Learn more about how data scientists can have the complete self-service capability to rapidly build, train, and deploy machine learning models, and how organisations can accelerate machine learning from research to production, while preserving the flexibility and agility data scientists and modern business use cases demand.

1. MACHINE LEARNING MODELS:
FROM RESEARCH TO PRODUCTION
Tristan Zajonc | Head of Machine Learning Engineering

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WHY ARE MACHINE LEARNING MODELS IMPORTANT?
PROTECT
business
CONNECT
products &
services (IoT)
DRIVE
customer insights
The world is filled with prediction problems that require a new approach to software
● Predictive maintenance
● Logistics optimization
● Self-driving cars
● Medical diagnostics
● Marketing effectiveness
● Next best action
● Insider threat
prevention
● Fraud prevention
● Payment integrity

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SOFTWARE 1.0
Traditional development relies on hand-coded programs that map inputs to outputs
FUNCTION OUTPUTINPUT
x f(x) y
This approach is intractable or performs poorly for many problems that are easy for humans.

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SOFTWARE 2.0 - MACHINE LEARNING
FUNCTION OUTPUTINPUT
x f(θ,x) y
Machine learning searches for programs that map inputs to outputs effectively
Machine Learning
Model

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THE POWER OF MACHINE LEARNING
FUNCTION TEXTPIXELS
Machine learning enables software to address entirely new applications
Source: https://cs.stanford.edu/people/karpathy/deepimagesent/

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MACHINE LEARNING AT FACEBOOK, UBER, AND GOOGLE
Efficient, reliable, large-scale machine learning requires new supporting tools
Facebook
FBLearner
Uber
Michelangelo
Google
TFX

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ACCELERATING THREE STAGES OF MACHINE LEARNING
Manage models
Deploy models
Monitor performance
DEPLOYDEVELOP
Explore data
Develop models
Share results
TRAIN
Optimize parameters
Track experiments
Compare performance
Machine learning platforms can accelerate model development, training, and deployment

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CLOUDERA DATA SCIENCE WORKBENCH
Enables self-service data science at scale in secure environments
For data scientists
• Open data science
Use R, Python, or Scala with
your favorite libraries and on-
demand compute
• No need to sample
Directly access secure data
via Spark, Impala, or HDFS
• Reproducible,
collaborative research
Share with your whole team
For IT professionals
• Bring analysis to the data
Give data science team the
freedom to work how they
want, when they want
• Secure by default
Stay compliant with out-of-the-
box Hadoop security
• Flexible deployment
On-premises or in the cloud

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NEW IN CLOUDERA DATA SCIENCE WORKBENCH 1.4
Accelerate machine learning from research to production
DEVELOP MODELS
• Explore data securely and
develop models as a team
TRAIN MODELS
• Train, track, and compare
reproducible experiments
DEPLOY MODELS
• Deploy and monitor models
as APIs to serve predictions
NEW! NEW!

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RUN AND TRACK EXPERIMENTS

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CHALLENGE: REPRODUCIBLE RESEARCH
How do you know what model is better? How can you repeat a result?
• Model development is iterative
• Try different data, features, libraries, algorithms,
hyperparameters, etc.
• Reproducing a model means you need (at
least)...
• Training data
• Data/feature pipeline code
• Model training code + dependencies
• Runtime environment (CPU, GPU, memory, …)
• Any results or performance metrics
• This is a lot to keep track of!

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HOW THIS WORKS TODAY
WHY THIS IS A PROBLEM
• Wasted time and effort keeping
track of model/environment
changes
• Wasted time and effort trying to
recreate a result, especially for junior
data scientists / new team members
• Compliance risk due to inability to
explain the modeling process
• Source control?
• Unless you forget
• And maybe the library changes
• Or this…
• mymodel.py
• mymodel.final.py
• mymodel.final.final2.py
• mymodel.final.final2.noreallyfinal.py
• Post-it notes or notebook
• To keep track of performance metrics

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INTRODUCING EXPERIMENTS
Versioned model training runs for evaluation and reproducibility
Data scientists can now...
• Create a snapshot of model code,
dependencies, and configuration
necessary to train the model
• Build and execute the training run in an
isolated container
• Track specified model metrics,
performance, and model artifacts
• Inspect, compare, or deploy prior models

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DEMO

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DEPLOY AND MANAGE MODELS

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CHALLENGE: GETTING TO PRODUCTION
So you’ve got a trained model. Now what?
• Data scientists want to rapidly expose
candidate models to serve predictions
• REST APIs are the most requested approach
• Development and production are very different
• Owners: Data Scientists vs. Data Engineers
• Languages: Python/R vs. Java/Scala/C++
• Policy Controls: Approved code, packages, etc.
• Vocabulary: Data Science vs. DevOps
• Data scientists do not often have the skills (or
entitlements) to deploy models

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HOW THIS WORKS TODAY
WHY THIS IS A PROBLEM
• Can take days to months, raising the
bar for what’s worth deploying,
leading to stale models and reduced
innovation
• Many opportunities to introduce
errors and compliance risk
• Workarounds are difficult to
maintain while increasing the skills
gap
• Models get handed off to an
engineering team for re-coding
• Can you reproduce the model?
• Can you prove it’s the same?
• Or data science teams try to hack it
• Requires an entirely new set of tools (e.g.
Flask, Docker, Kubernetes, …)
• Getting an API up is different from
maintaining it (e.g. managing, monitoring,
testing, updating, scaling, …)

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INTRODUCING MODELS
Machine learning models as one-click microservices (REST APIs)
Model APIs made easy!
1. Choose Python/R file, e.g. score.py
2. Choose function, e.g. forecast
f = open('model.pk', 'rb')
model = pickle.load(f)
def forecast(data):
return model.predict(data)
3. Choose resources
4. Deploy!

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DEMO

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HOW IT WORKS
Experiments and Models leverage a new way of building images from source
• When running and experiment or deploying a model:
• Provides declarative pathway from version control to experiment or model
SOURCE
• Stage 1: Git snapshot of
source, respecting .gitignore
(before sure to ignore any local
Python/R environment)
IMAGE
• Stage 2: Docker build from
source; cdsw-build.sh
defines build steps, e.g.:
RUN
• Stage 3: Run versioned image
as Experiment (batch) or
Model (online) in Kubernetes
#!/bin/bash
pip3 install -r requirements.txt

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Not all machine learning applications have the same requirements
AS ALWAYS, USE THE RIGHT TOOL FOR THE JOB
• Not all models need online scoring -- batch scoring is simple and reliable
• Latency sensitive models are often better deployed to the edge
• mobile applications
• autonomous vehicles
• high-frequency trading
• Self-service deployment is not always appropriate
• Facebook scale (200 trillion predictions per day)
• Prediction services with high SLAs
But self-service model deployment enables rapid delivery for many ML use cases
and should be an available tool in every agile enterprise.

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BENEFITS OF AN UNIFIED MACHINE LEARNING PLATFORM
RESEARCH EXPERIENCE
✓ Faster iteration, with confidence
✓ Easier to identify best models
✓ Easier to reproduce/explain work
✓ Easier to onboard team members
DEPLOYMENT EXPERIENCE
✓ Faster business impact
✓ Easier to deploy more models
✓ Easier to reproduce/explain work
✓ Easier to access CDH data/compute
OPERATIONAL EXPERIENCE
✓ Lower cost and risk for managing models
✓ Easier to support through self-service
✓ Easier to scale a shared environment
✓ Easier to control access

24. THANK YOU