Machine Learning Platformization and AutoML in the Enterprise, by Ed Fernández, Board Director at Arowana International.
This presentation focuses on the adoption of Machine Learning platforms and AutoML in the Enterprise, the challenges around DevOps and MLOps, latest market trends, future evolution and the impact of AutoML for rapid prototyping of Machine Learning models.
*MLSEV 2020: Virtual Conference.
MLSEV Virtual. Optimization of Passengers Waiting Time in ElevatorsBigML, Inc
Optimization of Passengers Waiting Time in Elevators using Machine Learning, by Delio Tolivia, Technical Manager of Research, Development and Innovation Projects at Talento Transformación Digital.
*MLSEV 2020: Virtual Conference.
MLSEV Virtual. From my First BigML Project to ProductionBigML, Inc
From my first BigML Project to Production, by Jose Antonio Ortega Ruiz (jao), CTO and part of the founding team of BigML.
*MLSEV 2020: Virtual Conference.
Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...Ed Fernandez
Adoption of ML at scale in the Enterprise, Machine Learning Platforms & AutoML
[1] Definitions & Context
• Machine Learning Platforms, Definitions
• ML models & apps as first class assets in the Enterprise
• Workflow of an ML application
• ML Algorithms, overview
• Architecture of a ML platform
• Update on the Hype cycle for ML & predictive apps
[2] Adopting ML at Scale
• The Problem with Machine Learning - Scaling ML in the
Enterprise
• Technical Debt in ML systems
• How many models are too many models
• The need for ML platforms
[3] The Market for ML Platforms
• ML platform Market References - from early adopters to
mainstream
• Custom Build vs Buy: ROI & Technical Debt
• ML Platforms - Vendor Landscape
[4] Custom Built ML Platforms
• ML platform Market References - a closer look
Facebook - FBlearner
Uber - Michelangelo
AirBnB - BigHead
• ML Platformization Going Mainstream: The Great Enterprise Pivot
[5] From DevOps to MLOps
• DevOps <> ModelOps
• The ML platform driven Organization
• Leadership & Accountability (labour division)
[6] Automated ML - AutoML
• Scaling ML - Rapid Prototyping & AutoML:
• Definition, Rationale
• Vendor Comparison
• AutoML - OptiML: Use Cases
[7] Future Evolution for ML Platforms
Appendix I: Practical Recommendations for ML onboarding in the Enterprise
Appendix II: List of References & Additional Resources
What is Machine Learning: A Business Perspective.
A Gentle Introduction to Machine Learning, by Enrique Dans, professor of innovation at IE Business School.
*MLSEV 2020: Virtual Conference.
Machine Learning: Past, Present and Future - by Tom DietterichBigML, Inc
There are many uses to Machine Learning. This technology began as a form of Data-Driven Software Engineering; but a more recent development is Machine Learning for Data Science: its tools can help us understand the many forms of data that are collected by companies, scientists and governments. Another important trend is Machine Learning for Optimizing Operations: for example, logistics, scheduling, advertisement placement, etc. Also, recent advances in anomaly detection are helping us understand when the results of previous Machine Learning cannot be trusted or when changes in the inputs are surprising.
Find more details here: http://www.madridml.com/en/.
The Past, Present, and Future of Machine Learning APIsBigML, Inc
Machine Learning (or Predictive) APIs can:
+ Abstract the inherent complexity of ML algorithms
+ Manage the heavy infrastructure needed to learn from data and make predictions at scale. No additional servers to provision or manage
+ Easily close the gap between model training and scoring + Be built for developers and provide full flow automation + Add traceability and repeatability to ML tasks
MLSEV Virtual. Optimization of Passengers Waiting Time in ElevatorsBigML, Inc
Optimization of Passengers Waiting Time in Elevators using Machine Learning, by Delio Tolivia, Technical Manager of Research, Development and Innovation Projects at Talento Transformación Digital.
*MLSEV 2020: Virtual Conference.
MLSEV Virtual. From my First BigML Project to ProductionBigML, Inc
From my first BigML Project to Production, by Jose Antonio Ortega Ruiz (jao), CTO and part of the founding team of BigML.
*MLSEV 2020: Virtual Conference.
Machine Learning Platformization & AutoML: Adopting ML at Scale in the Enterp...Ed Fernandez
Adoption of ML at scale in the Enterprise, Machine Learning Platforms & AutoML
[1] Definitions & Context
• Machine Learning Platforms, Definitions
• ML models & apps as first class assets in the Enterprise
• Workflow of an ML application
• ML Algorithms, overview
• Architecture of a ML platform
• Update on the Hype cycle for ML & predictive apps
[2] Adopting ML at Scale
• The Problem with Machine Learning - Scaling ML in the
Enterprise
• Technical Debt in ML systems
• How many models are too many models
• The need for ML platforms
[3] The Market for ML Platforms
• ML platform Market References - from early adopters to
mainstream
• Custom Build vs Buy: ROI & Technical Debt
• ML Platforms - Vendor Landscape
[4] Custom Built ML Platforms
• ML platform Market References - a closer look
Facebook - FBlearner
Uber - Michelangelo
AirBnB - BigHead
• ML Platformization Going Mainstream: The Great Enterprise Pivot
[5] From DevOps to MLOps
• DevOps <> ModelOps
• The ML platform driven Organization
• Leadership & Accountability (labour division)
[6] Automated ML - AutoML
• Scaling ML - Rapid Prototyping & AutoML:
• Definition, Rationale
• Vendor Comparison
• AutoML - OptiML: Use Cases
[7] Future Evolution for ML Platforms
Appendix I: Practical Recommendations for ML onboarding in the Enterprise
Appendix II: List of References & Additional Resources
What is Machine Learning: A Business Perspective.
A Gentle Introduction to Machine Learning, by Enrique Dans, professor of innovation at IE Business School.
*MLSEV 2020: Virtual Conference.
Machine Learning: Past, Present and Future - by Tom DietterichBigML, Inc
There are many uses to Machine Learning. This technology began as a form of Data-Driven Software Engineering; but a more recent development is Machine Learning for Data Science: its tools can help us understand the many forms of data that are collected by companies, scientists and governments. Another important trend is Machine Learning for Optimizing Operations: for example, logistics, scheduling, advertisement placement, etc. Also, recent advances in anomaly detection are helping us understand when the results of previous Machine Learning cannot be trusted or when changes in the inputs are surprising.
Find more details here: http://www.madridml.com/en/.
The Past, Present, and Future of Machine Learning APIsBigML, Inc
Machine Learning (or Predictive) APIs can:
+ Abstract the inherent complexity of ML algorithms
+ Manage the heavy infrastructure needed to learn from data and make predictions at scale. No additional servers to provision or manage
+ Easily close the gap between model training and scoring + Be built for developers and provide full flow automation + Add traceability and repeatability to ML tasks
As part of the IBM PartyCloud 2018 in Milan, the talk "A Journey into Data Science & AI" will present a case study about estimating Panelists Latent Affinities. I will show the components to develop an intelligent social agent able to classify entities and estimate latent affinities. The session will also cover good practices and common challenges faced by R&D organizations dealing with Machine Learning products.
MLSEV Virtual. One Platform to Rule Them AllBigML, Inc
One Platform to Rule Them All, by Francis Cepero, Head of Vertical Market Solutions at A1Digital, a spinoff of the Telekom Austria Group. This talk presents how to create a data analytics platform that delivers insights for the connected enterprise.
*MLSEV 2020: Virtual Conference.
CD4ML and the challenges of testing and quality in ML systemsSeldon
Speaker: Danilo Sato, principal consultant at ThoughtWorks.
Bio: Danilo Sato (@dtsato) is a principal consultant at ThoughtWorks with experience in many areas of architecture and engineering: software, data, infrastructure, and machine learning. He is the author of "DevOps in Practice: Reliable and Automated Software Delivery", a member of ThoughtWorks Technology Advisory Board, and ThoughtWorks Office of the CTO.
Title: CD4ML and the challenges of testing and quality in ML systems
Abstract: Continuous Delivery for Machine Learning (CD4ML) deals with the challenges of applying Continuous Delivery principles to ML systems to make the end-to-end process of developing and deploying them more repeatable and reliable. These systems are generally more complex than traditional software applications, and ML models are non-deterministic and hard to explain. In this talk we will discuss the challenges of testing and quality in ML systems, and share some practices for applying different types of tests to help overcome those issues.
www.devopsinpractice.com
www.devopsnapratica.com.br
When you wake up in the morning, you probably unlock your smartphone with your fingerprint, talk to it in your own language to open your email or agenda or weather apps, ask for a recommendation for a meeting later in the day and look for the shortest path to its location. Our lives are being reshaped thanks to the amount of available data, to the computing capabilities, to Machine Learning (ML) and recently Deep Learning (DL) algorithms.
How does a ML algorithm work? What are the steps to take to success an ML project? What should one do to apply DL? Is ML hard to Learn? Is it hard to apply?
Automating your own Machine Learning Projects - Workshop: Working with the Masters.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
John Robert: Making your machine learning model usable by othersLviv Startup Club
John Robert: Making your machine learning model usable by others
Data Science Online Camp 2021
Website - https://dscamp.org/
Youtube - https://www.youtube.com/startuplviv
FB - https://www.facebook.com/Data-Science-Camp-103012708431833
Making machine learning model deployment boring - Big Data Expo 2019webwinkelvakdag
The free lunch for machine learning is over. Organizations are quickly ramping up their abilities to automate and professionalize their machine learning processes and infrastructure. As a consequence organizational goals, processes and requirements put an increasing burden on teams to put machine learning models in production. We believe much of this burden relates to engineering issues, which with proper abstractions can be greatly reduced for product teams. In this presentation we will talk about the organizational context of ING and the design our Machine Learning Platform. In the first part we will sketch some organizational context and the requirements it brings. Next, we will picture the kind of use cases and user journey we have in mind. Finally, we will present how these considerations led the platform design we are currently deploying.
The session is about creating, training, evaluating and deploying machine learning with no-code approach using Azure AutoML.
* NO MACHINE LEARNING EXPERIENCE REQUIRED *
Agenda:
1. Introduction to Machine Learning
2. What is AutoML (Automated Machine Learning) ?
3. AutoML versus Conventional ML practices
4. Intro to Azure Automated Machine Learning
5. Hands-on demo
6 Contest
6. Learning resources
7. Conclusion
Kyrylo Perevozchykov "Continuous delivery for Machine Learning, the future of...Fwdays
MLOps itself is a derivative of DevOps, the thought being that there is an entire industry that exists for “Ops” for normal software, and that such an industry will need to emerge for ML as well. But it hasn’t yet. Various technologies has made it easy for people to build predictive models, so people have lots of predictive models now. But to get value out of models you have to deploy, monitor, and maintain them. Very few people know how to do this, even fewer than know how to build a good model in the first place.
This talk will be dedicated to the plans of what is MLOps, what is cases and how it will develop and evolve into a new industry.
BigMLSchool: ML Platforms and AutoML in the EnterpriseBigML, Inc
An introductory session on the current situation of Machine Learning, the importance of ML platforms and AutoML, and why ML needs to be properly taught at schools and universities.
The lecturer is Ed Fernández, Board Director at BigML and Arowana International, a Private Equity firm, Faculty at Northeastern University (the Silicon Valley campus), lecturer at Headspring Corporate Learning (the Joint Venture of Financial Times and IE Business School), and mentor at Berkeley Sutardja Center for Entrepreneurship and Technology.
*Machine Learning School for Business Schools 2021: Virtual Conference.
.NET Fest 2018. Оля Гавриш. Машинное обучение для .NET разработчиков с помощь...NETFest
А Вы знали, что практически для каждого проекта можно применить машинное обучение? К счастью времена, когда для этого нужно было становится математиком-аналитиком давно прошли. Больше нет необходимасти изучать новый язык программирования (как Python или R) и осваивать численные методы. Теперь, благодаря ML.NET, Вы можете программировать в хорошо знакомой .NET среде и использовать уже реализованные для Вас алгоритмы и методы обработки данных. ML.NET – это расширяемый .NET фреймворк для машинного обучения. В этом докладе Вы узнаете, что уже доступно в ML.NET и что планируется в следующих версиях. Мы вместе напишем в Visual Studio модель для машинного обучения с помощью нескольких строк C# кода и поговорим о том, как улучшать Ваши приложения применяя методы искусственного интеллекта.
As part of the IBM PartyCloud 2018 in Milan, the talk "A Journey into Data Science & AI" will present a case study about estimating Panelists Latent Affinities. I will show the components to develop an intelligent social agent able to classify entities and estimate latent affinities. The session will also cover good practices and common challenges faced by R&D organizations dealing with Machine Learning products.
MLSEV Virtual. One Platform to Rule Them AllBigML, Inc
One Platform to Rule Them All, by Francis Cepero, Head of Vertical Market Solutions at A1Digital, a spinoff of the Telekom Austria Group. This talk presents how to create a data analytics platform that delivers insights for the connected enterprise.
*MLSEV 2020: Virtual Conference.
CD4ML and the challenges of testing and quality in ML systemsSeldon
Speaker: Danilo Sato, principal consultant at ThoughtWorks.
Bio: Danilo Sato (@dtsato) is a principal consultant at ThoughtWorks with experience in many areas of architecture and engineering: software, data, infrastructure, and machine learning. He is the author of "DevOps in Practice: Reliable and Automated Software Delivery", a member of ThoughtWorks Technology Advisory Board, and ThoughtWorks Office of the CTO.
Title: CD4ML and the challenges of testing and quality in ML systems
Abstract: Continuous Delivery for Machine Learning (CD4ML) deals with the challenges of applying Continuous Delivery principles to ML systems to make the end-to-end process of developing and deploying them more repeatable and reliable. These systems are generally more complex than traditional software applications, and ML models are non-deterministic and hard to explain. In this talk we will discuss the challenges of testing and quality in ML systems, and share some practices for applying different types of tests to help overcome those issues.
www.devopsinpractice.com
www.devopsnapratica.com.br
When you wake up in the morning, you probably unlock your smartphone with your fingerprint, talk to it in your own language to open your email or agenda or weather apps, ask for a recommendation for a meeting later in the day and look for the shortest path to its location. Our lives are being reshaped thanks to the amount of available data, to the computing capabilities, to Machine Learning (ML) and recently Deep Learning (DL) algorithms.
How does a ML algorithm work? What are the steps to take to success an ML project? What should one do to apply DL? Is ML hard to Learn? Is it hard to apply?
Automating your own Machine Learning Projects - Workshop: Working with the Masters.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
John Robert: Making your machine learning model usable by othersLviv Startup Club
John Robert: Making your machine learning model usable by others
Data Science Online Camp 2021
Website - https://dscamp.org/
Youtube - https://www.youtube.com/startuplviv
FB - https://www.facebook.com/Data-Science-Camp-103012708431833
Making machine learning model deployment boring - Big Data Expo 2019webwinkelvakdag
The free lunch for machine learning is over. Organizations are quickly ramping up their abilities to automate and professionalize their machine learning processes and infrastructure. As a consequence organizational goals, processes and requirements put an increasing burden on teams to put machine learning models in production. We believe much of this burden relates to engineering issues, which with proper abstractions can be greatly reduced for product teams. In this presentation we will talk about the organizational context of ING and the design our Machine Learning Platform. In the first part we will sketch some organizational context and the requirements it brings. Next, we will picture the kind of use cases and user journey we have in mind. Finally, we will present how these considerations led the platform design we are currently deploying.
The session is about creating, training, evaluating and deploying machine learning with no-code approach using Azure AutoML.
* NO MACHINE LEARNING EXPERIENCE REQUIRED *
Agenda:
1. Introduction to Machine Learning
2. What is AutoML (Automated Machine Learning) ?
3. AutoML versus Conventional ML practices
4. Intro to Azure Automated Machine Learning
5. Hands-on demo
6 Contest
6. Learning resources
7. Conclusion
Kyrylo Perevozchykov "Continuous delivery for Machine Learning, the future of...Fwdays
MLOps itself is a derivative of DevOps, the thought being that there is an entire industry that exists for “Ops” for normal software, and that such an industry will need to emerge for ML as well. But it hasn’t yet. Various technologies has made it easy for people to build predictive models, so people have lots of predictive models now. But to get value out of models you have to deploy, monitor, and maintain them. Very few people know how to do this, even fewer than know how to build a good model in the first place.
This talk will be dedicated to the plans of what is MLOps, what is cases and how it will develop and evolve into a new industry.
BigMLSchool: ML Platforms and AutoML in the EnterpriseBigML, Inc
An introductory session on the current situation of Machine Learning, the importance of ML platforms and AutoML, and why ML needs to be properly taught at schools and universities.
The lecturer is Ed Fernández, Board Director at BigML and Arowana International, a Private Equity firm, Faculty at Northeastern University (the Silicon Valley campus), lecturer at Headspring Corporate Learning (the Joint Venture of Financial Times and IE Business School), and mentor at Berkeley Sutardja Center for Entrepreneurship and Technology.
*Machine Learning School for Business Schools 2021: Virtual Conference.
.NET Fest 2018. Оля Гавриш. Машинное обучение для .NET разработчиков с помощь...NETFest
А Вы знали, что практически для каждого проекта можно применить машинное обучение? К счастью времена, когда для этого нужно было становится математиком-аналитиком давно прошли. Больше нет необходимасти изучать новый язык программирования (как Python или R) и осваивать численные методы. Теперь, благодаря ML.NET, Вы можете программировать в хорошо знакомой .NET среде и использовать уже реализованные для Вас алгоритмы и методы обработки данных. ML.NET – это расширяемый .NET фреймворк для машинного обучения. В этом докладе Вы узнаете, что уже доступно в ML.NET и что планируется в следующих версиях. Мы вместе напишем в Visual Studio модель для машинного обучения с помощью нескольких строк C# кода и поговорим о том, как улучшать Ваши приложения применяя методы искусственного интеллекта.
Accelerating Machine Learning as a Service with Automated Feature EngineeringCognizant
Building scalable machine learning as a service, or MLaaS, is critical to enterprise success. Key to translate machine learning project success into program success is to solve the evolving convoluted data engineering challenge, using local and global data. Enabling sharing of data features across a multitude of models within and across various line of business is pivotal to program success.
Команда Data Phoenix Events приглашает всех, 17 августа в 19:00, на первый вебинар из серии "The A-Z of Data", который будет посвящен MLOps. В рамках вводного вебинара, мы рассмотрим, что такое MLOps, основные принципы и практики, лучшие инструменты и возможные архитектуры. Мы начнем с простого жизненного цикла разработки ML решений и закончим сложным, максимально автоматизированным, циклом, который нам позволяет реализовать MLOps.
https://dataphoenix.info/the-a-z-of-data/
https://dataphoenix.info/the-a-z-of-data-introduction-to-mlops/
Enterprise adoption of AI/ML services has significantly accelerated in recent years. However, the majority of ML models are still developed with the goal of solving a single task, e.g., prediction, classification. In this talk, we emphasize on the compositionality aspect that enables seamless composition / orchestration of existing data and models addressing complex multi-domain use-cases. This enables reuse, agility, and efficiency in model development and maintenance efforts. We then extend this concept to the Generative AI world, discussing the different LLMOps architectural patterns enabling composition of Large Language Models (LLMs) and AI Agents.
2022: AI/ML Workloads in Containers: 6 Key FactsWeCode Inc
Before IT leaders and their teams begin to dig into the nitty-gritty technical aspects of containerizing AI/ML workloads, some principles are worth thinking about up front. Here are six fact to consider. today’s big IT trends, AI/ML and containers, have become part of the same conversation at many organisations. They’re increasingly paired together, as teams look for better ways to manage their AI and ML workloads – enabled by a growing menu of commercial and open source technologies for doing so. The best news for IT leaders is that tooling and processes for running machine learning at scale in containers has improved significantly over the past few years,” says Blair Hanley Frank, enterprise technology analyst at ISG. “There is no shortage of available open source tooling, commercial products, and tutorials to help data scientists and IT teams get these systems up and running. https://wecode-inc.com/service/next-generation-technology.html
Many businesses have developed and implemented a variety of AI use cases. However, to become a truly AI-enabled organization, several standalone use cases must be developed, maintained, and deployed to address various challenges across the enterprise. Machine Learning Operations (MLOps) promises to make it seamless to leverage the potential of AI without hassle.
DutchMLSchool. ML for Energy Trading and Automotive SectorBigML, Inc
Machine Learning for Energy Trading, Automotive Sector, and Logistics, presented by BigML's Partners A1 Digital.
Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
AI & ML: Although it has been around for a long time, artificial intelligence was once thought to be incredibly challenging. It was typical for scientists and developers to avoid examining utilising it.
To know more about, Top AI & ML tools and frameworks, see https://www.logic-fruit.com/blog/al-ml/top-ai-ml-tools-and-frameworks/
About Logic Fruit Technologies
Logic Fruit Technologies is a product engineering R&D & consulting services provider for embedded systems and application development. We provide end-to-end solutions from the conception of the idea and design to the finished product. We have been servicing customers globally for over a decade.
The company has specific experience in various fields, such as
FPGA Design & hardware design
RTL IP Design
A variety of digital protocols
Communication buses as1G, 10G Ethernet
PCIe
DIGRF
STM16/64
HDMI.
Logic Fruit Technologies is also an expert in developing,
software-defined radio (SDR) IPs
Encryption
Signal generation
Data analysis, and
Multiple Image Processing Techniques.
Recently Logic Fruit technologies are also exploring FPGA acceleration on data centers for real-time data processing.
**Our Social Media Channels**
Facebook: https://www.facebook.com/LogicFruit/
Twitter: https://twitter.com/logicfruit
LinkedIn: https://www.linkedin.com/company/logi…
Website: https://www.logic-fruit.com/
#LFT #LogicFruitTechnologies #LogicFruit
Interested to view more SlideShares, Click on the below links,
https://www.slideshare.net/LogicFruit/a-designers-practical-guide-to-arinc-429-standard-3pptx
https://www.slideshare.net/LogicFruit/a-swift-introduction-to-milstd
https://www.slideshare.net/LogicFruit/arinc-the-ultimate-guide-to-modern-avionics-protocol/LogicFruit/arinc-the-ultimate-guide-to-modern-avionics-protocol
https://www.slideshare.net/LogicFruit/arinc-629-digital-data-bus-specifications/LogicFruit/arinc-629-digital-data-bus-specifications
https://www.slideshare.net/LogicFruit/afdx
https://www.slideshare.net/LogicFruit/end-system-design-parameters-of-the-arinc-664-part-7
https://www.slideshare.net/LogicFruit/compute-express-link-cxl-everything-you-ought-to-know
https://www.logic-fruit.com/blog/fpga/what-is-fpga/
https://www.slideshare.net/LogicFruit/cxl-vs-pcie-gen-5-the-brief-comparison
https://www.slideshare.net/LogicFruit/fpga-technology-development-and-market-trends-in-the-new-decade
https://www.slideshare.net/LogicFruit/fpga-design-an-ultimate-guide-for-fpga-enthusiasts
https://www.slideshare.net/LogicFruit/fpga-vs-asic-design-comparison
https://www.slideshare.net/LogicFruit/afdx-a-timedeterministic-application-of-arinc-664-part-7
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https://www.slideshare.net/LogicFruit/arinc-8182-standard-overview-and-its-characteristics
Machine learning and artificial intelligence are two of the most rapidly growing and transformative technologies of our time. These technologies are revolutionizing the way businesses operate, improving healthcare outcomes, and transforming the way we live our daily lives. Learn more about it in the PPT below!
Studying Software Engineering Patterns for Designing Machine Learning SystemsHironori Washizaki
Hironori Washizaki, Hiromu Uchida, Foutse Khomh and Yann-Gaël Guéhéneuc, “Studying Software Engineering Patterns for Designing Machine Learning Systems,” The 10th International Workshop on Empirical Software Engineering in Practice (IWESEP 2019), Tokyo, Japan, on December 13-14, 2019.
Digital Transformation and Process Optimization in ManufacturingBigML, Inc
Keyanoush Razavidinani, Digital Services Consultant at A1 Digital, a BigML Partner, highlights why it is important to identify and reduce human bottlenecks that optimize processes and let you focus on important activities. Additionally, Guillem Vidal, Machine Learning Engineer at BigML completes the session by showcasing how Machine Learning is put to use in the manufacturing industry with a use case to detect factory failures.
The Road to Production: Automating your Anomaly Detectors - by jao (Jose A. Ortega), Co-Founder and Chief Technology Officer at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - ML for AML ComplianceBigML, Inc
Machine Learning for Anti Money Laundering Compliance, by Kevin Nagel, Consultant and Data Scientist at INFORM.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Multi Perspective AnomaliesBigML, Inc
Multi Perspective Anomalies, by Jan W Veldsink, Master in the art of AI at Nyenrode, Rabobank, and Grio.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - My First Anomaly Detector BigML, Inc
My First Anomaly Detector: Practical Workshop, by Mercè Martín, VP of Bindings and Applications at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - History and Developments in MLBigML, Inc
History and Present Developments in Machine Learning, by Tom Dietterich, Emeritus Professor of computer science at Oregon State University and Chief Scientist at BigML.
*Machine Learning School in The Netherlands 2022.
Introduction to End-to-End Machine Learning: Classification and Regression - Mercè Martín, VP of Bindings and Applications at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - A Data-Driven CompanyBigML, Inc
A Data-Driven Company: 21 Lessons for Large Organizations to Create Value from AI, by Richard Benjamins, Chief AI and Data Strategist at Telefónica.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - ML in the Legal SectorBigML, Inc
How Machine Learning Transforms and Automates Legal Services, by Arnoud Engelfriet, Co-Founder at Lynn Legal.
*Machine Learning School in The Netherlands 2022.
Machine Learning for Public Safety: Reducing Violence and Discrimination in Stadiums.
Speakers: Ramon van Ingen, Co-Founder at Siip, Entrepreneur, Researcher, and Pablo González, Machine Learning Engineer at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Process Optimization in Manufacturing PlantsBigML, Inc
Process Optimization in Manufacturing Plants, by Keyanoush Razavidinani, Digital Business Consultant at A1 Digital.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Anomaly Detection at ScaleBigML, Inc
Lessons Learned Applying Anomaly Detection at Scale, by Álvaro Clemente, Machine Learning Engineer at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Citizen Development in AIBigML, Inc
Citizen Development in AI, by Jan W Veldsink, Master in the art of AI at Nyenrode, Rabobank, and Grio.
*Machine Learning School in The Netherlands 2022.
This new feature is a continuation of and improvement on our previous Image Processing release. Now, Object Detection lets you go a step further with your image data and allows you to locate objects and annotate regions in your images. Once your image regions are defined, you can train and evaluate Object Detection models, make predictions with them, and automate end-to-end Machine Learning workflows on a single platform. To make that possible, BigML enables Object Detection by introducing the regions optype.
As with any other BigML feature, Object Detection is available from the BigML Dashboard, API, and WhizzML for automation. Object Detection is extremely helpful to tackle a wide range of computer vision use cases such as medical image analysis, quality control in manufacturing, license plate recognition in transportation, people detection in security surveillance, among many others.
This new release brings Image Processing to the BigML platform, a feature that enhances our offering to solve image data-driven business problems with remarkable ease of use. Because BigML treats images as any other data type, this unique implementation allows you to easily use image data alongside text, categorical, numeric, date-time, and items data types as input to create any Machine Learning model available in our platform, both supervised and unsupervised.
Now, it is easier than ever to solve a wide variety of computer vision and image classification use cases in a single platform: label your image data, train and evaluate your models, make predictions, and automate your end-to-end Machine Learning workflows. As with any other BigML feature, Image Processing is available from the BigML Dashboard, API, and WhizzML, and it can be applied to solve use cases such as medical image analysis, visual product search, security surveillance, and vehicle damage detection, among others.
Machine Learning in Retail: Know Your Customers' Customer. See Your FutureBigML, Inc
This session presents a quite common situation for those working in food and beverage retail (FnB) and highlights interesting insights to fight waste reduction.
Speaker: Stephen Kinns, CEO and Co-Founder at catsAi.
*ML in Retail 2021: Webinar.
Machine Learning in Retail: ML in the Retail SectorBigML, Inc
This is an introductory session about the role that Machine Learning is playing in the retail sector and how it is being deployed across the different areas of this industry.
Speaker: Atakan Cetinsoy, VP of Predictive Applications at BigML.
*ML in Retail 2021: Webinar.
ML in GRC: Machine Learning in Legal Automation, How to Trust a LawyerbotBigML, Inc
This presentation analyzes the role that Machine Learning plays in legal automation with a real-world Machine Learning application.
Speaker: Arnoud Engelfriet, Co-Founder at Lynn Legal.
*ML in GRC 2021: Virtual Conference.
ML in GRC: Supporting Human Decision Making for Regulatory Adherence with Mac...BigML, Inc
This is a real-life Machine Learning use case about integrated risk.
Speakers: Thomas Rengersen, Product Owner of the Governance Risk and Compliance Tool for Rabobank, and Thomas Alderse Baas, Co-Founder and Director of The Bowmen Group.
*ML in GRC 2021: Virtual Conference.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
3. 3
Disclaimer:
The term AI (Artificial Intelligence) appears several times throughout these slides in several references and 3rd party content
In the context of this presentation it refers specifically to the ability to build machine learning driven applications which
ultimately automate and/or optimize business processes and DOES NOT refer to true or strong Artificial Intelligence in the
formal sense, which is not likely to happen for decades to come (emphasis from author)
1. ML platforms - Uber - Pooyan Jamshidi USC: https://pooyanjamshidi.github.io/mls/lectures/mls03.pdf
2. ML Systems - Jeff Smith (book)
3. Real World End to End ML: Srivatsan Srinivasan: https://www.slideshare.net/srivatsan88/real-world-end-to-end-machine-learning-pipeline-157130773
4. MLPaaS: https://thenewstack.io/an-introduction-to-the-machine-learning-platform-as-a-service/
5. NIPS: Hidden technical debt in ML: https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf
6. Twinml guide to AI platforms - Sam Charrington : https://twimlai.com/mlplatforms-ebook/
7. Carlos A. Gomez-Uribe and Neil Hunt, “The Netflix Recommender System: Algorithms, Business, Value, an Innovation,” ACM Transactions on Management Information Systems, January 2016, https://dl.acm.org/citation.cfm?id=2843948.
8. Robert Chang, “Using Machine Learning to Predict Value of Home on Airbnb,” Medium, July 17, 2017, https://medium.com/airbnb-engineering/using-machine-learning-to-predict-value-of- homes-on-airbnb-9272d3d4739d.
9. Andrew Hoh and Nikhil Simha, “Zipline: Airbnb’s Machine Learning Data Management Platform,” SAIS 2018, June 12, 2018, https://databricks.com/session/zipline-airbnbs-machine-learning- data-management-platform.
10. Jeffrey Dunn, “Introducing FBLearner Flow: Facebook’s AI Backbone,” Facebook Engineering, May 9, 2016, https://engineering.fb.com/core-data/introducing-fblearner-flow-facebook-s-ai- backbone.
11. Kim Hazelwood, et al, “Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective,” Facebook, Inc., February 24, 2018, https://research.fb.com/wp-content/ uploads/2017/12/hpca-2018-facebook.pdf.
12. Jermey Hermann and Mike Del Balso, “Meet Michelangelo: Uber’s Machine Learning Platform,” Uber Engineering, September 5, 2017, https://eng.uber.com/michelangelo/.
13. Monica Rogati, “The AI Hierarchy of Needs,” Hackernoon, June 12, 2017, https://hackernoon. com/the-ai-hierarchy-of-needs-18f111fcc007.
14. BigML Documentation https://bigml.com/documentation/
15. Domain Specific Language for ML Workflows Automation - WhizzML - BigML https://bigml.com/whatsnew/whizzml#whizzml-automating-machine-learning
16. Domain Specific Language for Feature Engineering - Flatline https://github.com/bigmlcom/flatline
17. AutoML - OptiML https://bigml.com/api/optimls
References (Partial List):
4. #MLSEV
[1] Definitions & Context
•Machine Learning Platforms, Definitions
•ML models & apps as first class assets in the Enterprise
•Workflow of an ML application
•ML Algorithms, overview
•Architecture of a ML platform
•Update on the Hype cycle for ML & predictive apps
[2] Adopting ML at Scale
•The Problem with Machine Learning - Scaling ML in the
Enterprise
•Technical Debt in ML systems
•How many models are too many models
•The need for ML platforms
[3] The Market for ML Platforms
•ML platform Market References - from early adopters to
mainstream
•Custom Build vs Buy: ROI & Technical Debt
•ML Platforms - Vendor Landscape
4
Summary
[7] Future Evolution for ML Platforms
Appendix I: Practical Recommendations for ML onboarding in the
Enterprise
Appendix II: List of References & Additional Resources
[4] Custom Built ML Platforms
• ML platform Market References - a closer look
Facebook - FBlearner
Uber - Michelangelo
AirBnB - BigHead
• ML Platformization Going Mainstream: The Great Enterprise Pivot
[5] From DevOps to MLOps
• DevOps <> ModelOps
• The ML platform driven Organization
• Leadership & Accountability (labour division)
[6] Automated ML - AutoML
• Scaling ML - Rapid Prototyping & AutoML:
• Definition, Rationale
• Vendor Comparison
• AutoML - OptiML: Use Cases
5. #MLSEV
•
Machine Learning Platforms, Definitions
•
ML use cases & apps as first class assets in the Enterprise
•
Workflow of an ML application
•
ML Algorithms, overview
•
Architecture of an ML platform
•
Update on the Hype cycle for ML & predictive apps
5
Definitions & Context
Section 1
6. 6
The ML platform offers advanced functionality essential for building ML solutions (primarily predictive and prescriptive models).
The platform supports the incorporation of these solutions into business processes, surrounding infrastructure, products and applications.
It supports variously skilled data scientists (and other stakeholders i.e ML engineers, Data Analysts & Business Analysts and experts) in
multiple tasks across the data and analytics pipeline, including all of the following areas:
• Data ingestion
• Data preparation & Transformation
• Data exploration & Visualization
• Feature engineering
• Model Selection, Evaluation & testing (AutoML)
• Deployment
• Monitoring
• Maintenance
• Collaboration
Machine Learning Platforms
A formal definition
The workflow of a machine learning project. Defining a problem, prototyping a solution, productionizing the solution and measuring the impact of
the solution is the core workflow. The loops throughout the workflow represent the many iterations of feedback gathering needed to perfect the
solution and complete the project.
Adapted from Gartner DSML Data Science and Machine Learning Platforms report, February 2020 - ID G00385005
7. Internal &
External
AI assets:
ML modeling,
heuristics
AI assets:
ML platform
AI assets:
People
skills/expertise
ML Adoption
cross-function
Enterprise Roadmap for AI & ML
ML models as first-class enterprise asset
8. SUPERVISED UNSUPERVISED
DATA Requires “labelled” data Does not require “labelled” data
GOAL
Goal is to predict the label often called the objective
(churn, sales predictions, etc).
Goal is “structure discovery”, with
algorithms focused on type of relation
(clustering, etc.)
EVALUATION Predictions can be compared to real labels
Each algorithm has it’s own quality
measures
ALGORITHMS
ML Algorithms
8
CLUSTER ANOMALY
TOPIC
MODEL
ASSOCIATIONTREE
MODEL
ENSEMBLE DEEPNETLOGISTIC
REGRESSION
TIME SERIES
CLASSIFICATION / REGRESSION
OPTIML
9. 9
Deep Learning:
Specific Use
Cases
ANN
CNN & RNN
Bayesian NN
(traditional)
Machine Learning:
Workhorse
algorithms
Linear & Logistic
Regression
Decision Trees &
Random Forest
Ensembles
source: Kaggle · The State of Data Science
& ML 2019 ·
https://www.kaggle.com/kaggle-survey-2019
Machine Learning Adoption
ML Algorithms in practice
10. BigML, Inc
Where are my models?
10
Architecture of a ML Platform - MLaaS - BigML
• Models are stored in the BigML server, in the cloud.
• Private and On premises clouds are also available.
• API first: every execution (model, dataset,
evaluation, automation script) is an immutable
resource that can be managed programmatically.
• Resources are encoded in JSON. are easy to
integrate and export to other applications and
workflows
API-first, auto-scalable, auto-deployable
distributed architecture for Machine Learning
12. Emerging Technology hype cycle: Machine Learning
The Great Enterprise Pivot
We are here
~2 years to
mainstream
13. Adoption Cycle: Machine Learning
Custom Built vs Buy, crossing the chasm
source: adapted from BigML Inc materials · http://bigml.com
We are here
• Open
Source
• Custom Built
vs Buy
• Fragmented
• Proprietary
• Buy vs Build
• Consolidated
14. #MLSEV
The Problem with Machine Learning - Adopting ML at Scale in the
Enterprise
Technical Debt in ML systems
How many models are too many models
The need for ML platforms
14
Adopting ML at Scale
Section 2
15. “
The problem with Machine Learning
Adopting Machine Learning at Scale in the Enterprise
It is time to bring the AI exploration
era to the next stage of production -
enabling sustainable, industrial-
grade AI systems within the IT and
cultural fabric.
Gartner
“Artificial Intelligence Primer for 2020” Erick Brethenoux, 24 January 2020
16. 16
The problem with Machine Learning
source: Kaggle · The State of Data Science &
ML 2019 ·
https://www.kaggle.com/kaggle-survey-2019
From prototyping to production
17. 17
D. Sculley et al., Google, NIPS 2015
Technical Debt in Machine Learning
Model Drifting - Data Lifecycle
18. 18
NIPS: Hidden technical debt in ML
https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf
Dealing with Complexity
Infrastructure & Fragmentation
19. 19
How many ML models are too many models
Facebook ML platform (a.k.a FBlearner):
+1Mn ML models trained
+6 Mn predictions/sec
25% of engineering team using it
Source: ModelOps IBM research Waldemar Hummer et al http://hummer.io/docs/2019-ic2e-modelops.pdf
20. 20
Source: David Talby CTO, Pacific AI - Strata Conference
https://conferences.oreilly.com/strata/strata-ny-2018/public/schedule/detail/68616
Increasing number of models & complexity
ML Use Cases
21. 21
Increasing number of models & complexity
Uber
Facebook
Twitter
Linkedin
SO PUT THE RIGHT ML PLATFORM IN PLACE
THESE COMPANIES DID ALREADY (Custom Built)
•e-commerce
•online/real time
transaccions
•consumer C2C services
•Predictions driven by
volume (millions) & models
•long term trends &
patterns
•B2B & Government
services
•consumer C2C services
•Predictions driven by
quality &
•rules based knowledge
AirBnB
Lyft
Netflix
Spotify
GE
AT&T
eBay
Amazon
22. #MLSEV
ML platform Market References - from early adopters to mainstream
Custom Build vs Buy: ROI & Technical Debt
ML Platforms - Vendor Landscape
22
The Market for ML Platforms
Section 3
23. Amazon
Jeff Bezos’ letter to Amazon shareholders - May, 2017
“Machine learning and AI is a horizontal
enabling layer. It will empower and improve
every business, every government
organization, every philanthropy — basically
there’s no institution in the world that cannot
be improved with machine learning” .
Jeff Bezos
24. Machine Learning Platforms
An Infrastructure & Service layer to drive ML at scale in the enterprise
Facebook FBlearner May 9, 2016
https://code.fb.com/core-data/
introducing-fblearner-flow-facebook-s-
ai-backbone/
Google TFX Tensorflow Aug 13, 2017
https://www.tensorflow.org/tfx/
https://dl.acm.org/ft_gateway.cfm?
id=3098021&ftid=1899117&dwn=1&CF
ID=81485403&CFTOKEN=79729647b
2ac491f-EAC34BCC-93F2-A3C5-
BE9311C722468452
Netflix
Notebook Data
Platform
Aug 16, 2018 https://medium.com/netflix-techblog/
notebook-innovation-591ee3221233
Uber Michelangelo Sept 5, 2017 https://eng.uber.com/michelangelo/
Twitter Cortex Sept, 2015
https://cortex.twitter.com/en.html
https://blog.twitter.com/engineering/
en_us/topics/insights/2018/ml-
workflows.html
Magic Pony acquisition - 2016:
https://www.bernardmarr.com/
default.asp?contentID=1373
AirBnB BigHead Feb, 2018
https://databricks.com/session/
bighead-airbnbs-end-to-end-machine-
learning-platform
LinkedIN Pro-ML Oct, 2018
https://engineering.linkedin.com/blog/
2018/10/an-introduction-to-ai-at-
linkedin
26. Machine Learning Platforms
eBay Krylov Dec 17, 2019
https://tech.ebayinc.com/engineering/
ebays-transformation-to-a-modern-ai-
platform/
Lyft Flyte Jan 20, 2020
https://eng.lyft.com/introducing-flyte-
cloud-native-machine-learning-and-
data-processing-platform-
fb2bb3046a59
AT&T Acumos Oct 30, 2017 https://medium.com/netflix-techblog/
notebook-innovation-591ee3221233
Spotify
Spotify ML
platform
Dec 13, 2019
https://labs.spotify.com/2019/12/13/the-
winding-road-to-better-machine-
learning-infrastructure-through-
tensorflow-extended-and-kubeflow/
Delta Airlines (licensed) Jan 8, 2020
https://www.aviationtoday.com/
2020/01/08/delta-develops-ai-tool-
address-weather-disruption-improve-
flight-operations/
GE
Predix (customer
IoT platform)
Feb, 2018
https://www.ge.com/digital/sites/
default/files/download_assets/Predix-
The-Industrial-Internet-Platform-
Brief.pdf
KT Telecom Neuroflow Jan, 2018 https://disruptive.asia/kt-ai-platform-
internal-use/
An Infrastructure & Service layer to drive ML at scale in the enterprise
27. Machine Learning Platforms
Build vs Buy
The “custom build” approach, while highly customized to the needs of the organization, is
expensive, requires time and strong engineering talent and teams to develop and maintain it
The “buy” option often requires adapting to a given vendor’s approach but demands less time and
expertise and provides continued access to innovations
Ultimately, it’s a business case decision (ROI calculator next slide)
Partial list of ML platform licensees (courtesy of BigML Inc)
Most enterprises will ultimately implement
their ML platforms from commercial or
cloud-delivered software,
along with custom integration and custom-
coded modules tailored to their specific
needs
28. 28
ML Platform
Build vs Buy ROI
Source: Dataiku DS ROI toolkit https://pages.dataiku.com/data-science-roi-toolkit
29. 29
MACHINE LEARNING AS A SERVICE MACHINE LEARNING PLATFORM & SOFTWARE
https://www.crisp-research.com/vendor-universe/machine-learning/#fndtn-mlaas
Machine Learning Platforms
Vendor Landscape MLaaS: Machine Learning as a Service & On Premise
30. 30
ML Platformization Going Mainstream
Buy vs Build
Partial list of ML platform public customer references: HG Insights (BigML Inc, Dataiku & H2O.ai https://discovery.hgdata.com/product/bigml)
31. #MLSEV
ML platform Market References - a closer look
•Facebook - FBlearner
•Uber - Michelangelo
•AirBnB - BigHead
ML Platformization Going Mainstream: The Great Enterprise Pivot
31
Custom Built ML Platforms
Section 4
32. Facebook
FBlearner Flow: Facebook’s ML platform for internal use - May, 2016
25% of engineering team
using it
+1Mn ML models trained
+6 Mn predictions/sec
ML at scale:
Reusability
Parallelization
Simplicity
Automation
Rapid prototyping & experimentation
33. Facebook
FBlearner Flow: Facebook’s ML platform for internal use - May, 2016
Eliminating manual work for
experimentation
Engineers can spend more time
on feature engineering
which in turn produce greater
accuracy improvements
“
34. Uber
Michelangelo: Uber’s MLaaS platform for internal use - Sept, 2017
end-to-end ML workflow:
• manage data
• train
• evaluate
• deploy models
• make and monitor predictions.
Supports traditional ML models,
time series forecasting, and deep
learning.
36. AirBnB
Bighead - Feb, 2018
Airbnb’s internal ML platform is called Bighead.
Bighead is an end-to-end platform for building and deploying ML
models that aims to make the machine learning process at Airbnb
seamless, versatile, consistent, and scalable.
It is built in Python and relies on open source technology like
Docker, Jupyter, Spark, Kubernetes, and more.
These open source components are customized and integrated for
Airbnb’s specific needs. Like much of Airbnb’s technology
infrastructure, Bighead runs in AWS.
The platform was supported by an ML infrastructure team of 11
engineers and one product manager.
In the fall of 2018, Airbnb announced its plans to open source parts
of Bighead and Zipline in early 2019, but this hasn’t yet materialized.
37. The Great Pivot - ML at scale
Systems of Intelligence/ML drive efficiencies (1st), competitive advantages (2nd) & next
defensible business models ultimately
• Most large technology companies are
reconfiguring themselves around ML.
• Google was (arguably) the first company to
move, followed by Microsoft, Facebook,
Amazon, Apple and IBM.
• 2nd tier corporations following suit: GE, Uber,
even carriers as AT&T
• Not only a US phenomena - Alibaba, Baidu
chief Robin Li said in an internal memo that
Baidu’s strategic future relies on AI
• Ultimately all global players will need to re-tool
their processes adopting a ML driven
approach.h/t Jerry Chen - Greylock Partners
https://news.greylock.com/the-new-moats-53f61aeac2d9
38. #MLSEV
Scaling ML - Rapid Prototyping & AutoML:
Definition, Rationale
Vendor Comparison
AutoML - OptiML: Use Cases
38
Automated ML - AutoML
Section 6
40. AutoML
Automated Machine Learning
40
Problem Formulation
Data Acquisition
Feature Engineering
Modeling and Evaluations
Predictions
Measure Results
Data Transformations
5%
80%
• Data tasks, most consuming - Semi
automated.
• Feature Engineering is key to model
performance - semi automated
10% • Goal definition - Human driven
5%
• AutoML enables fast modeling/prototyping -
Automated
• Automated
41. 41
Enable knowledge workers (e.g., analysts, developers) to build stable and
insightful models quickly
Scale the number of predictive use cases in collaboration with non-technical
peers through quick prototyping.
Best AutoML approaches rely on automation of parts of the Machine Learning
process (e.g., hyper-parameter tuning) without limiting the practitioners’ ability
control customization.
GDPR, data privacy, interpretability and prediction explanations became
critical concerns when deploying AutoML
AutoML
Automated Machine Learning
43. 43
AutoML
Trade off in Model/Algorithm Selection
• Simple (Logistic Reg) vs
Complex (Deepnets, ANNs)
• Weak and Fast vs. Slow and
Robust
• Interpretability vs.
Representability
• Confidence vs. Performance
• Biased vs. Data-hungry
44. 44
AutoML DATAROBOT H2O BigML
Data Preparation
• Encoded categorical variables (one-hot);
Text n- grams; Missing values imputing;
Discretization (bins)
• limited manual transformations • Max. of
10 classes in the objective*
•Encoded categorical variables (one-hot); Missing
values handling; Date-time fields expansion; Bulk
interactions transformers; SVD numeric
transformer; CV target encoding; Cluster distance
transformer; Time lag
•Automatic feature engineering possible when
using AutoDL
• Encoded categorical variables (one-hot); Text
analysis; Missing values handling; Date-time fields
expansion
• Automatic Recursive Feature Selection & Feature
Engineering
• Multiple flexible manual transformations • Max of
1,000 classes in the objective
Optimization
Undisclosed optimization technique
(“expert data scientists preset
hyperparameter search space for models*)
Random Stacking
(a combination of random grid search and stacked
ensembles, plus early stopping)
Bayesian Parameter Optimization
(SMAC — Sequential Model-based Algorithm
Configuration) & DNN Metalearning
Models
•Open-source libraries: scikit-learn, R, H2O,
Tensorflow (not CNN or RNN), Spark,
XGBoost, DMTK, and Vowpal Wabbit
•They also “blend” multiple models during
the optimization process.
•GBMs, Random Forests, XGBoost, deep neural
nets, and extreme random forests
•· Stacks of models can be learned. Best of family
stacks adopt the top model type from each of the
main algorithms.
•Decision trees, random decision forests, boosting,
logistic regression, deep neural networks
•Customizable model ensembles with Fusions
leveraging the individually optimized models for
different classification, regression algorithms.
Speed It tests 30-40 different modeling
approaches and takes ~20 min.
Default time limit for AutoML is 1 hour. Can use
GPU or CPU. Can specify settings for accuracy,
time, and interpretability.
It tests 128 different modeling approaches
(creating more than 500 resources) and takes ~30
min.
Model
Visualizations &
Interpretability
• Limited model visualizations
• Feature importance for models • Predictions
explainability
• Dashboard: A single page with a global
interpretable model explanations plot, a feature
importance plot, a decision tree plot, and a partial
dependence plot.
• A machine learning interpretation tool (MLI) that
includes a KLIME or LIME-SUP graph.
• Multiple model visualizations to analyze the
impact of the variables on predictions:
sunburst, decision tree, partial dependence
plots, line chart (LR)
• Feature importance for models
• Predictions explainability
Model Evaluations
• Confusion matrix
• ROC curve (only for binary classification)
• Lift curve (only for binary classification)
• Side-by-side evaluations comparison
• Trade-off between complexity vs.
performance
• Models are ranked by cross-validation
AUC by default.
• Return leaderboard sortable by deviance (mean
residual deviance), logloss, MSE, RMSE, MAE,
RMSLE, mean per class error
• Confusion matrix
• ROC curve
• Precision-Recall curve
• Gain curve
• Lift curve
• Multiple evaluations comparison chart
Programmability &
Deployability
• Models can be used and created via API •
Export models
• Cloud, VPC or on-premises
• H2O allows you to convert the models you have
built to either a Plain Old Java Object (POJO) or a
Model ObJect, Optimized (MOJO).
• H2O-generated MOJO and POJO models are
ieasily embeddable in Java environments
• Models can be used and created via API • Export
models
• Cloud, VPC or on-premises
Source: Public Resources, Vendor Docs, BigML Analysis
Metalearning!
45. 45
AutoML - Metalearning
Automatic Network Hyperparameters Selection - DNNs (DeepNets)
We trained 296,748 deep neural networks
so you don’t have to!
• 296,748+ deep neural networks trained on 50 datasets
• For each one, recorded the optimum network structure for the
given dataset structure (number of fields, types of fields, etc)
• Trained a model to predict the optimum network structure for any
given dataset.
• This predicted network structure & hyper parameters can be
used directly or as a seed for a more intensive network search
Source: BigML - DeepNets https://blog.bigml.com/2017/10/04/deepnets-behind-the-scenes/
47. We are
here
(mostly)
Simplified* AI Technologies Landscape
* and imperfect
Future:
• Knowledge
representation
(symbolic/
Subsymbolic)
• Planning
(Reinforcement
Learning, Agents)
• Reasoning (Logic,
Symbolic)
• Search &
Optimization
(evolutionary/
genetic algos)
48. 48
BigML, IncPrivate and Confidential
BigML Product Progression
5
AutoML, Linear
Regression, Node-
Red, Workflow
Report, Improved
Topic Modeling
Organizations,
Operating
Thresholds, OptiML,
Fusions, Data
Transformations, PCA
Boosted Trees,
ROC Analysis,
Time Series,
DeepNets
Scripts, Libraries,
Executions,
WhizzML, Logistic
Regression, Topic
Models
Association
Discovery,
Correlations,
Samples,
Statistical Tests
Anomaly Detection,
Clusters, Flatline
Evaluations, Batch
Predictions,
Ensembles,
Starbursts
Core ML Workflow:
Source, Dataset,
Model, Prediction
Prototyping and
Beta
201920182017201620152014201320122011
Automating Model Creation, Selection, Operation and Workflows = Making Machine Learning Easier
Reproducibility at the core:
Programmability, Interpretability, Explainability are
essential part of BigML's platform
Sophistication
EaseofUse
WE HAVE BEEN BUILDING A STRONG FOUNDATION TO DEVELOP, DEPLOY AND OPERATE MACHINE-LEARNING BASED APPLICATIONS OF UNPARALLELED QUALITY
49. 49
BigML, IncPrivate and Confidential7
AI/MLMarketMaturity
Automating Workflows for
Model Creation,
Selection, Operation
Extending the Platform to Build and Manage Smarter Predictive Applications End-to-End
Building the BEST End-
to-End Machine
Learning Platform
2020 20301980
BigML's Co-Founder
Participates in first University
Machine Learning
2011
BigML
Founded
BigML Future
EXTENDING THE PLATFORM TO BUILD AND MANAGE SMARTER PREDICTIVE APPLICATIONS END-TO-END
Reasoning
Knowledge
Representation
Planning Optimization
Principles
Machine Learning
ROBUST AI
Doing to Reasoning, Planning, Knowledge Representation
and Optimization what we have done to Machine Learning
and combining them to build Robust AI Applications
Machine Learning
50.
51. 51
1. ML platforms - Uber - Pooyan Jamshidi USC: https://pooyanjamshidi.github.io/mls/lectures/mls03.pdf
2. ML Systems - Jeff Smith (book)
3. Real World End to End ML: Srivatsan Srinivasan: https://www.slideshare.net/srivatsan88/real-world-end-to-end-machine-learning-pipeline-157130773
4. MLPaaS: https://thenewstack.io/an-introduction-to-the-machine-learning-platform-as-a-service/
5. NIPS: Hidden technical debt in ML: https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf
6. Twinml guide to AI platforms - Sam Charrington : https://twimlai.com/mlplatforms-ebook/
7. Carlos A. Gomez-Uribe and Neil Hunt, “The Netflix Recommender System: Algorithms, Business, Value, an Innovation,” ACM Transactions on Management Information Systems, January 2016, https://dl.acm.org/
citation.cfm?id=2843948.
8. Robert Chang, “Using Machine Learning to Predict Value of Home on Airbnb,” Medium, July 17, 2017, https://medium.com/airbnb-engineering/using-machine-learning-to-predict-value-of- homes-on-
airbnb-9272d3d4739d.
9. Andrew Hoh and Nikhil Simha, “Zipline: Airbnb’s Machine Learning Data Management Platform,” SAIS 2018, June 12, 2018, https://databricks.com/session/zipline-airbnbs-machine-learning- data-management-
platform.
10.Jeffrey Dunn, “Introducing FBLearner Flow: Facebook’s AI Backbone,” Facebook Engineering, May 9, 2016, https://engineering.fb.com/core-data/introducing-fblearner-flow-facebook-s-ai- backbone.
11.Kim Hazelwood, et al, “Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective,” Facebook, Inc., February 24, 2018, https://research.fb.com/wp-content/ uploads/2017/12/hpca-2018-
facebook.pdf.
12.Jermey Hermann and Mike Del Balso, “Meet Michelangelo: Uber’s Machine Learning Platform,” Uber Engineering, September 5, 2017, https://eng.uber.com/michelangelo/.
13.Kubeflow, “Kubeflow: Machine Learning Toolkit for Kubernetes,” Github, https://github.com/ kubeflow/.
14.James Kanter and Kalyan Veeramachaneni, “Deep Feature Synthesis: Towards Automating Data Science Endeavors,” 2015, http://www.jmaxkanter.com/static/papers/DSAA_DSM_2015.pdf.
15.Feature Labs, “Featuretools: An Open Source Python Framework for Automated Feature Engineering,” Github, https://github.com/featuretools/featuretools.
16.Frank Hutter, et al, “SMAC,” AutoML Freiburg-Hannover, https://www.automl.org/automated- algorithm-design/algorithm-configuration/smac/
17.Ruben Martinez-Cantin, “BayesOpt: A Toolbox for Bayesian Optimization, Experimental Design and Stochastic Bandits,” Github https://github.com/rmcantin/bayesopt.
18.Hyperopt, “Hyperopt: Distributed Asynchronous Hyperparameter Optimization in Python” Github, Septermber 4, 2011, https://github.com/hyperopt/hyperopt.
19.UCBerkeley RISELab, “Tune: Scalable Hyperparameter Search,” Github https://github.com/ ray-project/ray/tree/master/python/ray/tune.
20.Cade Metz, “Building A.I. That Can Build A.I.,” The New York Times, November 5, 2017, https:// www.nytimes.com/2017/11/05/technology/machine-learning-artificial-intelligence-ai.html
21.Google, “Cloud AutoML,” Google Cloud, https://cloud.google.com/automl/.
22.Neoklis Polyzotis, et al, “Data Management Challenges in Production Machine Learning,” Google, 2017, https://static.googleusercontent.com/media/research.google.com/en//pubs/ archive/46178.pdf.
23.Julien Simon, “Mastering the Mystical Art of Model Deployment,” Medium, July 28, 2018, https:// medium.com/faun/mastering-the-mystical-art-of-model-deployment-c0cafe011175.
24.Monica Rogati, “The AI Hierarchy of Needs,” Hackernoon, June 12, 2017, https://hackernoon. com/the-ai-hierarchy-of-needs-18f111fcc007.
List
of
References