This document summarizes a presentation about building AI products. It discusses the typical AI product lifecycle including defining the problem, data preparation, model development, evaluation and deployment. It provides examples of different types of AI systems and how the role of AI evolves from assisting humans to being more autonomous. The presentation emphasizes understanding users, data and metrics to build ethical and unbiased AI. It also discusses ongoing learning after deployment and providing case studies of AI products from companies like Microsoft, Uber and Tesla.
20NTC - Getting Started with Data Science and AI in the Non-Profit Sector - v...Ria Sankar
Ria Sankar is the Director of Program Management for Microsoft's AI for Good programs. This webinar covers her talk on "Getting Started with Data Science and AI in the Non-Profit Sector" that was supported to be offered during 2020 NTEN Conference in Baltimore (20NTC) and recorded via Keela's Plugged In platform due to COVID-19.
An introduction to the heart, mind, and soul of Product Management: Customer Obsession, Metrics, and Product Sense. Presented at Product School Bellevue.
DataTalkClub Conference, Feb 12 2021
Creating a machine learning model is not an easy task.
Creating a useful machine learning model that gets into production and generates actual business value - is an even harder one.
There are many ways for an ML project or product to fail even when the data is there and the model technically performs well. From the wrong problem statement to lack of trust from stakeholders, in this talk I will discuss what issues to look out for, and how to avoid them.
How to Use Data to Build Better Products by HelloSociety PMProduct School
Data is a really powerful way to build better products for users. The ability to set up and vet a hypothesis about product is a huge asset for Product Managers and it's not just for the strong Data Scientists among us. Data and analytics can be a huge asset in figuring out what users want and in keeping development aligned with business goals.
Rose talked about what that means and how you can develop your sense around actionable data.
AI Models For Fun and Profit by Walmart Director of Artificial IntelligenceProduct School
Product Management Event at #ProductCon NY on how to create AI models for fun and for profit by Jason Nichols, Director of Artificial Intelligence at Walmart Intelligent Research Lab.
How to Build an AI/ML Product and Sell it by SalesChoice CPOProduct School
Main takeaways:
- How to identify the use cases to build an AI/ML product?
- What are the challenges that you would face and how to over come them?
- How to establish stake holder buy-in and design the go-to market strategy?
How to Use Data to Build Better Products by fmr NY Times PMProduct School
Main takeaways:
- Why it matters what you measure- How data can tell you what users want, and what they don't want
- How to get familiar enough with your own data to be able to get what you want
- GA, SQL, etc.
- Why your goal should be to find the point in the data- What "actionable data" can look like
20NTC - Getting Started with Data Science and AI in the Non-Profit Sector - v...Ria Sankar
Ria Sankar is the Director of Program Management for Microsoft's AI for Good programs. This webinar covers her talk on "Getting Started with Data Science and AI in the Non-Profit Sector" that was supported to be offered during 2020 NTEN Conference in Baltimore (20NTC) and recorded via Keela's Plugged In platform due to COVID-19.
An introduction to the heart, mind, and soul of Product Management: Customer Obsession, Metrics, and Product Sense. Presented at Product School Bellevue.
DataTalkClub Conference, Feb 12 2021
Creating a machine learning model is not an easy task.
Creating a useful machine learning model that gets into production and generates actual business value - is an even harder one.
There are many ways for an ML project or product to fail even when the data is there and the model technically performs well. From the wrong problem statement to lack of trust from stakeholders, in this talk I will discuss what issues to look out for, and how to avoid them.
How to Use Data to Build Better Products by HelloSociety PMProduct School
Data is a really powerful way to build better products for users. The ability to set up and vet a hypothesis about product is a huge asset for Product Managers and it's not just for the strong Data Scientists among us. Data and analytics can be a huge asset in figuring out what users want and in keeping development aligned with business goals.
Rose talked about what that means and how you can develop your sense around actionable data.
AI Models For Fun and Profit by Walmart Director of Artificial IntelligenceProduct School
Product Management Event at #ProductCon NY on how to create AI models for fun and for profit by Jason Nichols, Director of Artificial Intelligence at Walmart Intelligent Research Lab.
How to Build an AI/ML Product and Sell it by SalesChoice CPOProduct School
Main takeaways:
- How to identify the use cases to build an AI/ML product?
- What are the challenges that you would face and how to over come them?
- How to establish stake holder buy-in and design the go-to market strategy?
How to Use Data to Build Better Products by fmr NY Times PMProduct School
Main takeaways:
- Why it matters what you measure- How data can tell you what users want, and what they don't want
- How to get familiar enough with your own data to be able to get what you want
- GA, SQL, etc.
- Why your goal should be to find the point in the data- What "actionable data" can look like
How to Use Artificial Intelligence by Microsoft Product ManagerProduct School
The talk focused on the Fundamentals of Product Management, leveraging the speaker's personal experiences in the AI field. It covered core Product Manager topics such as managing customer needs, business goals & technology feasibility, the holy trinity of the Product Manager discipline, delve into data analyses, rapid experimentation, and execution, and finally, explored the challenges of customer privacy, bias, and inclusivity in AI products.
How to Use AI in Product by Intel Product ManagerProduct School
This presentation covers what it's like to use AI in Product for a company and the different ways they can be implemented within an organization and we'll also touch on some of the misconceptions that come with using AI in Product.
Main takeaways:
- Multidisciplinary Product Manager
- Managing a product with invisible software, vague requirements in AI/IoT,
- Customer vs Industry
- Difference between technology and product; When to productize?
- AI as a feature vs AI as a product
- Product Management for the Internet of Things
Slides Chris Butler recently used in his discussion w/ mentees of The Product Mentor.
Synopsis: In this talk, Vikas will share his thoughts on what is Product Strategy and how Product Managers can develop it, He will also share some concepts in Strategy and how Product Managers can apply them to make their products more successful.
The Product Mentor is a program designed to pair Product Mentors and Mentees from around the World, across all industries, from start-up to enterprise, guided by the fundamental goals…Better Decisions. Better Products. Better Product People.
Throughout the program, each mentor leads a conversation in an area of their expertise that is live streamed and available to both mentee and the broader product community.
http://TheProductMentor.com
Essential Tools for Product Managers and Marketers (Oct 2011)Jesse Gant
A refresh of the tools and resources (research/analysis, social monitoring, A/B testing, wireframing, SEO/SEM, etc.) that every Product Manager and Marketer should use or at least know about.
Solution Design - The Hidden Side of UX (for Product Managers)Joe Baz
User Experience is not just about the user interface, it's about understanding customer needs and creating a solution that addresses their needs. Software product managers have a huge, and often understated role, in the creation of a great user experience for customers. At the heart of User Experience is the ability to creatively solve customer problems, which is a key responsibility of a product manager.
The Ultimate Beginner's Guide For Beta Testing Mobile AppsInstabug
Learn about beta testing and get tips and resources to start beta testing your mobile app. From where to find beta testers to how to motivate them and more, learn how to make the most out of this crucial stage in your app's journey.
This presentation will look at ways for using the Amazon Mechanical Turk system for conducting UX Research, with an emphasis on Specialized Techniques, and how to work around some of Mechanical Turk's inherent limitations. The intended tone is to provide an "Insider's Guide" to using Mechanical Turk ethically and effectively.
The speaker will share his experiences, including both challenges and successes, in working with Amazon's Mechanical Turk, along with gleaned incites.
Amazon Mechanical Turk is an online tool for recruiting and paying human subjects for completing specific work tasks. User Experience Professionals have been using Mechanical Turk for data gathering activities. It has been designed to link to supplemental tools and resources, such as the Qualtrics Survey Management system.
How to Build Trustworthy AI Products by Philosophie Dir. of AIProduct School
You can’t just ‘add AI’ to a project and expect it to work. It isn’t magic dust that can be sprinkled on a product. The key to building systems that are integrated into people’s lives is trust. If you don’t have the right amount of trust, you open the system up to disuse and misuse.
During this talk we went through the building blocks of AI from a UX Design perspective, what trust is, how trust is gained, and maybe more importantly lost, in UX/UI, how to effectively team humans/machines and techniques you can use day-to-day to build trusted AI products.
Analytics in Action: What Users Want: How and Why to Build Knowledge into You...Aggregage
Usage data allows PMs, the product team, and the whole organization to make better decisions. Good usage intelligence gives you the ability to be smarter, more active, more decisive, nimbler, and to minimize risk. But what if you don't have that data - such as before you have users? Or, what if the right decision seems to fly in the face of the data you have? Or, what if your product offers more than just the standard features?
To get deeper into these questions, Nils Davis asks, "What is the most interesting thing about Instagram?" (Because who doesn't like a product that Facebook paid $1 billion for when it had fewer than 50 employees and no revenue?) Nils will use the example of Instagram’s Filters to talk about how putting prebuilt knowledge in your product can change the way your product is used for the better - putting you in the company of most market-leading products. Finally, he’ll tie it all together by explaining how the way you interpret and use usage data can impact the way your tell your product’s story, and ultimately, how your users use your product.
[CXL Live 16] A/B Testing Pitfalls: Getting Numbers is Easy; Getting Numbers ...CXL
At Microsoft, we run over a thousand online controlled experiments (A/B tests being the simplest) every month. A key goal for the Experimentation Platform is to provide scorecards you can trust. We will share real examples of surprising results and pitfalls, so you can generate trustworthy analyses.
Analytics in Action: What Users Want: How and Why to Build Knowledge into You...Hannah Flynn
Usage data allows PMs, the product team, and the whole organization to make better decisions. Good usage intelligence gives you the ability to be smarter, more active, more decisive, nimbler, and to minimize risk. But what if you don't have that data - such as before you have users? Or, what if the right decision seems to fly in the face of the data you have? Or, what if your product offers more than just the standard features?
To get deeper into these questions, Nils Davis asks, "What is the most interesting thing about Instagram?" (Because who doesn't like a product that Facebook paid $1 billion for when it had fewer than 50 employees and no revenue?) Nils will use the example of Instagram’s Filters to talk about how putting prebuilt knowledge in your product can change the way your product is used for the better - putting you in the company of most market-leading products. Finally, he’ll tie it all together by explaining how the way you interpret and use usage data can impact the way your tell your product’s story, and ultimately, how your users use your product.
Breaking In – Finding Your First Role in Product ManagementPaul Parent
First shared with the ProductTank Phoenix community on November 29, 2018.
Short presentation with three goals:
(1) define the role and some of the unique characteristics,
(2) walk through a framework for thinking about how to work toward this role if it appeals to you, and
(3) share a collection of resources which I have found to be excellent and should help you get off to a strong start
Intro to Cryptocurrency & Blockchain by SelfKey Product ManagerProduct School
The crypto industry is currently at an inflection point, expanding from the early adopter crowd to a more mainstream audience. With the recent booms in Bitcoin, Ethereum, and ICOs, many early stages teams are finding themselves capitalized in a way never seen before and need to grow by hiring the right people. Terry Lin from SelfKey gave a talk about what it’s like working in this new and exciting space.
Eureka Analytics Seminar Series - Product Management for Data Science ProductsEureka Analytics Pte Ltd
Data Science is increasingly being used to build new products in every industry, from Internet companies to physical businesses, and from large enterprise systems to consumer products that we carry in our pockets. The ability to understand the Data Science process is an increasingly important skill for Software Product Managers. What are some of the unique challenges when building a Data Science product? How do we build products that scale if there is an element of experimentation and research? In this seminar, you will learn what it takes to manage a Data Science product, and hear practical tips and examples from our experience at Eureka Analytics. This seminar is brought to you by Eureka Analytics
Sometimes when you are starting on an idea for a project you dont know where or how to start. This is a tried and tested strategy that gets you going. From inspiration to organization, tools to knowledge, all you need to know to build the next great app.
Reviewing progress in the machine learning certification journey
𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗔𝗱𝗱𝗶𝘁𝗶𝗼𝗻 - Short tech talk on How to Network by Qingyue(Annie) Wang
C𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 AI and ML on Google Cloud by Margaret Maynard-Reid
𝗔 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 𝗠𝗟 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗳𝗿𝗮𝗺𝗶𝗻𝗴, 𝗺𝗼𝗱𝗲𝗹 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗳𝗮𝗶𝗿𝗻𝗲𝘀𝘀 by Sowndarya Venkateswaran.
A discussion on sample questions to aid certification exam preparation.
An interactive Q&A session to clarify doubts and questions.
Previewing next steps and topics, including course completions and material reviews.
How to Use Artificial Intelligence by Microsoft Product ManagerProduct School
The talk focused on the Fundamentals of Product Management, leveraging the speaker's personal experiences in the AI field. It covered core Product Manager topics such as managing customer needs, business goals & technology feasibility, the holy trinity of the Product Manager discipline, delve into data analyses, rapid experimentation, and execution, and finally, explored the challenges of customer privacy, bias, and inclusivity in AI products.
How to Use AI in Product by Intel Product ManagerProduct School
This presentation covers what it's like to use AI in Product for a company and the different ways they can be implemented within an organization and we'll also touch on some of the misconceptions that come with using AI in Product.
Main takeaways:
- Multidisciplinary Product Manager
- Managing a product with invisible software, vague requirements in AI/IoT,
- Customer vs Industry
- Difference between technology and product; When to productize?
- AI as a feature vs AI as a product
- Product Management for the Internet of Things
Slides Chris Butler recently used in his discussion w/ mentees of The Product Mentor.
Synopsis: In this talk, Vikas will share his thoughts on what is Product Strategy and how Product Managers can develop it, He will also share some concepts in Strategy and how Product Managers can apply them to make their products more successful.
The Product Mentor is a program designed to pair Product Mentors and Mentees from around the World, across all industries, from start-up to enterprise, guided by the fundamental goals…Better Decisions. Better Products. Better Product People.
Throughout the program, each mentor leads a conversation in an area of their expertise that is live streamed and available to both mentee and the broader product community.
http://TheProductMentor.com
Essential Tools for Product Managers and Marketers (Oct 2011)Jesse Gant
A refresh of the tools and resources (research/analysis, social monitoring, A/B testing, wireframing, SEO/SEM, etc.) that every Product Manager and Marketer should use or at least know about.
Solution Design - The Hidden Side of UX (for Product Managers)Joe Baz
User Experience is not just about the user interface, it's about understanding customer needs and creating a solution that addresses their needs. Software product managers have a huge, and often understated role, in the creation of a great user experience for customers. At the heart of User Experience is the ability to creatively solve customer problems, which is a key responsibility of a product manager.
The Ultimate Beginner's Guide For Beta Testing Mobile AppsInstabug
Learn about beta testing and get tips and resources to start beta testing your mobile app. From where to find beta testers to how to motivate them and more, learn how to make the most out of this crucial stage in your app's journey.
This presentation will look at ways for using the Amazon Mechanical Turk system for conducting UX Research, with an emphasis on Specialized Techniques, and how to work around some of Mechanical Turk's inherent limitations. The intended tone is to provide an "Insider's Guide" to using Mechanical Turk ethically and effectively.
The speaker will share his experiences, including both challenges and successes, in working with Amazon's Mechanical Turk, along with gleaned incites.
Amazon Mechanical Turk is an online tool for recruiting and paying human subjects for completing specific work tasks. User Experience Professionals have been using Mechanical Turk for data gathering activities. It has been designed to link to supplemental tools and resources, such as the Qualtrics Survey Management system.
How to Build Trustworthy AI Products by Philosophie Dir. of AIProduct School
You can’t just ‘add AI’ to a project and expect it to work. It isn’t magic dust that can be sprinkled on a product. The key to building systems that are integrated into people’s lives is trust. If you don’t have the right amount of trust, you open the system up to disuse and misuse.
During this talk we went through the building blocks of AI from a UX Design perspective, what trust is, how trust is gained, and maybe more importantly lost, in UX/UI, how to effectively team humans/machines and techniques you can use day-to-day to build trusted AI products.
Analytics in Action: What Users Want: How and Why to Build Knowledge into You...Aggregage
Usage data allows PMs, the product team, and the whole organization to make better decisions. Good usage intelligence gives you the ability to be smarter, more active, more decisive, nimbler, and to minimize risk. But what if you don't have that data - such as before you have users? Or, what if the right decision seems to fly in the face of the data you have? Or, what if your product offers more than just the standard features?
To get deeper into these questions, Nils Davis asks, "What is the most interesting thing about Instagram?" (Because who doesn't like a product that Facebook paid $1 billion for when it had fewer than 50 employees and no revenue?) Nils will use the example of Instagram’s Filters to talk about how putting prebuilt knowledge in your product can change the way your product is used for the better - putting you in the company of most market-leading products. Finally, he’ll tie it all together by explaining how the way you interpret and use usage data can impact the way your tell your product’s story, and ultimately, how your users use your product.
[CXL Live 16] A/B Testing Pitfalls: Getting Numbers is Easy; Getting Numbers ...CXL
At Microsoft, we run over a thousand online controlled experiments (A/B tests being the simplest) every month. A key goal for the Experimentation Platform is to provide scorecards you can trust. We will share real examples of surprising results and pitfalls, so you can generate trustworthy analyses.
Analytics in Action: What Users Want: How and Why to Build Knowledge into You...Hannah Flynn
Usage data allows PMs, the product team, and the whole organization to make better decisions. Good usage intelligence gives you the ability to be smarter, more active, more decisive, nimbler, and to minimize risk. But what if you don't have that data - such as before you have users? Or, what if the right decision seems to fly in the face of the data you have? Or, what if your product offers more than just the standard features?
To get deeper into these questions, Nils Davis asks, "What is the most interesting thing about Instagram?" (Because who doesn't like a product that Facebook paid $1 billion for when it had fewer than 50 employees and no revenue?) Nils will use the example of Instagram’s Filters to talk about how putting prebuilt knowledge in your product can change the way your product is used for the better - putting you in the company of most market-leading products. Finally, he’ll tie it all together by explaining how the way you interpret and use usage data can impact the way your tell your product’s story, and ultimately, how your users use your product.
Breaking In – Finding Your First Role in Product ManagementPaul Parent
First shared with the ProductTank Phoenix community on November 29, 2018.
Short presentation with three goals:
(1) define the role and some of the unique characteristics,
(2) walk through a framework for thinking about how to work toward this role if it appeals to you, and
(3) share a collection of resources which I have found to be excellent and should help you get off to a strong start
Intro to Cryptocurrency & Blockchain by SelfKey Product ManagerProduct School
The crypto industry is currently at an inflection point, expanding from the early adopter crowd to a more mainstream audience. With the recent booms in Bitcoin, Ethereum, and ICOs, many early stages teams are finding themselves capitalized in a way never seen before and need to grow by hiring the right people. Terry Lin from SelfKey gave a talk about what it’s like working in this new and exciting space.
Eureka Analytics Seminar Series - Product Management for Data Science ProductsEureka Analytics Pte Ltd
Data Science is increasingly being used to build new products in every industry, from Internet companies to physical businesses, and from large enterprise systems to consumer products that we carry in our pockets. The ability to understand the Data Science process is an increasingly important skill for Software Product Managers. What are some of the unique challenges when building a Data Science product? How do we build products that scale if there is an element of experimentation and research? In this seminar, you will learn what it takes to manage a Data Science product, and hear practical tips and examples from our experience at Eureka Analytics. This seminar is brought to you by Eureka Analytics
Sometimes when you are starting on an idea for a project you dont know where or how to start. This is a tried and tested strategy that gets you going. From inspiration to organization, tools to knowledge, all you need to know to build the next great app.
Reviewing progress in the machine learning certification journey
𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗔𝗱𝗱𝗶𝘁𝗶𝗼𝗻 - Short tech talk on How to Network by Qingyue(Annie) Wang
C𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 AI and ML on Google Cloud by Margaret Maynard-Reid
𝗔 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 𝗠𝗟 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗳𝗿𝗮𝗺𝗶𝗻𝗴, 𝗺𝗼𝗱𝗲𝗹 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗳𝗮𝗶𝗿𝗻𝗲𝘀𝘀 by Sowndarya Venkateswaran.
A discussion on sample questions to aid certification exam preparation.
An interactive Q&A session to clarify doubts and questions.
Previewing next steps and topics, including course completions and material reviews.
The AI Platform Business Revolution: Matchmaking, Empathetic Technology, and ...Steve Omohundro
Popular media is full of stories about self-driving cars, video deepfakes, and robot citizens. But this kind of popular artificial intelligence is having very little business impact. The actual impact of AI on business is in automating business processes and in creating the "AI Platform Business Revolution". Platform companies create value by facilitating exchanges between two or more groups. AI is central to these businesses for matchmaking between producers and consumers, organizing massive data flows, eliminating malicious content, providing empathetic personalization, and generating engagement through gamification. The platform structure creates moats which generate outsized sustainable profits. This is why platform businesses are now dominating the world economy. The top five companies by market cap, half of the unicorn startups, and most of the biggest IPOs and acquisitions are platforms. For example, the platform startup ByteDance is now worth $75 billion based on three simple AI technologies.
In this talk we survey the current state of AI and show how it will generate massive business value in coming years. A recent McKinsey study estimates that AI will likely create over 70 trillion dollars of value by 2030. Every business must carefully choose its AI strategy now in order to thrive over coming decades. We discuss the limitations of today's deep learning based systems and the "Software 2.0" infrastructure which has arisen to support it. We discuss the likely next steps in natural language, machine vision, machine learning, and robotic systems. We argue that the biggest impact will be created by systems which serve to engage, connect, and help individuals. There is an enormous opportunity to use this technology to create both social and business value.
Model governance in the age of data science & AIQuantUniversity
As more and more open-source technologies penetrate enterprises, data scientists have a plethora of choices for building, testing and scaling models. In addition, data scientists have been able to leverage the growing support for cloud-based infrastructure and open data sets to develop machine learning applications. Even though there are multiple solutions and platforms available to build machine learning solutions, challenges remain in adopting machine learning in the enterprise. Many of the challenges are associated with how machine learning process can be formalized. As the field matures, formal mechanism for a replicable, interpretable, auditable process for a complete machine learning pipeline from data ingestion to deployment is warranted. Projects like Docker, Binderhub, MLFlow are efforts in this quest and research and industry efforts on replicable machine learning processes are gaining steam. Heavily regulated industries like financial and healthcare industries are looking for best practices to enable their research teams to reproduce research and adopt best practices in model governance. In this talk, we will discuss the challenges and best practices of governing AI and ML model in the enterprise
A mashup application can provide a view to a wealth of related data from different origins. Mark Jordan demonstrates the creation of such a mashup page in SharePoint that shows related data from an external database, a web service, and other data origins.
test - Future of Ecommerce: How to Improve the Online Shopping Experience Usi...Skyl.ai
About the webinar
It’s no secret that a well-organized product catalog becomes extremely crucial as consumers look for a more rich and consistent online experience while E-shopping. Often, the task of digitizing the catalog of the fast-moving and large volume products becomes daunting due to insufficient, erroneous and fragmented data.
This leads us to the question: If E-commerce and fashion companies need to be agile and consumer-friendly, then why are so many still using the same product catalog management methods that were devised years ago? The manual product classification and data attribution process are only leading to an increased risk of error and time delay affecting the brand reputation. Also, leading to lost sales opportunities due to incomplete or inaccurate product records that don’t really reflect the actual product.
In this webinar, we will discuss how to efficiently manage machine learning projects without tech headaches by plugging in your data and building your models instantly.
What you'll learn
- How E-commerce companies are using AI to drive more sales and seamless customer experience
- Know the secret sauce of automating time-intensive, repetitive steps to quickly build models
- Demo: A deeper understanding of the end-to-end machine learning workflow for a fashion product catalog management using Skyl.ai
Introducción al Machine Learning AutomáticoSri Ambati
¿Cómo puede llevar el aprendizaje automático a las masas? Los proyectos de Machine Learning con la búsqueda de talento, el tiempo para construir e implementar modelos y confiar en los modelos que se construyen.
¿Cómo puede tener varios equipos en su organización para crear modelos de ML precisos sin ser expertos en ciencia de datos o aprendizaje automático?
¿Se pregunta sobre los diferentes sabores de AutoML?
H2O Driverless AI emplea las técnicas de científicos expertos en datos en una aplicación fácil de usar que ayuda a escalar sus esfuerzos de ciencia de datos. La inteligencia artificial Driverless permite a los científicos de datos trabajar en proyectos más rápido utilizando la automatización y la potencia de computación de vanguardia de las GPU para realizar tareas en minutos que solían tomar meses.
Con H2O Driverless AI, todos, incluyendo expertos y científicos de datos junior, científicos de dominio e ingenieros de datos pueden desarrollar modelos confiables de aprendizaje automático. Esta plataforma de aprendizaje automático de última generación ofrece una funcionalidad única y avanzada para la visualización de datos, la ingeniería de características, la interpretabilidad del modelo y la implementación de baja latencia.
H2O Driverless AI hace:
* Visualización automática de datos
* Ingeniería automática de funciones a nivel de Grandmaster
* Selección automática del modelo
* Ajuste y capacitación automáticos del modelo
* Paralelización automática utilizando múltiples CPU o GPU
* Ensamblaje automático del modelo
*automática del Interpretaciónaprendizaje automático (MLI)
* Generación automática de código de puntuación
¿Quieres probarlo tú mismo? Puede obtener una prueba gratuita aquí: H2O Driverless AI trial.
Venga a esta sesión y descubra cómo comenzar con el Aprendizaje automático automático con AI sin conductor H2O, y cree modelos potentes con solo unos pocos clics.
¡Te veo pronto!
Acerca de H2O.ai
H2O.ai es una empresa visionaria de software de código abierto de Silicon Valley que creó y reimaginó lo que es posible. Somos una empresa de fabricantes que trajeron al mercado nuevas plataformas y tecnologías para impulsar el movimiento de inteligencia artificial. Somos los creadores de, H2O, la principal plataforma de aprendizaje de ciencia de datos de fuente abierta y de aprendizaje automático utilizada por casi la mitad de Fortune 500 y en la que confían más de 14,000 organizaciones y cientos de miles de científicos de datos de todo el mundo.
Drifting Away: Testing ML Models in ProductionDatabricks
Deploying machine learning models has become a relatively frictionless process. However, properly deploying a model with a robust testing and monitoring framework is a vastly more complex task. There is no one-size-fits-all solution when it comes to productionizing ML models, oftentimes requiring custom implementations utilising multiple libraries and tools. There are however, a set of core statistical tests and metrics one should have in place to detect phenomena such as data and concept drift to prevent models from becoming unknowingly stale and detrimental to the business.
Combining our experiences from working with Databricks customers, we do a deep dive on how to test your ML models in production using open source tools such as MLflow, SciPy and statsmodels. You will come away from this talk armed with knowledge of the key tenets for testing both model and data validity in production, along with a generalizable demo which uses MLflow to assist with the reproducibility of this process.
Future of Ecommerce: How to Improve the Online Shopping Experience Using Mach...Skyl.ai
About the webinar
It’s no secret that a well-organized product catalog becomes extremely crucial as consumers look for a more rich and consistent online experience while E-shopping. Often, the task of digitizing the catalog of the fast-moving and large volume products becomes daunting due to insufficient, erroneous, and fragmented data.
This leads us to the question: If E-commerce and fashion companies need to be agile and consumer-friendly, then why are so many still using the same product catalog management methods that were devised years ago? The manual product classification and data attribution process are only leading to an increased risk of error and time delay affecting the brand reputation. Also, leading to lost sales opportunities due to incomplete or inaccurate product records that don’t really reflect the actual product.
In this webinar, we will discuss how to efficiently manage machine learning projects without tech headaches by plugging in your data and building your models instantly.
What you will learn
- How E-commerce companies are using AI to drive more sales and seamless customer experience
- Know the secret sauce of automating time-intensive, repetitive steps to quickly build models
- Demo: A deeper understanding of the end-to-end machine learning workflow for a fashion product catalog management using Skyl.ai
Knowledge Graphs for a Connected World - AI, Deep & Machine Learning MeetupBenjamin Nussbaum
We live in an era where the world is more connected than ever before and the trajectory is such that data relationships will only continue to increase with no signs of slowing down. Connected data is the key to your business succeeding and growing in today’s connected world. Leading enterprises will be the ones that utilize relationship-centric technologies to leverage connections from their internal operations and supply chain to their customer and user interactions. This ability to utilize connected data to understand all the nuanced relationships within their organization will propel them forward as they act on more holistic insights.
Every organization needs a knowledge graph because connected data is an essential foundation to advancing business. Additional reading on connected can be found here: https://www.graphgrid.com/why-connected-data-is-more-useful/
Data Workflows for Machine Learning - Seattle DAMLPaco Nathan
First public meetup at Twitter Seattle, for Seattle DAML:
http://www.meetup.com/Seattle-DAML/events/159043422/
We compare/contrast several open source frameworks which have emerged for Machine Learning workflows, including KNIME, IPython Notebook and related Py libraries, Cascading, Cascalog, Scalding, Summingbird, Spark/MLbase, MBrace on .NET, etc. The analysis develops several points for "best of breed" and what features would be great to see across the board for many frameworks... leading up to a "scorecard" to help evaluate different alternatives. We also review the PMML standard for migrating predictive models, e.g., from SAS to Hadoop.
Flink Forward Berlin 2017: Bas Geerdink, Martijn Visser - Fast Data at ING - ...Flink Forward
ING is using Apache Flink for creating streaming analytics ('fast data') solutions. We created a platform with Flink and Kafka that offers high-throughput and low-latency, ideally suited for complex and demanding use cases in the international bank such as customer notifications and fraud detection. These use cases require fast data processing and a business rules engine and/or machine learning evaluation system. Integrating these components together in a always-on, distributed architecture can be challenging. In this talk, we'll start with a brief overview of the use cases. You'll learn why ING chose Flink for these use cases, and see the architecture of the streaming data platform in depth. Finally, we'll share some lessons learned and useful insights for organizations who embark on a similar journey.
Algorithm Marketplace and the new "Algorithm Economy"Diego Oppenheimer
Talk by Diego Oppenheimer CEO of Algorithmia.com at Data Day Texas 2016.
Peter Sondergaard VP of Research for Gartner recently said the next digital gold rush is "How we do something with data not just what you do with it". During this talk we will cover a brief history of the different algorithmic advances in computer vision, natural language processing, machine learning and general AI and how they are being applied to Big Data today. From there we will talk about how algorithms are playing a crucial part in the next Big Data revolution, new opportunities that are opening up for startups and large companies alike as well as a first look into the role Algorithm Marketplaces will play in this space.
Do you understand the differences between pattern recognition, artificial intelligence and machine learning? And most important, what they separately bring to the table? In this week’s webinar we will tackle the terminology and discuss its recent explosion of popularity, and also look at how the Ogilvy analytics team has applied machine learning methods to effectively answer client challenges and drive value.
Similar to Ria Sankar on Building AI Products (20)
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
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State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
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See how to accelerate model training and optimize model performance with active learning
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Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
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Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
When stars align: studies in data quality, knowledge graphs, and machine lear...
Ria Sankar on Building AI Products
1. www.productschool.com
Part-time Product Management, Coding, Data, Digital
Marketing and Blockchain courses in San Francisco, Silicon
Valley, New York, Santa Monica, Los Angeles, Austin, Boston,
Boulder, Chicago, Denver, Orange County, Seattle, Bellevue,
Toronto, London and Online
3. +5000
Alumni Graduated
across 14
Campuses
· San Francisco
· Silicon Valley
· New York
· Los Angeles
· Santa Monica
· Orange County
· Austin
· Boston
· Boulder
· Chicago
· Denver
· Seattle
· Toronto (Canada)
· London (UK)
15. AI product lifecycle
Define
SimplifyDeploy
• User & Business Understanding
• Data & Metric Definition
• Data Preparation
• UX Design, Model Evaluation
• Model Deployment
• Ongoing Measurement &
Learning
13
2
17. Customer Understanding: Jobs Theory
Seeing tasks from a customer vs. product context
1a
Functional
“Help me wake up
with the best coffee
at consistent
quality”
Social
“Give me a place to
connect with my
friends”
Emotional
“Help me treat
myself
at the end of a long
day”
20. CASE STUDY: Travel Chatbots (Mezi, Expedia)
https://www.altexsoft.com/blog/business/chatbots-in-travel-how-to-build-a-bot-that-travelers-will-love/
21. Data Understanding: Preventing Bias1c
https://i.imgflip.com/1w3emg.jpg
• Comprehensive test cases
(represent the real world)
• Data stratification
• Diverse workforce (avoid tech
bro AI)
• Unconscious bias – review
model outputs for correlations
to race and gender
22. Case Study:
What Celeb do you look like?
http://www.playbuzz.com/chloep19/what-female-celebrity-do-you-look-like
• Image understanding
• Face types, Colors, Tones
• Emotion understanding
23. Metric Understanding: Problem & Output
Definition
1d
https://i.imgflip.com/1w3emg.jpg
• Instrumentation
• Data Quality
• Primary vs. secondary goals
• Product vs. Feature
• Standard vs. derived
metrics
24. Task & Domain Selection1e
When to use:
• Diversify dataset
• Generate dataset
• Generate labelled data
• Structure NL responses
25. Outcome: Find your niche
Customer
DataBusiness
NicheArticulate AI value in terms of:
• Agility / Performance / Cost
• Growth drivers
• Brand value / Industry Status
• Risk reduction
• Accessibility
• Customer Delight
• Convenience/usability
28. Data Preparation
4Cs of data quality
• Correct
• Conforms
• Current
• Consistent
• Consolidated
2a
http://4.bp.blogspot.com/
Why is this important:
• Avoid underfitting, remove bias
• Avoid overfitting, reduce noise
29. Designing AI
5 principles of ethical design
• Humans as Heroes
• Honor Diversity
• Balance EQ and IQ
• Know context
• Evolve over time
2b
https://www.microsoft.com/en-gb/ai/our-approach-to-ai
"The AI tools and services we create must assist humanity and augment our
capabilities."
—Harry Shum, Executive Vice President, AI and Research
31. What model will you choose?
• Classifying cheese into Brie, Mexican,
Parmesan, Mozzarella
32. What model will you choose?
• Analyzing weather patterns to uncover
trends
33. What model will you choose?
• Analyzing weather patterns to FORECAST
the next 10 days
34. What model will you choose?
• To find anomalies in your dataset?
• For spam, fraud filtration?
35. Model Evaluation2d
• Iterative process to find the best model for your scenario (your MVP)
• Balance model performance vs. accuracy
Sample feature recovery rate for a matrix completion algorithm
37. Model Deployment
• Scale data collection
• Scale scenario coverage
• Action movie recos for all users vs.
Movie recos for subset of users
• Scale model
• Check outliers and bias
• Visualize outputs
• Model specific
• Optimizations: Bagging, Boosting
3a
https://cloud.google.com/automl/
https://cloud.withgoogle.com/next18/sf/sessions/session/193072
https://www.youtube.com/watch?v=GbLQE2C181U
48. RECAP: CRISP-DM Methodology
Business &
User
Understanding
Data & Metric
Understanding
Data Prep
Model
Development
Model
Evaluation
Deployment
1. Scope your problem -> find your
niche
2. Build the business case for ML / AI
3. Select your ML model
4. Balance model performance and
accuracy
5. Ensure model relevancye to
changing business needs
6. Human powered vs. machine AI