SlideShare a Scribd company logo

From Research to Production: ML/DL in the Enterprise

•
•
Thomas da Silva Paula
Thomas da Silva Paula

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).

Technology
1 of 38
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 1
ML/DL in the Enterprise Thomas Paula WHO AM I? Thomas Paula tsp.thomas@gmail.com @tsp_thomas 2
ML/DL in the Enterprise Thomas Paula 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 3
ML/DL in the Enterprise Thomas Paula Part I: Motivation, Deep Learning, and State-of-the-Art 4
ML/DL in the Enterprise Thomas Paula 5 WHY SHOULD YOU CARE ABOUT IT?
ML/DL in the Enterprise Thomas Paula ARTIFICIAL INTELLIGENCE ON THE NEWS 6
ML/DL in the Enterprise Thomas Paula ARTIFICIAL INTELLIGENCE ON THE NEWS 7 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".
ML/DL in the Enterprise Thomas Paula 8 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
ML/DL in the Enterprise Thomas Paula YOU’RE PROBABLY USING DEEP LEARNING AND YOU DON’T KNOW 9
ML/DL in the Enterprise Thomas Paula DEFINITIONS 10 Source: Deep Learning Book (Goodfellow, Bengio, Courville) Artificial Intelligence Machine Learning Representation Learning Deep Learning
ML/DL in the Enterprise Thomas Paula TRADITIONAL APPROACHES Input Feature Extraction Classification ● Expert knowledge ● Time-consuming hand-tuning ● In industrial applications, it is 90% of the time ● Domain-specific 11
ML/DL in the Enterprise Thomas Paula 12 “INTUITION” OF WHAT FEATURES ARE
ML/DL in the Enterprise Thomas Paula TRADITIONAL VS DEEP LEARNING Traditional Deep Learning 13 Hand-crafted feature extractor Trained classifier Trained feature extractor Trained classifier
ML/DL in the Enterprise Thomas Paula 14
ML/DL in the Enterprise Thomas Paula WHY NOW? 15 Data Hardware Algorithms
ML/DL in the Enterprise Thomas Paula WHY COMPUTER VISION IS HARD? 16 What we see What the computer sees Source: Inspired in CS231n slides
ML/DL in the Enterprise Thomas Paula 17 Illumination Occlusion Background Clutter Deformation WHY COMPUTER VISION IS HARD? Source: Inspired in CS231n slides
ML/DL in the Enterprise Thomas Paula 18 WHY COMPUTER VISION IS HARD?
ML/DL in the Enterprise Thomas Paula 19 WHY COMPUTER VISION IS HARD?
ML/DL in the Enterprise Thomas Paula IMAGENET 20
ML/DL in the Enterprise Thomas Paula IMAGE CLASSIFICATION 21
ML/DL in the Enterprise Thomas Paula OBJECT LOCALIZATION 22
ML/DL in the Enterprise Thomas Paula EXAMPLES OF THE EVOLUTION Source: ImageNet - http:/ /image-net.org/ 23
ML/DL in the Enterprise Thomas Paula AN AUGMENTED REALITY MICROSCOPE FOR CANCER DETECTION 24 Source: https:/ /ai.googleblog.com/2018/04/an-augmented-reality-microscope.html
ML/DL in the Enterprise Thomas Paula OBJECT RECOGNITION AND SEGMENTATION 25 Source: He, Kaiming, et al. "Mask r-cnn." Computer Vision (ICCV), 2017 IEEE International Conference on. IEEE, 2017.
ML/DL in the Enterprise Thomas Paula LEARNING DEXTERITY 26 Source: Solving Rubik's Cube with a Robot Hand, 2019
ML/DL in the Enterprise Thomas Paula Part II: ML/DL in the Enterprise 27
ML/DL in the Enterprise Thomas Paula 28 WITH ALL THESE ADVANCES, HOW IS ML BEING APPLIED IN THE ENTERPRISE?
ML/DL in the Enterprise Thomas Paula 29 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
ML/DL in the Enterprise Thomas Paula 30 AND WHAT ARE THE CHALLENGES?
ML/DL in the Enterprise Thomas Paula of data science projects never make it into production¹ 31 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
ML/DL in the Enterprise Thomas Paula THERE ARE MANY COMPONENTS IN A REAL-WORLD ML SYSTEM, NOT JUST ML CODE 32 Source: Sculley, David, et al. "Hidden technical debt in machine learning systems." Advances in neural information processing systems. 2015.
ML/DL in the Enterprise Thomas Paula ENTERS MLOPS 33 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.
ML/DL in the Enterprise Thomas Paula AND A LOT OF TOOLS! 34 Sources: https:/ /course.fullstackdeeplearning.com/course-content/infrastructure-and-tooling
ML/DL in the Enterprise Thomas Paula Closing Thoughts 35
ML/DL in the Enterprise Thomas Paula 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 36
ML/DL in the Enterprise Thomas Paula MACHINE LEARNING PORTO ALEGRE 37
Photo by Alina Grubnyak on Unsplash FROM RESEARCH TO PRODUCTION: ML/DL IN THE ENTERPRISE Thomas Paula April, 22nd 2021 38 THANK YOU! QUESTIONS?

Recommended

Deep learning for fun and profit (a simple introduction to Artificial Intelli...
Deep learning for fun and profit (a simple introduction to Artificial Intelli...Deep learning for fun and profit (a simple introduction to Artificial Intelli...
Deep learning for fun and profit (a simple introduction to Artificial Intelli...Thomas da Silva Paula
 
Why big data didn’t end causal inference by Totte Harinen at Big Data Spain 2017
Why big data didn’t end causal inference by Totte Harinen at Big Data Spain 2017Why big data didn’t end causal inference by Totte Harinen at Big Data Spain 2017
Why big data didn’t end causal inference by Totte Harinen at Big Data Spain 2017Big Data Spain
 
The Data Errors we Make by Sean Taylor at Big Data Spain 2017
The Data Errors we Make by Sean Taylor at Big Data Spain 2017The Data Errors we Make by Sean Taylor at Big Data Spain 2017
The Data Errors we Make by Sean Taylor at Big Data Spain 2017Big Data Spain
 
Ten Reasons Every Business Needs Remote Workers
Ten Reasons Every Business Needs Remote WorkersTen Reasons Every Business Needs Remote Workers
Ten Reasons Every Business Needs Remote WorkersPGi
 
Machine Learning in Big Data
Machine Learning in Big DataMachine Learning in Big Data
Machine Learning in Big DataDataWorks Summit
 
Deep Learning Use Cases - Data Science Pop-up Seattle
Deep Learning Use Cases - Data Science Pop-up SeattleDeep Learning Use Cases - Data Science Pop-up Seattle
Deep Learning Use Cases - Data Science Pop-up SeattleDomino Data Lab
 
Junkbots and computational thinking
Junkbots and computational thinkingJunkbots and computational thinking
Junkbots and computational thinkingScott Turner
 
Key to a Smarter Future Leverage MLOps to scale AI ML.pdf
Key to a Smarter Future Leverage MLOps to scale AI ML.pdfKey to a Smarter Future Leverage MLOps to scale AI ML.pdf
Key to a Smarter Future Leverage MLOps to scale AI ML.pdfMindfire LLC
 

Recommended

Deep learning for fun and profit (a simple introduction to Artificial Intelli...
Deep learning for fun and profit (a simple introduction to Artificial Intelli...Deep learning for fun and profit (a simple introduction to Artificial Intelli...
Deep learning for fun and profit (a simple introduction to Artificial Intelli...Thomas da Silva Paula
 
Why big data didn’t end causal inference by Totte Harinen at Big Data Spain 2017
Why big data didn’t end causal inference by Totte Harinen at Big Data Spain 2017Why big data didn’t end causal inference by Totte Harinen at Big Data Spain 2017
Why big data didn’t end causal inference by Totte Harinen at Big Data Spain 2017Big Data Spain
 
The Data Errors we Make by Sean Taylor at Big Data Spain 2017
The Data Errors we Make by Sean Taylor at Big Data Spain 2017The Data Errors we Make by Sean Taylor at Big Data Spain 2017
The Data Errors we Make by Sean Taylor at Big Data Spain 2017Big Data Spain
 
Ten Reasons Every Business Needs Remote Workers
Ten Reasons Every Business Needs Remote WorkersTen Reasons Every Business Needs Remote Workers
Ten Reasons Every Business Needs Remote WorkersPGi
 
Machine Learning in Big Data
Machine Learning in Big DataMachine Learning in Big Data
Machine Learning in Big DataDataWorks Summit
 
Deep Learning Use Cases - Data Science Pop-up Seattle
Deep Learning Use Cases - Data Science Pop-up SeattleDeep Learning Use Cases - Data Science Pop-up Seattle
Deep Learning Use Cases - Data Science Pop-up SeattleDomino Data Lab
 
Junkbots and computational thinking
Junkbots and computational thinkingJunkbots and computational thinking
Junkbots and computational thinkingScott Turner
 
Key to a Smarter Future Leverage MLOps to scale AI ML.pdf
Key to a Smarter Future Leverage MLOps to scale AI ML.pdfKey to a Smarter Future Leverage MLOps to scale AI ML.pdf
Key to a Smarter Future Leverage MLOps to scale AI ML.pdfMindfire LLC
 
Data and analytics leadership vision for 2017
Data and analytics leadership vision for 2017Data and analytics leadership vision for 2017
Data and analytics leadership vision for 2017Sameer Dhanrajani
 
Rady School Master of Science Business Analytics (MSBA) Program Overview
Rady School Master of Science Business Analytics (MSBA) Program OverviewRady School Master of Science Business Analytics (MSBA) Program Overview
Rady School Master of Science Business Analytics (MSBA) Program OverviewUC San Diego Rady School of Management
 
Findability in the Social and Digital Workplace
Findability in the Social and Digital WorkplaceFindability in the Social and Digital Workplace
Findability in the Social and Digital WorkplaceStephan Schillerwein
 
The DNA of Data Quality and the Data Genome
The DNA of Data Quality and the Data GenomeThe DNA of Data Quality and the Data Genome
The DNA of Data Quality and the Data GenomeJohn Owens
 
Digital Strategies for Employee Engagement
Digital Strategies for Employee EngagementDigital Strategies for Employee Engagement
Digital Strategies for Employee EngagementStephan Schillerwein
 
BigMLSchool: Customer Segmentation
BigMLSchool: Customer SegmentationBigMLSchool: Customer Segmentation
BigMLSchool: Customer SegmentationBigML, Inc
 
Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...
Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...
Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...Ed Fernandez
 
Cores and Peripheries
Cores and PeripheriesCores and Peripheries
Cores and PeripheriesManage Train Learn
 
MLSEV Virtual. ML Platformization and AutoML in the Enterprise
MLSEV Virtual. ML Platformization and AutoML in the EnterpriseMLSEV Virtual. ML Platformization and AutoML in the Enterprise
MLSEV Virtual. ML Platformization and AutoML in the EnterpriseBigML, Inc
 
Collaboration strategy how-to
Collaboration strategy how-toCollaboration strategy how-to
Collaboration strategy how-toGordon Vala-Webb
 
NCMS Digital Manufacturing Strategic Interest Group Dassault
NCMS Digital Manufacturing Strategic Interest Group DassaultNCMS Digital Manufacturing Strategic Interest Group Dassault
NCMS Digital Manufacturing Strategic Interest Group DassaultThomas Francis
 
Map your best_route_to_product_value_ebook
Map your best_route_to_product_value_ebookMap your best_route_to_product_value_ebook
Map your best_route_to_product_value_ebookdynamicscom
 
Introduction Business Analytics
Introduction Business AnalyticsIntroduction Business Analytics
Introduction Business AnalyticsRenu Lamba
 
Requirements Capabilities, Alignment, and Software Success - Kappelman ASEE 2015
Requirements Capabilities, Alignment, and Software Success - Kappelman ASEE 2015Requirements Capabilities, Alignment, and Software Success - Kappelman ASEE 2015
Requirements Capabilities, Alignment, and Software Success - Kappelman ASEE 2015Leon Kappelman
 
Transparency for Startups—A Practical Guide
Transparency for Startups—A Practical GuideTransparency for Startups—A Practical Guide
Transparency for Startups—A Practical GuideMohamed Mahdy
 
How cloud are you?
How cloud are you?How cloud are you?
How cloud are you?TAUS - The Language Data Network
 
Step up to the digital skills challenge
Step up to the digital skills challengeStep up to the digital skills challenge
Step up to the digital skills challengeTim Flagg
 
Step up to the digital skills challenge
Step up to the digital skills challengeStep up to the digital skills challenge
Step up to the digital skills challengeThe ADBL
 
Odsc machine-learning-guide-v1
Odsc machine-learning-guide-v1Odsc machine-learning-guide-v1
Odsc machine-learning-guide-v1Harsh Khatke
 
Harnessing Data Growth
Harnessing Data GrowthHarnessing Data Growth
Harnessing Data GrowthEmbarcadero Technologies
 
TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....
TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....
TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....rightmanforbloodline
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusZilliz
 

More Related Content

Similar to From Research to Production: ML/DL in the Enterprise

Data and analytics leadership vision for 2017
Data and analytics leadership vision for 2017Data and analytics leadership vision for 2017
Data and analytics leadership vision for 2017Sameer Dhanrajani
 
Rady School Master of Science Business Analytics (MSBA) Program Overview
Rady School Master of Science Business Analytics (MSBA) Program OverviewRady School Master of Science Business Analytics (MSBA) Program Overview
Rady School Master of Science Business Analytics (MSBA) Program OverviewUC San Diego Rady School of Management
 
Findability in the Social and Digital Workplace
Findability in the Social and Digital WorkplaceFindability in the Social and Digital Workplace
Findability in the Social and Digital WorkplaceStephan Schillerwein
 
The DNA of Data Quality and the Data Genome
The DNA of Data Quality and the Data GenomeThe DNA of Data Quality and the Data Genome
The DNA of Data Quality and the Data GenomeJohn Owens
 
Digital Strategies for Employee Engagement
Digital Strategies for Employee EngagementDigital Strategies for Employee Engagement
Digital Strategies for Employee EngagementStephan Schillerwein
 
BigMLSchool: Customer Segmentation
BigMLSchool: Customer SegmentationBigMLSchool: Customer Segmentation
BigMLSchool: Customer SegmentationBigML, Inc
 
Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...
Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...
Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...Ed Fernandez
 
MLSEV Virtual. ML Platformization and AutoML in the Enterprise
MLSEV Virtual. ML Platformization and AutoML in the EnterpriseMLSEV Virtual. ML Platformization and AutoML in the Enterprise
MLSEV Virtual. ML Platformization and AutoML in the EnterpriseBigML, Inc
 
Collaboration strategy how-to
Collaboration strategy how-toCollaboration strategy how-to
Collaboration strategy how-toGordon Vala-Webb
 
NCMS Digital Manufacturing Strategic Interest Group Dassault
NCMS Digital Manufacturing Strategic Interest Group DassaultNCMS Digital Manufacturing Strategic Interest Group Dassault
NCMS Digital Manufacturing Strategic Interest Group DassaultThomas Francis
 
Map your best_route_to_product_value_ebook
Map your best_route_to_product_value_ebookMap your best_route_to_product_value_ebook
Map your best_route_to_product_value_ebookdynamicscom
 
Introduction Business Analytics
Introduction Business AnalyticsIntroduction Business Analytics
Introduction Business AnalyticsRenu Lamba
 
Requirements Capabilities, Alignment, and Software Success - Kappelman ASEE 2015
Requirements Capabilities, Alignment, and Software Success - Kappelman ASEE 2015Requirements Capabilities, Alignment, and Software Success - Kappelman ASEE 2015
Requirements Capabilities, Alignment, and Software Success - Kappelman ASEE 2015Leon Kappelman
 
Transparency for Startups—A Practical Guide
Transparency for Startups—A Practical GuideTransparency for Startups—A Practical Guide
Transparency for Startups—A Practical GuideMohamed Mahdy
 
Step up to the digital skills challenge
Step up to the digital skills challengeStep up to the digital skills challenge
Step up to the digital skills challengeTim Flagg
 
Step up to the digital skills challenge
Step up to the digital skills challengeStep up to the digital skills challenge
Step up to the digital skills challengeThe ADBL
 
Odsc machine-learning-guide-v1
Odsc machine-learning-guide-v1Odsc machine-learning-guide-v1
Odsc machine-learning-guide-v1Harsh Khatke
 

Similar to From Research to Production: ML/DL in the Enterprise (20)

Data and analytics leadership vision for 2017
Data and analytics leadership vision for 2017Data and analytics leadership vision for 2017
Data and analytics leadership vision for 2017
 
Rady School Master of Science Business Analytics (MSBA) Program Overview
Rady School Master of Science Business Analytics (MSBA) Program OverviewRady School Master of Science Business Analytics (MSBA) Program Overview
Rady School Master of Science Business Analytics (MSBA) Program Overview
 
Findability in the Social and Digital Workplace
Findability in the Social and Digital WorkplaceFindability in the Social and Digital Workplace
Findability in the Social and Digital Workplace
 
The DNA of Data Quality and the Data Genome
The DNA of Data Quality and the Data GenomeThe DNA of Data Quality and the Data Genome
The DNA of Data Quality and the Data Genome
 
Digital Strategies for Employee Engagement
Digital Strategies for Employee EngagementDigital Strategies for Employee Engagement
Digital Strategies for Employee Engagement
 
BigMLSchool: Customer Segmentation
BigMLSchool: Customer SegmentationBigMLSchool: Customer Segmentation
BigMLSchool: Customer Segmentation
 
Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...
Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...
Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...
 
Cores and Peripheries
Cores and PeripheriesCores and Peripheries
Cores and Peripheries
 
MLSEV Virtual. ML Platformization and AutoML in the Enterprise
MLSEV Virtual. ML Platformization and AutoML in the EnterpriseMLSEV Virtual. ML Platformization and AutoML in the Enterprise
MLSEV Virtual. ML Platformization and AutoML in the Enterprise
 
Collaboration strategy how-to
Collaboration strategy how-toCollaboration strategy how-to
Collaboration strategy how-to
 
NCMS Digital Manufacturing Strategic Interest Group Dassault
NCMS Digital Manufacturing Strategic Interest Group DassaultNCMS Digital Manufacturing Strategic Interest Group Dassault
NCMS Digital Manufacturing Strategic Interest Group Dassault
 
Map your best_route_to_product_value_ebook
Map your best_route_to_product_value_ebookMap your best_route_to_product_value_ebook
Map your best_route_to_product_value_ebook
 
Introduction Business Analytics
Introduction Business AnalyticsIntroduction Business Analytics
Introduction Business Analytics
 
Requirements Capabilities, Alignment, and Software Success - Kappelman ASEE 2015
Requirements Capabilities, Alignment, and Software Success - Kappelman ASEE 2015Requirements Capabilities, Alignment, and Software Success - Kappelman ASEE 2015
Requirements Capabilities, Alignment, and Software Success - Kappelman ASEE 2015
 
Transparency for Startups—A Practical Guide
Transparency for Startups—A Practical GuideTransparency for Startups—A Practical Guide
Transparency for Startups—A Practical Guide
 
How cloud are you?
How cloud are you?How cloud are you?
How cloud are you?
 
Step up to the digital skills challenge
Step up to the digital skills challengeStep up to the digital skills challenge
Step up to the digital skills challenge
 
Step up to the digital skills challenge
Step up to the digital skills challengeStep up to the digital skills challenge
Step up to the digital skills challenge
 
Odsc machine-learning-guide-v1
Odsc machine-learning-guide-v1Odsc machine-learning-guide-v1
Odsc machine-learning-guide-v1
 
Harnessing Data Growth
Harnessing Data GrowthHarnessing Data Growth
Harnessing Data Growth
 

Recently uploaded

TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....
TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....
TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....rightmanforbloodline
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusZilliz
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Victor Rentea
 
How to Check CNIC Information Online with Pakdata cf
How to Check CNIC Information Online with Pakdata cfHow to Check CNIC Information Online with Pakdata cf
How to Check CNIC Information Online with Pakdata cfdanishmna97
 
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamUiPathCommunity
 
The Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightThe Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightSafe Software
 
Less Is More: Utilizing Ballerina to Architect a Cloud Data Platform
Less Is More: Utilizing Ballerina to Architect a Cloud Data PlatformLess Is More: Utilizing Ballerina to Architect a Cloud Data Platform
Less Is More: Utilizing Ballerina to Architect a Cloud Data PlatformWSO2
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMECloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 
Choreo: Empowering the Future of Enterprise Software Engineering
Choreo: Empowering the Future of Enterprise Software EngineeringChoreo: Empowering the Future of Enterprise Software Engineering
Choreo: Empowering the Future of Enterprise Software EngineeringWSO2
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodJuan lago vázquez
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2
 
ChatGPT and Beyond - Elevating DevOps Productivity
ChatGPT and Beyond - Elevating DevOps ProductivityChatGPT and Beyond - Elevating DevOps Productivity
ChatGPT and Beyond - Elevating DevOps ProductivityVictorSzoltysek
 
AI+A11Y 11MAY2024 HYDERBAD GAAD 2024 - HelloA11Y (11 May 2024)
AI+A11Y 11MAY2024 HYDERBAD GAAD 2024 - HelloA11Y (11 May 2024)AI+A11Y 11MAY2024 HYDERBAD GAAD 2024 - HelloA11Y (11 May 2024)
AI+A11Y 11MAY2024 HYDERBAD GAAD 2024 - HelloA11Y (11 May 2024)Samir Dash
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontologyjohnbeverley2021
 
Modernizing Legacy Systems Using Ballerina
Modernizing Legacy Systems Using BallerinaModernizing Legacy Systems Using Ballerina
Modernizing Legacy Systems Using BallerinaWSO2
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MIND CTI
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native ApplicationsWSO2
 
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Orbitshub
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxRemote DBA Services
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfOrbitshub
 

Recently uploaded (20)

TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....
TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....
TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
 
How to Check CNIC Information Online with Pakdata cf
How to Check CNIC Information Online with Pakdata cfHow to Check CNIC Information Online with Pakdata cf
How to Check CNIC Information Online with Pakdata cf
 
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
 
The Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightThe Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and Insight
 
Less Is More: Utilizing Ballerina to Architect a Cloud Data Platform
Less Is More: Utilizing Ballerina to Architect a Cloud Data PlatformLess Is More: Utilizing Ballerina to Architect a Cloud Data Platform
Less Is More: Utilizing Ballerina to Architect a Cloud Data Platform
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMECloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Choreo: Empowering the Future of Enterprise Software Engineering
Choreo: Empowering the Future of Enterprise Software EngineeringChoreo: Empowering the Future of Enterprise Software Engineering
Choreo: Empowering the Future of Enterprise Software Engineering
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
 
ChatGPT and Beyond - Elevating DevOps Productivity
ChatGPT and Beyond - Elevating DevOps ProductivityChatGPT and Beyond - Elevating DevOps Productivity
ChatGPT and Beyond - Elevating DevOps Productivity
 
AI+A11Y 11MAY2024 HYDERBAD GAAD 2024 - HelloA11Y (11 May 2024)
AI+A11Y 11MAY2024 HYDERBAD GAAD 2024 - HelloA11Y (11 May 2024)AI+A11Y 11MAY2024 HYDERBAD GAAD 2024 - HelloA11Y (11 May 2024)
AI+A11Y 11MAY2024 HYDERBAD GAAD 2024 - HelloA11Y (11 May 2024)
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontology
 
Modernizing Legacy Systems Using Ballerina
Modernizing Legacy Systems Using BallerinaModernizing Legacy Systems Using Ballerina
Modernizing Legacy Systems Using Ballerina
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
 
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptx
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
 

From Research to Production: ML/DL in the Enterprise

  • 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 1
  • 2. ML/DL in the Enterprise Thomas Paula WHO AM I? Thomas Paula tsp.thomas@gmail.com @tsp_thomas 2
  • 3. ML/DL in the Enterprise Thomas Paula 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 3
  • 4. ML/DL in the Enterprise Thomas Paula Part I: Motivation, Deep Learning, and State-of-the-Art 4
  • 5. ML/DL in the Enterprise Thomas Paula 5 WHY SHOULD YOU CARE ABOUT IT?
  • 6. ML/DL in the Enterprise Thomas Paula ARTIFICIAL INTELLIGENCE ON THE NEWS 6
  • 7. ML/DL in the Enterprise Thomas Paula ARTIFICIAL INTELLIGENCE ON THE NEWS 7 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".
  • 8. ML/DL in the Enterprise Thomas Paula 8 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
  • 9. ML/DL in the Enterprise Thomas Paula YOU’RE PROBABLY USING DEEP LEARNING AND YOU DON’T KNOW 9
  • 10. ML/DL in the Enterprise Thomas Paula DEFINITIONS 10 Source: Deep Learning Book (Goodfellow, Bengio, Courville) Artificial Intelligence Machine Learning Representation Learning Deep Learning
  • 11. ML/DL in the Enterprise Thomas Paula TRADITIONAL APPROACHES Input Feature Extraction Classification ● Expert knowledge ● Time-consuming hand-tuning ● In industrial applications, it is 90% of the time ● Domain-specific 11
  • 12. ML/DL in the Enterprise Thomas Paula 12 “INTUITION” OF WHAT FEATURES ARE
  • 13. ML/DL in the Enterprise Thomas Paula TRADITIONAL VS DEEP LEARNING Traditional Deep Learning 13 Hand-crafted feature extractor Trained classifier Trained feature extractor Trained classifier
  • 14. ML/DL in the Enterprise Thomas Paula 14
  • 15. ML/DL in the Enterprise Thomas Paula WHY NOW? 15 Data Hardware Algorithms
  • 16. ML/DL in the Enterprise Thomas Paula WHY COMPUTER VISION IS HARD? 16 What we see What the computer sees Source: Inspired in CS231n slides
  • 17. ML/DL in the Enterprise Thomas Paula 17 Illumination Occlusion Background Clutter Deformation WHY COMPUTER VISION IS HARD? Source: Inspired in CS231n slides
  • 18. ML/DL in the Enterprise Thomas Paula 18 WHY COMPUTER VISION IS HARD?
  • 19. ML/DL in the Enterprise Thomas Paula 19 WHY COMPUTER VISION IS HARD?
  • 20. ML/DL in the Enterprise Thomas Paula IMAGENET 20
  • 21. ML/DL in the Enterprise Thomas Paula IMAGE CLASSIFICATION 21
  • 22. ML/DL in the Enterprise Thomas Paula OBJECT LOCALIZATION 22
  • 23. ML/DL in the Enterprise Thomas Paula EXAMPLES OF THE EVOLUTION Source: ImageNet - http:/ /image-net.org/ 23
  • 24. ML/DL in the Enterprise Thomas Paula AN AUGMENTED REALITY MICROSCOPE FOR CANCER DETECTION 24 Source: https:/ /ai.googleblog.com/2018/04/an-augmented-reality-microscope.html
  • 25. ML/DL in the Enterprise Thomas Paula OBJECT RECOGNITION AND SEGMENTATION 25 Source: He, Kaiming, et al. "Mask r-cnn." Computer Vision (ICCV), 2017 IEEE International Conference on. IEEE, 2017.
  • 26. ML/DL in the Enterprise Thomas Paula LEARNING DEXTERITY 26 Source: Solving Rubik's Cube with a Robot Hand, 2019
  • 27. ML/DL in the Enterprise Thomas Paula Part II: ML/DL in the Enterprise 27
  • 28. ML/DL in the Enterprise Thomas Paula 28 WITH ALL THESE ADVANCES, HOW IS ML BEING APPLIED IN THE ENTERPRISE?
  • 29. ML/DL in the Enterprise Thomas Paula 29 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
  • 30. ML/DL in the Enterprise Thomas Paula 30 AND WHAT ARE THE CHALLENGES?
  • 31. ML/DL in the Enterprise Thomas Paula of data science projects never make it into production¹ 31 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
  • 32. ML/DL in the Enterprise Thomas Paula THERE ARE MANY COMPONENTS IN A REAL-WORLD ML SYSTEM, NOT JUST ML CODE 32 Source: Sculley, David, et al. "Hidden technical debt in machine learning systems." Advances in neural information processing systems. 2015.
  • 33. ML/DL in the Enterprise Thomas Paula ENTERS MLOPS 33 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.
  • 34. ML/DL in the Enterprise Thomas Paula AND A LOT OF TOOLS! 34 Sources: https:/ /course.fullstackdeeplearning.com/course-content/infrastructure-and-tooling
  • 35. ML/DL in the Enterprise Thomas Paula Closing Thoughts 35
  • 36. ML/DL in the Enterprise Thomas Paula 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 36
  • 37. ML/DL in the Enterprise Thomas Paula MACHINE LEARNING PORTO ALEGRE 37
  • 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?