The document discusses challenges in building machine learning platforms and pipelines. It covers topics like data exploration challenges due to versioning issues; managing large numbers of model experiments with different hyperparameters, datasets, and performance tracking; and difficulties deploying models at scale for monitoring. The presentation demonstrates examples of machine learning applications in industries like telecommunications, manufacturing, and finance. It also discusses trends in deep learning, distributed learning, transfer learning, and edge device machine learning.

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Josh YEH | Software Engineer | Cloudera
https://www.linkedin.com/in/joshyeh/
How to Build the Next-Gen
ML/AI Platform

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“AI-Driven Companies will take $1.2 Trillion from
competitors by 2020”
~ Forrester Predictions 2017
AI decision will drive the insights revolution

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“Facebook's machine learning systems handle more
than 200 trillion predictions and five billion
translations per day. Facebook's algorithms
automatically remove millions of fake accounts every
day.”
https://www.zdnet.com/
200 trillion / day =~ 5.8 million / second

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Machine Learning Community

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Machine Learning WorkFlow Practice

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Data Science Pipeline

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Model Engineering

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Data Science Pipeline

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Data Exploration Challenges
• Leverage existing packages: panda, seaborn, scikit learn,
keras, tensorflow…
• Version compatibility hell...
• Train, deploy setup repeatedly.
• Virtual environments vs Containerization
• Personal experience: Nvidia GPU library, Tensorflow
and etc very aggressively.

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Model Experiments/Management
Trials and errors to find the “gem”

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Data Science Pipeline

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Model Experiments Challenges
• Manage models
• Different hyperparameters
• Different models
• Tracking model performance
• Storage of training artifacts
• New training dataset
• Training efficiency

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Model Deployment
How to easily deploy in large scale with monitoring

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Data Science Pipeline

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Model Deployment Challenge
• Canary testing.
• Shorten development → production cycle
• Reliability. Redundancy.
• Easiness to scale up and down.

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Demonstration
Model experiments and deployment from 0 to 60

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Demonstration
• Teleco: Customer churn, Fraud detection, ...
• Manufacture: Product quality, Predictive maintenance,
...
• Finances: Fraud detection, ...
• E-Commerce: Production recommendation, Fraud
detection
• and etc ...

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Data Science Trending
How to make what we did better, faster, more efficient?

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The Revolution of Depth of Neural Network

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Machine Learning Trends
• Distribute Learning
• Transfer Learning
• Edge-device with Machine Learning
Capability

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Thank you!
jjyeh@cloudera.com