Next-Gen ML/AI Platform

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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.

Datos y análisis

1© Cloudera, Inc. All rights reserved.
Josh YEH | Software Engineer | Cloudera
https://www.linkedin.com/in/joshyeh/
How to Build the Next-Gen
ML/AI Platform

2© Cloudera, Inc. All rights reserved.
“AI-Driven Companies will take $1.2 Trillion from
competitors by 2020”
~ Forrester Predictions 2017
AI decision will drive the insights revolution

3© Cloudera, Inc. All rights reserved.
“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

5© Cloudera, Inc. All rights reserved.
Machine Learning Community

6© Cloudera, Inc. All rights reserved.
Machine Learning WorkFlow Practice

7© Cloudera, Inc. All rights reserved.
Data Science Pipeline

8© Cloudera, Inc. All rights reserved.
Model Engineering

9© Cloudera, Inc. All rights reserved.
Data Science Pipeline

10© Cloudera, Inc. All rights reserved.
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.

11© Cloudera, Inc. All rights reserved.
Model Experiments/Management
Trials and errors to find the “gem”

12© Cloudera, Inc. All rights reserved.
Data Science Pipeline

13© Cloudera, Inc. All rights reserved.
Model Experiments Challenges
• Manage models
• Different hyperparameters
• Different models
• Tracking model performance
• Storage of training artifacts
• New training dataset
• Training efficiency

14© Cloudera, Inc. All rights reserved.
Model Deployment
How to easily deploy in large scale with monitoring

15© Cloudera, Inc. All rights reserved.
Data Science Pipeline

16© Cloudera, Inc. All rights reserved.
Model Deployment Challenge
• Canary testing.
• Shorten development → production cycle
• Reliability. Redundancy.
• Easiness to scale up and down.

18© Cloudera, Inc. All rights reserved.
Demonstration
Model experiments and deployment from 0 to 60

19© Cloudera, Inc. All rights reserved.
Demonstration
• Teleco: Customer churn, Fraud detection, ...
• Manufacture: Product quality, Predictive maintenance,
...
• Finances: Fraud detection, ...
• E-Commerce: Production recommendation, Fraud
detection
• and etc ...

20© Cloudera, Inc. All rights reserved.
Data Science Trending
How to make what we did better, faster, more efficient?

21© Cloudera, Inc. All rights reserved.
The Revolution of Depth of Neural Network

22© Cloudera, Inc. All rights reserved.
Machine Learning Trends
• Distribute Learning
• Transfer Learning
• Edge-device with Machine Learning
Capability

23© Cloudera, Inc. All rights reserved.
Thank you!
jjyeh@cloudera.com

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3. 3© Cloudera, Inc. All rights reserved. “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

10. 10© Cloudera, Inc. All rights reserved. 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.

13. 13© Cloudera, Inc. All rights reserved. Model Experiments Challenges • Manage models • Different hyperparameters • Different models • Tracking model performance • Storage of training artifacts • New training dataset • Training efficiency

18. 19© Cloudera, Inc. All rights reserved. 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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