unveiling anaconda enterprise–the data science platform for the entire team

© 2017 Anaconda, Inc. - Confidential & Proprietary

UnveilingAnaconda Enterprise 5

The Enterprise-Ready Data Science Platform

Kristopher Overholt

Senior Product Manager


Kris Overholt

Senior Product Manager

Your Speaker For Today

• Works with DevOps, product engineering and platform infrastructure for the Anaconda Enterprise data science platform. His areas of expertise include distributed systems, data engineering and computational science workflows.

• Received his PhD in Civil Engineering from The University of Texas at Austin, and holds a BS and MS in Fire Protection Engineering. Prior to joining Anaconda, he worked at the National Institute of Standards and Technology (NIST), Southwest Research Institute and The University of Texas at Austin.

© 2017 Anaconda Inc- Confidential & Proprietary

Unveiling Anaconda Enterprise 5

• Anaconda Enterprise 5 Overview

• Anaconda Enterprise 5 Key Features

• Anaconda Enterprise 5 Demonstration

• Getting Started with Anaconda Enterprise 5

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Anaconda Enterprise 5 Overview


Anaconda - The Most Popular Data Science Platform


Anaconda Enterprise 5

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New Capabilities in Anaconda Enterprise

• Scalable architecture for on-premises or cloud installations

• Integrated data science experience, incl. JupyterLab

• One-click data science deployments

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Data Scientists IT/Administrators Business Analysts & Managers

Anaconda Enterprise Users

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Developers & ML Engineers

• Collaborate

• Reproduce

• Deploy

• Govern

• Secure

• Scale

• Self-Serve

• Interact

• Understand

• Build

• Publish

• Consume

© 2017 Anaconda Inc- Confidential & Proprietary 9

Data Science Platform

Anaconda Enterprise

Data Scientists

IT & Admin

BA & Managers

On-Premises

Cloud

Anaconda Enterprise Overview

UsersDeployment Infrastructure

Packages Projects Deployments

ML REST APIs

Dashboards

Notebooks

Web Apps

Bokeh Apps

Shiny Apps

Amazon AWS

Microsoft Azure

Google Cloud


Anaconda Enterprise 5 Key Features


• Centralized, browser-based notebook collaboration with versioning and access control

• Integrated data science environments with Jupyter Notebooks and JupyterLab

• Manage and share data science projects and dependencies

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Collaboration


• Upload and share projects and notebooks with portable data science environments

• Enterprise-grade data science reproducibility and portability with Anaconda Project

• Mirror data science packages and dependencies within your organization

Reproducibility


• One-click deployment of self-service notebooks, interactive visualizations, machine learning models, REST APIs and other apps

• Industry standard containerization and cluster orchestration technology

Deployment


• On-premises data science package repository

• License filtering and license audit reports

• Event logging and auditing of package, project and deployment activity

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Governance


• Integrated with enterprise-grade identity providers: LDAP, AD, SAML, Kerberos

• Secure network communications and end-to-end TLS/SSL encryption

• Centralized user management portal and token-based access to deployed models and applications

Security


• Distribute Anaconda libraries across Hadoop and Spark clusters

• Connect Anaconda Enterprise to Spark or Dask and perform distributed computations interactively

• Scalable distributed computation resources for project editing and user-deployed data science apps

Scalability


Anaconda Enterprise 5 Demo


Example - Tensorflow / Tensorboard

• Tensorflow with CPU/GPUs

• Deep learning

• Image classification

• Neural networks

• Easily deploy Tensorboard for ML development and performance monitoring

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Example - Deep Learning

Neural network approaches with:

• TensorFlow

• scikit-learn

• PyTorch

• Theano

• Caffe

• Keras

• Lasagne

• Neon

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Example - Classification and Regression Trees

Classification, random forests, and gradient boosting with:

• scikit-learn

• Tensorflow

• xgboost

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Example - R / Shiny

• Supports R and Microsoft R Open (MRO)

• Jupyter Notebooks with R (IRkernel)

• More than 200 popular R libraries

• One-click deployment of Shiny apps and REST APIs with R

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Example - Interactive Visualizations

• matplotlib/seaborn

• Bokeh

• plot.ly dash

• deck.gl

• and many more!

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Getting Started withAnaconda Enterprise 5


Key Strengths Unique Differentiators

Most comprehensive

data science

development cycle

- ML algorithm development and package management

- Self-service dashboards and reports for managers

Widest range of

deployment options

- Deployment of:

- Notebooks (Python, R, Spark)

- ML REST APIs

- Interactive applications: Bokeh apps, Shiny apps

- Web apps (Django, Flask)

- Composable applications

Unique continuity of

experience and

reproducibility

- Identical experience on laptop and Anaconda:

- JupyterLab, Jupyter Notebooks

- Run platform data science projects on Linux, Windows, macOS

Only vendor to support

and indemnify

Anaconda Distribution

- First-class integration with Anaconda Distribution

- Protect: Reduce Risk of Intrusions, Vulnerabilities & Infringement

- Solve: Get Questions Answered, Guided by Experts

- Deliver: Resolve Issues Quickly & Minimize Downtime


Get Started with Anaconda Enterprise

Register now for a free 30-day Anaconda Enterprise 5 Test Drivehttps://go.anaconda.com/test-drive-anaconda-enterprise-5/

Read Deploying Secure and Scalable Data Science Projectshttp://go.continuum.io/wp-productionizing-deploying-secure-scalable-ds-projects/

Contact us by phone or email+1 (512) 776-1066 | [email protected]