A presentation for the MBA on Digital Business Project Management in Pontifical Catholic University Rio Grande do Sul (PUCRS). The goal was to give an intro to Deep Learning and some of the challenges we have with ML in Production (MLOps).

1. Photo by Alina Grubnyak on Unsplash
Thomas Paula
MBA on Digital Business Project Management - PUCRS
Machine Learning Panel
April, 22nd
2021
FROM RESEARCH TO
PRODUCTION:
ML/DL IN THE ENTERPRISE
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WHO AM I?
Thomas Paula
tsp.thomas@gmail.com
@tsp_thomas
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AGENDA
● Part I: Motivation and definitions
○ Motivation
○ Intro to Deep Learning
○ State-of-the-art
● Part II: ML in production
○ How is ML being applied in the enterprise
○ Progress, challenges
● Closing thoughts
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Part I: Motivation, Deep Learning,
and State-of-the-Art
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WHY SHOULD YOU
CARE ABOUT IT?

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ARTIFICIAL INTELLIGENCE ON THE NEWS
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ARTIFICIAL INTELLIGENCE ON THE NEWS
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Hinton said: "(...) people should stop training radiologists now,
it's just completely obvious that in five years deep learning is
going to do better than radiologists, it might be ten years".

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Source: https:/
/business.linkedin.com/content/dam/me/business/en-us/talent-solutions/emerging-jobs-report/Emerging_Jobs_Report_U.S._FINAL.pdf
Top 10
● AI Specialist (74% annual growth)
● Robotics Engineer (40% annual growth)
● Data Scientist (37% annual growth)
● Full Stack Engineer
● Site Reliability Engineer
● Customer Success Specialist
● Sales Development Representative
● Data Engineer (33% annual growth)
● Behavioral Health Technician
● Cybersecurity Specialist

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YOU’RE PROBABLY
USING DEEP
LEARNING AND
YOU DON’T KNOW
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DEFINITIONS
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Source: Deep Learning Book (Goodfellow, Bengio, Courville)
Artificial Intelligence
Machine Learning
Representation Learning
Deep Learning

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TRADITIONAL APPROACHES
Input Feature Extraction Classification
● Expert knowledge
● Time-consuming hand-tuning
● In industrial applications, it is 90% of the time
● Domain-specific
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“INTUITION” OF WHAT FEATURES ARE

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TRADITIONAL VS DEEP LEARNING
Traditional
Deep Learning
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Hand-crafted
feature extractor
Trained classifier
Trained feature
extractor
Trained classifier

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WHY NOW?
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Data Hardware Algorithms

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WHY COMPUTER VISION IS HARD?
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What we see What the computer sees
Source: Inspired in CS231n slides

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Illumination Occlusion
Background Clutter
Deformation
WHY COMPUTER VISION IS HARD?
Source: Inspired in CS231n slides

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WHY COMPUTER VISION IS HARD?

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WHY COMPUTER VISION IS HARD?

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IMAGENET
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IMAGE CLASSIFICATION
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OBJECT LOCALIZATION
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EXAMPLES OF THE EVOLUTION
Source: ImageNet - http:/
/image-net.org/
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AN AUGMENTED REALITY MICROSCOPE FOR CANCER DETECTION
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Source: https:/
/ai.googleblog.com/2018/04/an-augmented-reality-microscope.html

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OBJECT RECOGNITION AND SEGMENTATION
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Source: He, Kaiming, et al. "Mask r-cnn." Computer Vision (ICCV), 2017 IEEE International Conference on. IEEE, 2017.

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LEARNING DEXTERITY
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Source: Solving Rubik's Cube with a Robot Hand, 2019

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Part II: ML/DL in the Enterprise
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WITH ALL THESE
ADVANCES, HOW IS
ML BEING APPLIED
IN THE ENTERPRISE?

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ALGORITHMIA: 2021 ENTERPRISE TRENDS IN ML
Source: https:/
/info.algorithmia.com/hubfs/2020/Reports/2021-Trends-in-ML/Algorithmia_2021_enterprise_ML_trends.pdf
AI/ML
Priority
AI/ML
Priority
Changes
Top AI/ML use cases

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AND WHAT ARE THE
CHALLENGES?

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of data science projects never make it into production¹
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WHAT ARE THE CHALLENGES OF ML IN PRODUCTION?
87%
businesses report that “business adoption” of AI initiatives continues to represent
a big challenge for business²
77%
of the organizations take 3 months or longer to put a model into production³
40%
In addition
● ML systems differ from traditional software-based systems in that the behavior of ML
systems is not specified directly in code but is learned from data.
● It is crucial to know not just that your ML system worked correctly at launch, but that it
continues to work correctly over time.
¹ https:/
/venturebeat.com/2019/07/19/why-do-87-of-data-science-projects-never-make-it-into-production/
² http:/
/newvantage.com/wp-content/uploads/2018/12/Big-Data-Executive-Survey-2019-Findings.pdf
³ 2021 Enterprise Trends in Machine Learning - Algorithmia

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THERE ARE MANY COMPONENTS IN A REAL-WORLD ML SYSTEM, NOT JUST ML CODE
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Source: Sculley, David, et al. "Hidden technical debt in machine learning systems." Advances in neural information processing systems. 2015.

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ENTERS MLOPS
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What is it?
● It is an ML engineering culture and practice that aims at unifying ML system development (Dev) and ML system
operation (Ops).
● Practicing MLOps means that you advocate for automation and monitoring at all steps of ML system construction,
including integration, testing, releasing, deployment, and infrastructure management.
From DevOps to MLOps - From CI/CD to CI/CD/CT/CM
● CI: how to validate data in addition to code?
● CD: how to manage the entire ML system: data, code,
model?
● CT: how to automatically retrain and serve ML models?
● CM: how to monitor production data and model
performance?
Sources: https:/
/cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning,
https:/
/nealanalytics.com/wp-content/uploads/2020/07/MLOps-Datasheet.pdf.

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AND A LOT OF TOOLS!
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Sources: https:/
/course.fullstackdeeplearning.com/course-content/infrastructure-and-tooling

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Closing Thoughts
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TAKE HOME MESSAGE
● Machine learning and deep learning are here to stay
○ However, be aware of the hype!
● There are several challenges to move from research to production
○ The challenges might be as hard (or harder) than training ML models
● Cross-area collaboration is essential
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MACHINE LEARNING PORTO ALEGRE
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38. Photo by Alina Grubnyak on Unsplash
FROM RESEARCH TO
PRODUCTION:
ML/DL IN THE ENTERPRISE
Thomas Paula
April, 22nd
2021 38
THANK YOU!
QUESTIONS?