Artificial intelligence, bots and agents, conversational interfaces, IoT, and cyborgs. Until recently, such things were more hype than substance, but now, the awaited brave new world may actually be dawning. “Pokémon GO anyone?” says Alexa. This talk will explore what these advances means for DevOps conceptually and practically. How do we help our organizations and communities deliver “applications” across multiple senses, devices, media, and realities with the same level of speed, agility, precision, and security we have achieved in much simpler realms?
2. ● Data Science
● Machine Learning/Deep Learning
● Natural Language
Processing/Conversational Agents
● Internet of Things
● Virtual Reality/Augmented Reality
Web: Transformations
3. ● Intelligent Applications
● Bots
● Multimodal Systems
● Industrial Product Design
● Hardware
● Health
● Public Safety
DevOps: New Opportunities
4. ● A state of development, preparation, or production: “several
projects in the pipeline”; also: the system for such processes: “a
strong product pipeline”
● A route, channel, or process along which something passes or is
provided at a steady rate; means, system, or flow of supply or
supplies:
“Freighters and cargo planes are a pipeline for overseas goods.”
● Workflow, Lifecycle
● Continuous Delivery, Deployment Pipeline
● Contrast to silos
Pipeline
13. ● Traditional statistics: statistical distributions
(normal distribution/Bell curve, exponential
distribution, binomial distribution). Linear and
logistic regression to predict the data based on
these numerical techniques.
● Machine learning: using the data to build the
model itself with the aid of computers
Traditional Stats vs Artificial Intelligence/Machine
Learning
14. ● Recommendation Systems
● Autonomous Vehicles
● Physics
● Real Estate
● Finance
Machine Learning Applications
15. ● “To make progress, every field of science needs
to have data commensurate with the complexity
of the phenomena it studies”
● Sciences that were data poor are now data rich:
○ Sociology: graph databases
○ Neurology: connectomes
Pedro Domingos: The Master Algorithm
16. ● Knowledge Engineers vs. Machine Learners
● Five Tribes of Machine Learning:
○ Symbolists: Expert Systems
○ Connectionists: Backpropagation, Neural
Networks
○ Evolutionaries: Genetic Programming
○ Bayesians: Bayes theorem, uncertainty
○ Analogizers: Similarity, SVM
The Master Algorithm
18. ● More neurons than previous networks
● More complex ways of connecting layers
(RNN, CNN)
● More computing power to train
● Automatic feature extraction
Deep Learning: Deep Neural Networks
28. ● Data Science is Application Development
● Ops Teams in Data Science
● Continuous Feedback
● Use of Stats/ML in DevOps (e.g., Anomaly
Detection)
DevOps and Data Science Converge
29. Previously: “a software application that runs
automated tasks (scripts) over the Internet.”
Now (AI/ML):
● Conversational Interface
● Personal Assistant
● Digital Agent
Bots
30. ● Advances in AI/ML
● Advances in NLP
● Mobile
● Messaging
● Social Networking
Rise of Bots
32. ● Context
● Generative vs. Retrieval Based
● Purpose
● Diversity
● Tone
● Interaction
● AI (Language, Image)
Conversational Interface Characteristics
33. ● Question Answering
● Recommender Systems
● Summarization
● Human Augmentation
● Sentiment Analysis
Bot/NLP Applications
34. Parts of Speech
Tagging
Quotation speaker
identification Character name
clustering
Lemmatization
Dependency
parsing
Named entity recognition
Text (Corpus) Tokenization
Pronominal coreference
resolution
NLP Pipeline
39. ● Home Appliances
● Industrial Equipment
● Medical Devices
● Vehicle Components
● Soil Sensors
IoT Smart Products
40. ● Physical Components: mechanical and electrical parts
● Smart Components: Sensors, microprocessors, data
storage, controls, software, embedded operating system,
digital user interface
● Connectivity Components: ports, antennae, protocols,
and networks that enable communication between the
product and the product cloud, which runs on remote
servers and contains the product’s external operating
system
Smart Products
41. ● Instrumented: Sensors detect conditions and changes in their
surroundings. Light, radiation, motion, heat, humidity, vibration,
sound, magnetic fields. Data collectors. Actuators, control
system or mechanism that acts on environment.
● Intelligent: Embedded microprocessors, knowledge bases,
user profile information. Make decisions, optimize outputs,
adapt to environment, trigger actions, customize UX.
● Interconnected: Wi-Fi or other, share data and decisions with
people or other products. Smart networks.
Smart Products
42. ● Product Cloud
● Devices
● Registry
● Messaging
● Data Collection/Analysis
● Digital Twin/Shadow
IoT Architecture
43. ● Continuous Verification
● Collaboration Across Engineering Disciplines
● Open Data
● Link Product, Market
● Extension to V Model (DoD ITS, DoT, Germany)
● Focus on Running Systems or Virtual Models
● Engineering Data Analytics
IBM: Continuous Engineering
45. Abstract mechanics, electronics, and software
entities to create a virtual prototype to test your
system before you build. Create executable models
that enable early analysis and tests of the
functionality, behavior, architecture, structure,
performance, reliability, and safety of the system
early in the development process.
Virtual Models
48. ● 3D-World: Surfaces, Objects, Boundaries.
● Virtual Objects: Shapes, Textures, Position
in the real world.
● Motion Tracking
● Depth Perception
● Area Learning
Augmented Reality
49. ● Marker vs Markerless AR
● GPS: Pokemon Go
● Simultaneous Localization and Mapping (SLAM):
3D Buildings
● BIM (Building Information Model) and CAVE
(Computer Augmented Virtual Environment)
Simple AR vs Complex AR
58. ● SDN: Software Defined Networking
● WAN: Wide Area Network
● LAN: Local Area Network
● MAN: Metropolitan Area Network
● PAN: Personal Area Networks
● CAN: Car Area Networks
● BAN: Body Area Networks
Delivery Pipelines: Networks
60. ● Unified Data Warehouse
● Pipeline Options (service vs platform)
● Life is just a just a collection of
microservices, we’re all headed towards the
final deployment
Transversal Delivery Pipeline