3 ways to move your data science projects to production: secure and scalable data science deployment...

© 2016 Continuum Analytics - Confidential & Proprietary© 2017 Continuum Analytics - Confidential & Proprietary

Three Ways to Move Your Data Science Projects to ProductionSecure and Scalable Data Science Deployment with AnacondaChristine Doig and Kris Overholt

May 24, 2017

© 2017 Continuum Analytics - Confidential & Proprietary

• Worked on MEMEX, a DARPA-funded project helping stop human trafficking

• Co-author of the recently published book, Breaking Data Science Open, published by O’Reilly

• 5+ years of experience in analytics, operations research, and machine learning

• MS in Industrial Engineering, Polytechnic University of Catalonia, Barcelona.

Christine Doig, Senior Data Scientist


• Developing the cluster management features of Anaconda • 10+ years of experience in scientific computing, systems administration,

computational modeling and more • Ph.D. in Civil Engineering, University of Texas • Master’s degree, Worcester Polytechnic Institute, focus on computational

fluid dynamics

Kris Overholt, Product Manager

© 2017 Continuum Analytics - Confidential & Proprietary 4

• Overview of Anaconda • End-to-End Collaborative Data Science Workflows • Data Science Development and Deployment

• Anaconda + Docker • Anaconda Project • Anaconda Enterprise

• Examples of Data Science Deployment • Getting Started with Anaconda Enterprise Deployment

Agenda

Overview of Anaconda


Anaconda, the leading Data Science ecosystem with over 4M users


Numba

dask

xlwings

Airflow

Blaze

Distributed Systems

Business Intelligence

Web

Scientific Computing / HPC

Machine Learning / Statistics

ANACONDA DISTRIBUTION

Python & R distribution with 1000+ curated packages that makes it easy to get started with Data Science


https://www.continuum.io/downloads

https://www.continuum.io/downloads


What’s in ANACONDA DISTRIBUTION?


• Install data science libraries

$ conda install pandas

• Manage package versions

$ conda install pandas=0.14

• Create isolated environments

$ conda create -n myenv python=3.5 pandas=0.18

• Update package version

$ conda update pandas


…


anaconda-project.yml

• Define and manage: • project package dependencies • deployment commands • data • …


• Launch applications • Manage package

versions and environments

• Create and upload projects

End-to-end Collaborative Data Science Workflows


• Explore, Analyze & Collaborate • Scale, Deploy & Operate

End-to-end Collaborative Data Science Workflows


Biz Analyst

Data Scientists

Explore, Analyze & Collaborate


DevOps

Scale, Deploy & Operate

Developer

Data Engineers

Data Science Development and Deployment


How do you… • Download and install data science libraries? • Manage versions and dependencies? • Upgrade libraries? • Isolate dependencies between projects?

Challenges in the data science ecosystem


What do data scientists develop?

Workflows

Data

Query Visualize

Clean & Tidy

Predict, Simulate, & Optimize

Reports

Presentations

Interactive Notebooks

Interactive Apps

Predictive Models

Interactive data visualizations and dashboards

Jupyter notebooks Scripts

Predictive models

Processed Data


LaptopData Science Development

scikit-learn

Bokeh Tensorflow

Jupyter pandas

matplotlib

seaborn

dask

numba

script 1 script 2 notebook A dataset Zscript 3

Python, R


How do you… • Share your data science project with others? • Ensure that you can reproduce your analysis? • Deploy your project?

Challenges in data science development and deployment


The Path to Simple Data Science Deployment!

Anaconda Enterprise

DIYAnaconda Project

Anaconda

Docker containers

conda env 1 conda env 2 conda env 3

Anaconda and Docker - Better Together


Laptop

conda env 1

Analysis 1

conda env 2 conda env 3

Analysis 2

Analysis 3

Server

conda env 1

Analysis 1

conda env 2 conda env 3

Analysis 2

Analysis 3

Docker container

Data Science DevelopmentData Science Deployment


https://hub.docker.com/r/continuumio/anaconda/

https://hub.docker.com/r/continuumio/anaconda/


• Dependencies

Anaconda and Docker

27

• Data • Deployment commands • Security • Scalability • Availability

Portable Data Science with Anaconda Project - More than just Dockerfiles


Laptop Server

Project 1 Project 2 Project 3 Project 1 Project 2 Project 3

Data Science Development Data Science Deployment


LaptopServer

Project 1 Project 2 Project 3 Project 1 Project 2 Project 3

Data Science Development Data Science Deployment

Docker container


• Dependencies • Data • Deployment commands

Anaconda Project

31

• Security • Scalability • Availability

One-click Data Science Deployments with Anaconda Enterprise


Laptop

Project 1 Project 2 Project 3

Project 1 Project 2 Project 3

Data Science Development Data Science Development and Deployment

Anaconda Enterprise

Container 1

Container 2

Container 3 Container 4


• Dependencies • Data • Deployment commands • Security • Scalability • Availability

Anaconda Enterprise

34


• One-click deployment of: • Self-Service Data Science Notebooks (Python and R) • Interactive visualizations and dashboards (Bokeh, Shiny, etc.) • Machine learning models with REST APIs

• Secure deployments to a cluster with end-to-end SSL • API wrapper for easily exposing inputs/outputs for models • Ability to securely share apps with other users, groups, and roles

(LDAP, AD, SAML, Kerberos)

Anaconda Enterprise Features - Data Science Deployment


• Ability to deploy apps and APIs that can be used/consumed via a token • Ability to configure CPU/memory limits for deployed apps in system-wide

configuration • Ability to fetch logs for each app with error handling, health checks, and

automatic app restarts • Deployments can be backed by remote storage, databases, or Hadoop/

Spark • Cluster can be configured for high availability

Anaconda Enterprise Features - Data Science Deployment

Example Data Science Deployments


• 1) Self-service notebooks • 2) Interactive visualizations and dashboards • 3) Machine learning models with REST APIs • 4) Composable data science projects • 5) Machine learning models with visualization

Examples Overview


• Self-service data science notebooks, including: • Python • R

• Notebooks with live, attached kernels • Can be used to share runnable versions of analyses • Share running notebooks with users, groups, and roles • Handle portability and manage dependencies with Anaconda Project

Example 1 - Notebooks (Python/R)


• Deploy apps using any visualization package in Anaconda, including: • Bokeh • Shiny apps • Datashader • deck.gl

• Develop and share visualizations and dashboards • Include data in project or reference remote data and databases • Deploy visualization apps powered by Hadoop and Spark

Example 2 - Interactive Visualizations

http://deck.gl


• Machine learning models and applications with REST APIs • scikit-learn, Theano, Lasagne, Neon • Tensorflow (w/ GPU), Caffe, H2O • and many more!

• Support for model scoring and prediction APIs from trained models • Compatible with web frameworks in Anaconda, including:

• Flask, Django, Tornado, and more • Models can be shared or consumed via API tokens

Example 3 - Machine Learning w/ APIs


• Deploy composable applications across your data science team • Example end-to-end workflow with custom endpoints and API tokens:

• Stage 1 - Data cleansing • Stage 2 - Anomaly detection • Stage 3 - Model scoring • Stage 4 - Interactive applications and dashboards • Stage 5 - Reports and file exports

Example 4 - Composable Deployments


• Can be built on top of machine learning libraries in Anaconda, including: • Tensorflow, H2O, and many more

• Easily develop interactive applications and dashboards with existing frameworks

• Handle inputs and outputs to machine learning models • Including complex visualization toolkits such as Tensorboard

Example 5 - ML Models with Visualization

Getting Started with Anaconda Enterprise for Data Science Deployments and More


Anaconda Platform

Anaconda Distribution

Anaconda Support

Anaconda Enterprise

•

•

•

The most trusted Python distribution for data science

Deploy Anaconda with Confidence. World class support for open source production environments.

Enterprise-ready data science platform for end-to-end workflows, including governance, collaboration, and deployment.


• Empower data scientists to easily deploy secure and scalable data science projects to production

• World class support for open-source production environments • Securely govern and version control data science artifacts (projects,

packages, installers) from development to production • Secure and scalable data science project collaboration • Manage Anaconda across a cluster and run data science projects

backed by enterprise scalable compute and data sources • Bring the power of data science to Business Analysts

Anaconda Enterprise Features


• Get started with the Anaconda Enterprise Innovator program • https://go.continuum.io/anaconda-enterprise-innovator/

• Contact us at: • [email protected] • https://www.continuum.io/contact-us

Next Steps

https://go.continuum.io/anaconda-enterprise-innovator/

mailto:[email protected]


Questions?

Christine Doig @ch_doig

Kristopher Overholt @koverholt

@ContinuumIO

3 ways to move your data science projects to production: secure and scalable data science deployment...

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