The document discusses the concept of a transversal delivery pipeline, highlighting its significance in various fields including data science, machine learning, and DevOps. It examines the evolution of technology, particularly in the areas of conversational agents, smart products, and multimodal systems, while emphasizing the importance of continuous integration and collaboration. The text also addresses challenges and advancements in artificial intelligence and natural language processing, as well as the convergence of these technologies with development operations.

1. Transversal Delivery
Pipeline
Mike Nescot, Director of DevOps & Cloud Solutions
Nick Grace, Director of UX & Web Development
JBS International, Inc,

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

5. Development Test Production
Simple Web Development Pipeline

6. DevOps Pipeline

7. Transverse

8. ● Velocity
● Infrastructure as Code
● Automation
● Collaboration
● Repeatable Process
● Agility
DevOps Principles

9. ● Big Data (3 V’s)
● Data Driven Decisionmaking (Moneyball)
● Predictive Analytics
● Algorithms in Control
Rise of Data Science

10. Data Science Venn Diagram (Drew Conway)

11. ● Programming
● Data Analysis
● Data Visualization
Data Science: Udacity

12. CleanStore
DeliveryTransform
Scrape
Scrapy MongoDB Pandas
Matplotlib
Flask
Jupyter
Explore,
Analyze,
Process
Pandas
D3
Data Science 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

17. ML Algorithms

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

19. Neuron

20. Artificial Neural Networks

21. Deep Neural Network

22. ● 2012: AlexNet: Superhuman visual pattern
recognition
● 2012: Google Speech Recognition
● 2015: Amazon Echo
● 2016: AlphaGo
● 2016: Google Search
Deep Learning Milestones

23. ● Parameters
● Layers
● Activation functions
● Loss functions
● Optimization methods
● Hyperparameters
● Backpropagation
Deep Neural Networks Architecture

24. Sampling
(Datasets)
Data
Processing
Model Selection
TrainingValidation
Data Collection
Transformation
(Feature
Engineering)
Standard Machine Learning Pipeline

25. ● Apache Kafka
● Apache Spark
● Apache Hadoop
● Apache Zookeeper
● Apache Mesos
● Torch, Caffe, TensorFlow
New Big Data/ML Open Source Technologies

26. ● Real-time, Distributed (Streams)
● More Data, Bigger Networks
● “End to End Deep ML”
● Serving Models, Continuous Delivery
New ML Workflow

27. Real-Time Deep ML Pipeline: PipelineIO

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

31. ● Personality
● Name
● Purpose (God vs. special purpose)
● Interaction
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

35. Speech Synthesis
Processing
Natural
Language
Generation
Speech
Recognition
Natural Language
Understanding
Translation
Service Delivery
Conversational Agent Pipeline

36. ● Complexity
● Context
● Ambiguity
● Slang
● Humor, sarcasm
● Dynamics
NLP Challenges

37. ● Visual
● Auditory
● Haptic
● Kinesthetic
● Proxemic
Multimodal/Multisensory Systems

38. InputOutput
Merge
Transfer
Translate
Substitute
Visual
Haptic
Auditory
Kinesthetic
Proxemic
Deliver
Visual
Haptic
Auditory
Kinesthetic
Proxemic
Multimodal Delivery 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

44. V Model

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

46. Electrical/Electronics
Design
Mechanical
Design
Requirements
Design
Testing
Manufacturing
Software
Design
Virtual
Model/Digital
Twin
Prototype
Smart
Product
Consumer
Continuous Engineering Pipeline

47. ● Virtual Reality (Oculus, Vive)
● Augmented Reality (Pokemon
Go, HoloLens, Project Tango)
● Mixed Reality
Virtuality Continuum

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

50. BIM and CAVE

51. Virtual
Realm
Physical
Realm
Mediated
Realm
Mutimodal
Systems
Mutimodal
Systems
User
VR/AR Pipeline

52. ● Connectivity
● Web Capabilities: WebVR, WebRTC, Web
Workers, Device access
● Device Capabilities: Embedded operating
systems (RTOS, Linux, Docker)
● Security
● Protocol: HTTP vs. MQTT
IoT and VR/AR: Cloud/Web vs Mobile/Device

53. ● Cathy O’Neill: “Weapons of Math
Destruction:
○ Predictive Policing
○ Employment
○ Education
○ Finance
● Unreproducible research
Data Driven Society: Problems

54. ● Platform for Interactive Science
● Web IDE
● Computational Narratives
● Multimedia
● Executable Papers
● Algorithm Collaboration
● Data Journalism
● JSON
Jupyter Notebook

55. Jupyter Notebook

56. Publish Clone/Fork Validate/
Update
Pull Request
Researcher
Researcher
Notebook Pipeline

57. ● Precision Medicine Initiative
● Wearables: IoT Sensors
○ Blood Pressure
○ Pulse
○ Sleep
○ Calories
○ Activity
○ Glucose
Health/Medicine

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

59. Ye Sun: BAN and CAN

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