transversal delivery pipeline by mike nescot and nick grace

Transversal Delivery Pipeline

Mike Nescot, Director of DevOps & Cloud SolutionsNick Grace, Director of UX & Web Development

JBS International, Inc,

● Data Science● Machine Learning/Deep Learning● Natural Language

Processing/Conversational Agents● Internet of Things ● Virtual Reality/Augmented Reality

Web: Transformations

● Intelligent Applications● Bots● Multimodal Systems● Industrial Product Design● Hardware● Health● Public Safety

DevOps: New Opportunities

● 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

Development Test Production

Simple Web Development Pipeline

DevOps Pipeline

Transverse

● Velocity● Infrastructure as Code● Automation● Collaboration● Repeatable Process● Agility

DevOps Principles

● Big Data (3 V’s)● Data Driven Decisionmaking (Moneyball)● Predictive Analytics● Algorithms in Control

Rise of Data Science

Data Science Venn Diagram (Drew Conway)

● Programming● Data Analysis● Data Visualization

Data Science: Udacity

CleanStore

DeliveryTransform

Scrape

Scrapy MongoDB Pandas

Matplotlib

Flask

Jupyter

Explore,Analyze, Process

Pandas

D3

Data Science Pipeline

● 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

● Recommendation Systems● Autonomous Vehicles● Physics● Real Estate● Finance

Machine Learning Applications

● “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

● 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

ML Algorithms

● 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

Neuron

Artificial Neural Networks

Deep Neural Network

● 2012: AlexNet: Superhuman visual pattern recognition

● 2012: Google Speech Recognition● 2015: Amazon Echo● 2016: AlphaGo● 2016: Google Search

Deep Learning Milestones

● Parameters● Layers● Activation functions● Loss functions● Optimization methods● Hyperparameters● Backpropagation

Deep Neural Networks Architecture

Sampling(Datasets)

Data Processing

Model Selection

TrainingValidation

Data CollectionTransformation

(Feature Engineering)

Standard Machine Learning Pipeline

● Apache Kafka● Apache Spark● Apache Hadoop● Apache Zookeeper● Apache Mesos● Torch, Caffe, TensorFlow

New Big Data/ML Open Source Technologies

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

New ML Workflow

Real-Time Deep ML Pipeline: PipelineIO

● 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

Previously: “a software application that runs automated tasks (scripts) over the Internet.”

Now (AI/ML):

● Conversational Interface● Personal Assistant● Digital Agent

Bots

● Advances in AI/ML● Advances in NLP● Mobile● Messaging● Social Networking

Rise of Bots

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

Bots

● Context● Generative vs. Retrieval Based● Purpose● Diversity● Tone● Interaction● AI (Language, Image)

Conversational Interface Characteristics

● Question Answering● Recommender Systems● Summarization● Human Augmentation● Sentiment Analysis

Bot/NLP Applications

Parts of Speech Tagging

Quotation speaker identification Character name

clustering

Lemmatization

Dependency parsingNamed entity recognition

Text (Corpus) Tokenization

Pronominal coreference resolution

NLP Pipeline

Speech Synthesis

Processing

Natural Language Generation

Speech Recognition

Natural Language Understanding

Translation

Service Delivery

Conversational Agent Pipeline

● Complexity● Context● Ambiguity● Slang● Humor, sarcasm● Dynamics

NLP Challenges

● Visual● Auditory● Haptic● Kinesthetic● Proxemic

Multimodal/Multisensory Systems

InputOutputMerge

Transfer

Translate

Substitute

Visual

Haptic

Auditory

Kinesthetic

Proxemic

Deliver Visual

Haptic

Auditory

Kinesthetic

Proxemic

Multimodal Delivery Pipeline

● Home Appliances ● Industrial Equipment ● Medical Devices● Vehicle Components● Soil Sensors

IoT Smart Products

● 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

● 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

● Product Cloud● Devices● Registry● Messaging ● Data Collection/Analysis● Digital Twin/Shadow

IoT Architecture

● 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

V Model

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

Electrical/Electronics Design

Mechanical Design Requirements

Design

Testing

Manufacturing

Software Design

Virtual Model/Digital

Twin

Prototype

SmartProduct

Consumer

Continuous Engineering Pipeline

● Virtual Reality (Oculus, Vive)● Augmented Reality (Pokemon

Go, HoloLens, Project Tango)● Mixed Reality

Virtuality Continuum

● 3D-World: Surfaces, Objects, Boundaries.● Virtual Objects:  Shapes, Textures, Position

in the real world.● Motion Tracking● Depth Perception● Area Learning

Augmented Reality

● 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

BIM and CAVE

Virtual Realm

Physical Realm

Mediated Realm

MutimodalSystems

MutimodalSystems

User

VR/AR Pipeline

● 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

● Cathy O’Neill: “Weapons of Math Destruction:○ Predictive Policing○ Employment○ Education○ Finance

● Unreproducible research

Data Driven Society: Problems

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

Jupyter Notebook

Jupyter Notebook

Publish Clone/Fork Validate/Update

Pull RequestResearcher

Researcher

Notebook Pipeline

● Precision Medicine Initiative● Wearables: IoT Sensors

○ Blood Pressure○ Pulse○ Sleep○ Calories○ Activity○ Glucose

Health/Medicine

● 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

Ye Sun: BAN and CAN

● 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