Trees Learning Deep ANNs CNNs Learning
Unsupervised Gaussian Hidden Clustering K-Means Learning Matrix Markov
Discover patterns Neural and anomalies Networks based on input data
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 39 Why is AI Emerging now? Cheap data storage… …and cheap processing
The decrease in storage cost has led Training ML takes a lot of compute to the emergence of Big Data power, which has become much cheaper through cloud computing and GPUs Other key factors include: Mathematical advances for training of NNs A thriving public-private ecosystem keeps on driving rapid progress in the field
Sources: Domingos, Ibid.; Mary Meeker, “Internet Trends 2014”, Kleiner Perkins Caufield Byers, 28May2014 BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 40 Supervised Learning Supervised Learning: (When You Know the Answer)
• You know the right answer, but there is too much data for you to produce the right answer for each case input into the system • Example: Oil flowing in a pipe • Comes with all sorts of viscosity / density levels • You can pump it at different pressure levels • You want to know what will be the output (cuft per seconds) • How do you do that?
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 42 Linear Regression
• You collect data about oil viscosity and pressure, and you plot these points
• Then you let the machine find the relationship between measure viscosity and output volume
• Next, when you’ll measure viscosity, you should be able to predict output
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 43 Supervised Learning – how do they do it?
Let’s do some math The red line is an equation y = ax + b
(in ML, we say 휃0 + 휃1x)
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 44 Supervised Learning – how do they do it?
Take two random 휃0 and 휃1 Correct y
Then, for every blue dot (휃0 + 휃1x)- y you have (xn, yn)
휃0 + 휃1x
Do 휃0 + 휃1x (that’s your hypothesis function) and check how far you are from yn
Take this x
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 45 Supervised Learning – how do they do it?
Repeat for each point for which you have (xn, yn) Add all these mini- differences (휃0 + 휃1x)- y The total is “how far is your theoretical line from the best line for these points”
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 46 How Close Is Our Prediction to the Dataset?
Another way to say it:
1 퐽 (휃 , 휃 ) = 푚 σ푚 ( 휃 + 휃 푥 − 푦 )2 0 1 2 푖=1 0 1 푖 푖
Okay, it’s the same thing, just said in math compact form
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 47 How Close Is Our Prediction to the Dataset?
This is called the cost function:
1 퐽 (휃 , 휃 ) = 푚 σ푚 ( 휃 + 휃 푥 − 푦 )2 0 1 2 푖=1 0 1 푖 푖
How far am I from the real y, if I use my random 휃0 and 휃1 and do 휃0 + 휃1x
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 48 How Do you Figure out 휃?
Then?
Take two new random 휃0 and 휃1 And repeat
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 49 Supervised Learning – how do they do it?
Then ask yourself:
Am I closer to the real y with the new 휃0 and 휃1 ?
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 50 Minimizing the Cost Function: Gradient Descent
This changing process is called gradient descent
You can call it brute-force
trying all 휃0 and 휃1 until you find the right values
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 51 Gradient Descent Getting Closer to the Answer
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 52 Gradient Descent Math Details
Math can help find the best next 휃0 and 휃1 We can use Calculus and derivatives
“When slope is 0… you can’t get much lower”
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 53 Supervised Learning – how do they do it?
푦 = 3.1−14푥6 - 6.4−11푥5 + 5−8푥4 - 1.8−5푥3 + 2.6−3푥2 -0.15x+95
The equation of the line you try to find can be complicated
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 54 Expanding to Many More Dimensions
And instead of 2 dimensions (x,y), You maybe have 1 million dimensions
But the idea is the same
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 55 Supervised Learning
• This was (linear) regression. You try to find a number (y). • There is also classification, where your line separates two groups
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 56 Supervised Learning
• In all cases, it is “supervised”, because you know the right answer from a bunch of samples.
• Use large number of calculations to find the ‘right’ line equation.
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 57 Supervised Learning
• The main challenge in Supervised Learning is to find the right equation… and figure out if the samples represent the full population
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 58 Supervised Learning Application
• Vending machine empties based on: Sensor data is • Product type, day of week, aggregated (sent to weather, location, local event, cloud for analysis) advertising campaigns… • Sensors report daily product quantity left • 6-dimension linear regression re-computes every day the “down to empty” curve to predict when to send the truck • Apply the same logic to ATMs, parking meters (with paper or coins) etc.
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 59 Components Necessary to Implement
• Model is trained in the cloud (infinite Cisco Kinetic Training Data is compute resources and lots of training GMM data passed to cloud
• Deploy AI/ML workload to IOx on IoT router (e.g. TensorFlow)
• New data is input to TensorFlow on edge router
Fully Trained Model is Passed to IOx
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 60 Unsupervised Learning Unsupervised Learning
• You do not know the right answer, and there is too much data for you to guess
• Example: you manufacture small engines
• Some of them will fail
• You want to spot the failures before they get installed on mowers, chainsaws, etc.
• How do you do that?
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 62 Unsupervised Learning
• You collect data about engine sound frequency, temperature, rotating speed, gas pressure, etc. • Then you plot (e.g. 2 parameters) • Then you ask the system: can you tell me what are the patterns? Which engine is “out of range”?
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 63 Unsupervised Learning
Your engine will group engines that have similar 4 groups = 4 engine categories characteristics. • In math, this is simply grouping points that are close to one another
And will spot the outliers • Those are the engines likely to fail (different from the others)
Outlier
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 64 Unsupervised Learning
The math can take may forms, but a common form is K-means Want 3 groups? Take 3 random points (3 ‘centroids’, 휇푐 푖 ) Then take a known point, calculate which centroid is closest
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 65 Unsupervised Learning
K-means Want 3 groups?
Repeat for all points. That’s our usual 퐾 푖 2 distance equation: min σ푖=1 | 푥 − 휇푐 푖 | 푐(푖)
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 66 Unsupervised Learning
Then you move your ‘centroids’, 휇푐 푖 to the center (mean x,y) of each group you formed
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 67 Unsupervised Learning
And you repeat. You are done when no point jumps to another group anymore
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 68 Unsupervised Learning
And you repeat. You are done when no point jumps to another group anymore
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 69 Unsupervised Learning
And you repeat. You are done when no point jumps to another group anymore
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 70 Unsupervised Learning
And you repeat. You are done when no point jumps to another group anymore
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 71 Unsupervised Learning Examples
You manage water in a city, you want it: • clean (no chemical contamination or poisoning), • “Working” (no breakage) • Is water flowing too much? Not enough? Is there a Superbowl commercial event? • “Safe” (no attack against sensors, valves, pumps • Yes water is an attack vector that can take control over a critical city resource… before sticking with full paralysis… can you detect the attack as it brews?
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 72 Unsupervised Learning Examples
• What is “normal” water consumption? Hard problem Linear Clustering shows outliers Multi-dimensional measurements when accidents or attacks are simulated
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 73 Unsupervised Learning Examples
• Utility Management
• You can use supervised learning to predict power consumption (weather, ToD, etc.)
• The model goes wrong when chance hits
• Will this storm affect the grid as projected?
• Can you predict where issues will popup first?
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 74 Unsupervised Learning Examples
• Can you organize shoppers in groups, even when new fashion pop up?
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 75 Deep Learning (Artificial Neural Networks) Neural Networks
Just like a neuron gets input (electricity) from one or more dendrites, and fires output (electricity in axon) if the input gets beyond a threshold
A neural network unit gets inputs, and outputs 1 if the combined input is beyond a given threshold
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 77 Neural Networks
Then you put tons of units, possibly in multiple layers (in this case, it is called deeeeep learning) Also called Artificial Neural Networks (ANNs)
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 78 Neural Networks
• The way you connect the units can vary immensely • And this is what makes this family very rich • Tons of possible applications depending on what data you are looking at, and what you try to find
© 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 79 Supervised or Unsupervised? It depends if you know the answer, or if you try to find patterns you do not see yet! SUPERVISED LEARNING
image by Jeff Dean
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 80 Machine Learning Computing Cost
• Cost depends on the algorithm, and the amount of data • You can run some on an IR829 router, or you may need a larger compute platform!
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 81 Neural Networks in IoT
• Common IoT use cases: • Image recognition (faces, handwriting, but also vehicles and objects) • Any complex combination of patterns that influence one another • e.g. multi RF parameters combined to produce connection efficiency • Deploy Kinetic EFM with TensorFlow on trucks
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 82 What Machines Can and Can’t Do (today)
• Machines can see, hear, talk, and … they can learn! • Machines can produce outcomes they have not been explicitly programmed for (a huge paradigm shift from traditional Computer Programming) • But . . . they do not have common sense, no true thinking (this is still the realm of science fiction) • Really good a things that take humans 1 second to think about
Understanding what ML algorithms can and cannot do is one of the key to success in data analytics
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 83 What Can Humans do in Less than 1s? • Machines can: • Translate from one language to another • Predict if it is going to rain or be sunny • Position targeted ads to a person visiting a web site • Identify objects in an image • Identify fraudulent credit card transactions • Actions in driving a car • Machines Can’t: • Typically anything that takes deeper reasoning, creativity, intuition, or a small amount of data
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 84 What is Cisco Doing in the World of AI/ML and Data Analytics? AI/ML Across Cisco’s Portfolio
Reimagine Secure Transform Empower your applications your data your infrastructure your teams
DNA-Center AMP Hyperflex AppDynamics Accompany
ETA Cloudlock Intersight IoT Edge Cloudcherry Intelligence DNA Analytics CTR UCS ML480 Computer Vision Tetration SD-WAN NGFW MindMeld
Stealthwatch Speaker Track
Talos “Okay Webex”
Umbrella © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public Data and Analytics for IoT The Value of IoT is in the Data! How do you access it when it’s so spread out?
• Imagine a mega-field of oil with no way to extract it from the ground
• Data needs to be extracted before it can be refined, and then used
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 88 In IoT, the “Things” are Highly Distributed
App App App
App App App
App App App App
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 89 Edge Computing + Cloud Management is Necessary
of data will be created and Smart Critical processed outside a Insights biz decision 45%75% traditional centralized data center or cloud by 2025*
Multi-Cloud Top Drivers
Cost, Efficiency, Regulatory IoT Edge & Data
Complexity
Instrumenting / Sensors / Measuring stands in the way - bringing HW/SW components together
* Gartner
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 90 Example: Fanuc • Data Collection • Edge Compute
• Predictive Analytics
• Proactive Part Replacement
CELL 07
REPLACE BEARINGS
Cisco Confidential Intelligence at the Edge is Needed Cisco IOx
• Run distributed compute at the edge
• Leverage secure connectivity of Cisco IOS software
• Manageable with on-premises or cloud- based interface
• Runs on wide variety of IoT platforms
• Builds on existing developer tools and trainings on DevNet
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 92 Major Components of Cisco IOx
IOx
Cisco Cisco Application IOS / IOx Services Local Application Hosting Manager IOS-XE Framework Software (CAF) Application Management
Linux
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 93 Example: IOx App Development with Docker
Edge
Network #> export DOCKER_HOST=tcp://***.**.***.***:****
#> exportdocker DOCKER_TLS=*run -–network=container:*****
#> export--volume=/software/ DOCKER_API_VERSION=*.*caf/work/repo-docker/*** Enable Docker Access And Create App Profile On Edge --memory= 64m docker_image_name Setup Remote Access Environment On Dev Machine
Transfer Docker Image To Edge
Run & Test Container With App Profile < / >
Developer
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 94 IOx Capable Edge Compute Technology
IoT Edge
Compute Network
CGR 1120, 1240 IC3000 IR1101 IR829 IR809 IE3000 with Compute Module
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 95 Introducing Cisco Edge Intelligence
Management App & Analytics App & Analytics EI Manager
Cloud On-Prem App Management Provider DC GW Management
Cyber Vision Edge Intelligence ISV Micro-svc
IOx - Edge Compute Infrastructure
IoT GW - Ready IoT networking/compute portfolio
Edge to multi-cloud Out of the box experience with Allows Cloud-Based data delivery centralized deployment for scale Data Analytics and AI/ML
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 96 Customer / Partner apps Customer / Partner User • Technician apps Persona • SI/OT Developer • SI/OT Soln Architect Azure/AWS IoT Hub Azure IoT Hub EI Manager for OT UI & Work Flow Azure/ AWS Infra Azure Stack/ DC Infra
Control Path Data Path Edge Intelligence Edge
GovernanceEngine NB Connector 1 NB Connector 2 NB Connector 3 NB Connector 4 NB Connector 5
Azure IoT MQTT Client AWS IoT …. …. EngineScripting
Broker
SB Connector 1 SB Connector 2 SB Connector 3 SB Connector 4 SB Connector 5 OPC ModBus EIP/CIP MQTT Server …..
IOx IC, IR, IE
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 97 Smart & Centralized to deploy across hundreds of Gateways -- Templates automatically push data policy to all locations
Streaming DB
Local Hadoop
Machine Vendor
Google AI
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 98 Building Better Engines in the Data Center for Big Data and AI/ML CPUs vs. GPUs ▪ GPUs are highly-specialized ▪ CPUs are capable of almost any processors used to solve task – but at a price complex math problems
CPUs are like a swiss army knife GPUs are like specialized surgical instruments
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 100 The Power of GPUs for Deep Learning
• Graphical Processing Units are specialized types of electronic circuitry designed to rapidly manipulate memory for graphics • GPUs support parallel processing, accelerating their ability to execute algorithms that require parallel processes • GPUs are at the heart of deep learning and neural networks
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 101 Neural Networks & GPU
Raw data Low-level features Mid-level features High-level features
Neural GPU Networks
Inherently ✓ ✓ Parallel
Matrix ✓ ✓ Operations
Bandwidth ✓ ✓
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 102 Google Brain’s YouTube Cat Video Detector
“NOW YOU CAN BUILD GOOGLE'S $1M ARTIFICIAL BRAIN ON THE CHEAP” – WIRED MAGAZINE
GOOGLE DATA CENTER STANFORD AI LAB
3-GPU Accelerated 1000 Servers / Servers / 18,432 16,000 cores cores $1,000,000 $20,000 600 KWatts 4KWatts
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 103 Cisco UCS C480 ML
GPUs 8 X V100 32GB:1st GPU to break 100 teraflops NVLink interconnect: >300GB/s bandwidth RAID controller
Storage Network Up to 24 SAS/SATA SSD/HDDs Choice of 10/25 Up to 6 NVMe drives or 40/100G M.2 SATA Four PCIe slots Two 10G Base-T shared LOMs on I/O module
CPUs 2 * Intel® Xeon® Scalable processors (up to 28 cores per socket) 24 DDR4 DIMMs — up to 3 TB memory
Redundant fans
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 104 Cisco AI/ML – Compute Portfolio
Testing and development, Deep learning/training Inferencing and model training Cisco UCS C240 Cisco HyperFlex 240 Cisco UCS C480 Cisco UCS C480 ML C/HX 220 C/HX 240
2 x P100/ V100 2 x P100/V100 per node
Option of GPU-only nodes
2 x P4 6 x PCIe P100/ V100 8x V100 with NVLink 6 x P4 Available today CY Q3’ 18 Available today CY Q4’ 18 Available today
Unified management
Cisco IMC XML API
Simplified management, customer choice, Cisco Validated Design
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 105 Tying it Together: Big Data with Machine Learning Cisco Validated Hadoop Design with Cloudera on GPU-Powered AI/ML Workloads
42 42
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 Popular ML/DL frameworks 1 2 3 BCN 4 STS
ENV 2 x UCS 6332 41 LS 41 L1 L2 CISCO UCS-FI-6332
40 40
Fabric Interconnect 39 39
38 38
37 37
36 36
35 35
34 34
33 33
32 32
31 31
30 30
29 29
28 28
27 27
26 26 Compute
25 25 24 24 Datanodes 23 23
22 22
21 21
20 20
19 19
18 18
17 17
16 x UCS C240 M5 16 16
15 15
datanode 14 14 13 13
12 12
11 11
10 10 Nvidia CUDA containers
09 09 08 08 orchestrated with YARN for Deep 07 07
06 06
05 05 Learning
S 04 04 X
03 03 X
X X X X X X X X X X X X X X X X
02 02
M U L T I ReW ritabl e DVD+ReWritable RECORDER 800 GB 800 GB 2 TB 2 TB 2 TB 2 TB 800 GB 800 GB 800 GB 800 GB 2 TB 2 TB 2 TB 2 TB 800 GB 800 GB NVMEHWH800 NVMEHWH800 HD2T7KL 6GN HD2T7KL 6GN HD2T7KL 6GN HD2T7KL 6GN NVMEHWH800 NVMEHWH800 NVMEHWH800 NVMEHWH800 HD2T7KL 6GN HD2T7KL 6GN HD2T7KL 6GN HD2T7KL 6GN NVMEHWH800 NVMEHWH800 UCS 01 01 NVME SSD NVME SSD SATA HDD SATA HDD SATA HDD SATA HDD NVME SSD NVME SSD NVME SSD NVME SSD SATA HDD SATA HDD SATA HDD SATA HDD NVME SSD NVME SSD C480 M5 Cisco Cloudera CVD: https://www.cisco.com/c/en/us/td/docs/unified_com puting/ucs/UCS_CVDs/Cisco_UCS_Integrated_Infr astructure_for_Big_Data_with_Cloudera_28node.ht ml BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 106 Applications of AI/ML to Collaboration AI-Powered Computer Vision
Spark Assistant (April) Face recognition (roadmap) In-room analytics
Noise suppression (1H CY18) Speaker tracking Best view© 2020 Cisco and/or its affiliates. All rights reserved. BotsCisco Public Webex Endpoints Built on AI
Webex Board 70 Webex Board 55 Room 70D Room 70S Room 55 Room Kit Plus Room Kit
NVIDIA Jetson Platform - The same electronics engine powering self-driving cars
© 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public Common Sounds and Noises In Meetings
• Voices…but also: • Table tapping • Keyboard • Siren • Dog barking • And more…
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 110 Classification: From Signals to Images
Voice Dog Siren Tapping
WAV Spectrogram
Voices and “noise” have a distinct “image” that can be detected and filtered. Deep Learning at Work in Cisco Collaboration Systems
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 111 Face recognition minimizes meeting friction
Brenda Song Brandon Burke Emma Totti
Simon Jones Barbara German AI Development in Computer Vision
If We Want Machines to Think, We Need to Teach Them to See. - Fei-Fei Li, Stanford AI Lab Director
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 113 AI/ML in Security Using AI/ML to Stop Attacks
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 115 Example: Body Language Says a Lot
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 116 Cisco Talos Security Intelligence & Research
Malware Reputation Protection Feeds
IPS Rules Vulnerability Database Sandboxing Updates Machine Learning Big Data Infrastructure
Millions of file Umbrella AMP Community FTD / IPS samples daily
SPARK Program Advanced Industry 100,000 True Open Source Threat Grid Disclosures Positive Communities Community Events/Day
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 117 Machine Learning and Anomaly Detection
Approach Linear Decision Boundary • Construct nominal model (aka baseline) Clustering • Assume that data which are far from the baseline - K-means are generated by a different process (anomalies) - K-NN - …
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 118 Encrypted Traffic Analytics Behavioural Analytics without seeing inside the packet
UADP 2.0 ASIC Machine learning Classify NetFlow (Classification and “known with Outliers) malware” and % enhanced “known 99 telemetry at benign” Threat Detection 1110110110000 line rate Accuracy* 0100011110011 Stealthwatch 1101001000100 001 0.01% Catalyst 9K False Positives* Switch
*Source : Identifying Encrypted Malware Traffic with Contextual Flow Data, Oct 2016 Cognitive Analytics
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 119 Cyber Vision Analytics – Protecting IoT Assets
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 120 Complete your online session • Please complete your session survey survey after each session. Your feedback is very important.
• Complete a minimum of 4 session surveys and the Overall Conference survey (starting on Thursday) to receive your Cisco Live t-shirt.
• All surveys can be taken in the Cisco Events Mobile App or by logging in to the Content Catalog on ciscolive.com/emea.
Cisco Live sessions will be available for viewing on demand after the event at ciscolive.com.
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 121 Continue your education
Demos in the Walk-In Labs Cisco Showcase
Meet the Engineer Related sessions 1:1 meetings
BRKIOT-2394 © 2020 Cisco and/or its affiliates. All rights reserved. Cisco Public 122 Thank you