This document discusses key considerations for managing AI products. It begins with an overview of intelligent systems and the OODA loop model of decision making. It then covers the different areas of AI including machine learning, deep learning, and supervised vs unsupervised learning. The rest of the document provides guidance on strategic areas for AI product management such as corporate and data strategy, analyzing use cases, building minimal viable products, and influencing other teams to deliver AI capabilities. It emphasizes the importance of data acquisition, network effects, and focusing on practical applications that create business value.
How an AI-backed recommendation system can help increase revenue for your onl...Skyl.ai
About the webinar
Picture this: A customer logs onto your E-commerce platform to purchase an item. As soon as they put in the product details into the search bar, they are bombarded with a long catalog of various items that they have to painfully sort through. High chance that they leave without completing a purchase, not sure of what they should pick.
Product recommendation systems must become way better - Platforms need to understand the shopper, and provide them with best-fitting tailored products. This can be way more challenging for retailers with vast catalogs or the ones with only slight variations in products.AI/ML model for 'Recommendations' generated using Skyl.ai can help E-commerce platforms to provide a superior digital-shopping experience to its customers.
This webinar will showcase a live demo of how to build such a robust recommendation model in hours.
What you will learn
- How e-commerce companies drive sales through AI-powered product recommendation engines
- Challenges faced in ML automation and how to overcome those using a unified ML platform
- Live Demo: Demo on how to create a product recommendation system using Skyl.ai end-end ML automation platform
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
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
Executive Briefing: Why managing machines is harder than you thinkPeter Skomoroch
Companies that understand how to apply machine intelligence will scale and win their respective markets over the next decade. That said, delivering on this promise is much harder than most executives realize. Without large amounts of labeled training data, solving most AI problems isn’t possible. The talent and leadership to bridge the worlds of product design, machine learning research, and user experience are scarce. Many organizations will tackle the wrong problems and fail to ship successful AI products that matter to their customers.
Pete Skomoroch explains how to navigate these challenges and build a business where every product interaction benefits from your investment in machine intelligence.
This talk was presented at the 2019 Strata Data Conference in London.
Topics include:
Who defines the data vision and roadmap in your organization?
Who is accountable for building and expanding your competitive moat?
Investing in foundational data infrastructure, training, logging, and tools
Fostering executive support for exploration and innovation, including user-facing data product and algorithm development
How to evaluate new machine intelligence projects and develop a portfolio that delivers
How AI product management differs from traditional product management
How to bridge the worlds of design and machine learning to get to product-market fit
Defining a framework for trading off investments in data quality, machine learning relevance, and other business objectives
Machine learning drove massive growth at consumer internet companies over the last decade, and this was enabled by open software, datasets, and AI research. For many problems, machine learning will produce better, faster, and more repeatable decisions at scale. Unfortunately, building and maintaining these systems is still extremely difficult and expensive. As more machine learning software moves to production, many of our traditional tools and best practices in software development will change.
Pete Skomoroch walks you through what you need to know as we shift from a world of deterministic programs to systems that give unpredictable results on ever-changing training data. To navigate this world powered by nondeterministic data-dependent programs, we’ll also need a new development stack to help us write, test, deploy, and monitor machine learning software.
Presented at OSCON Portland July 18, 2019
How to analyze text data for AI and ML with Named Entity RecognitionSkyl.ai
About the webinar
The Internet is a rich source of data, mainly textual data. But making use of huge quantities of data is a complex and time-consuming task. NLP can help with this problem through the use of Named Entity Recognition systems. Named entities are terms that refer to names, organizations, locations, values etc. NER annotates texts – marking where and what type of named entities occurred in it. This step significantly simplifies further use of such data, allowing for easy categorization of documents, analyze sentiments, improving automatically generated summaries etc.
Further, in many industries, the vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions, and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively extract, tag, index, and manage this fast and ever-growing knowledge.
Through this webinar, we will understand how NER can be used to extract key entities from large volumes of text data
What you will learn
- How organizations are leveraging Named Entity Recognition across various industries
- Live demo - Identify & classify complex terms & with NERC (Named Entity Recognition & Categorization)
- Best practice to automate machine learning models in hours not months
How an AI-backed recommendation system can help increase revenue for your onl...Skyl.ai
About the webinar
Picture this: A customer logs onto your E-commerce platform to purchase an item. As soon as they put in the product details into the search bar, they are bombarded with a long catalog of various items that they have to painfully sort through. High chance that they leave without completing a purchase, not sure of what they should pick.
Product recommendation systems must become way better - Platforms need to understand the shopper, and provide them with best-fitting tailored products. This can be way more challenging for retailers with vast catalogs or the ones with only slight variations in products.AI/ML model for 'Recommendations' generated using Skyl.ai can help E-commerce platforms to provide a superior digital-shopping experience to its customers.
This webinar will showcase a live demo of how to build such a robust recommendation model in hours.
What you will learn
- How e-commerce companies drive sales through AI-powered product recommendation engines
- Challenges faced in ML automation and how to overcome those using a unified ML platform
- Live Demo: Demo on how to create a product recommendation system using Skyl.ai end-end ML automation platform
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
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
Executive Briefing: Why managing machines is harder than you thinkPeter Skomoroch
Companies that understand how to apply machine intelligence will scale and win their respective markets over the next decade. That said, delivering on this promise is much harder than most executives realize. Without large amounts of labeled training data, solving most AI problems isn’t possible. The talent and leadership to bridge the worlds of product design, machine learning research, and user experience are scarce. Many organizations will tackle the wrong problems and fail to ship successful AI products that matter to their customers.
Pete Skomoroch explains how to navigate these challenges and build a business where every product interaction benefits from your investment in machine intelligence.
This talk was presented at the 2019 Strata Data Conference in London.
Topics include:
Who defines the data vision and roadmap in your organization?
Who is accountable for building and expanding your competitive moat?
Investing in foundational data infrastructure, training, logging, and tools
Fostering executive support for exploration and innovation, including user-facing data product and algorithm development
How to evaluate new machine intelligence projects and develop a portfolio that delivers
How AI product management differs from traditional product management
How to bridge the worlds of design and machine learning to get to product-market fit
Defining a framework for trading off investments in data quality, machine learning relevance, and other business objectives
Machine learning drove massive growth at consumer internet companies over the last decade, and this was enabled by open software, datasets, and AI research. For many problems, machine learning will produce better, faster, and more repeatable decisions at scale. Unfortunately, building and maintaining these systems is still extremely difficult and expensive. As more machine learning software moves to production, many of our traditional tools and best practices in software development will change.
Pete Skomoroch walks you through what you need to know as we shift from a world of deterministic programs to systems that give unpredictable results on ever-changing training data. To navigate this world powered by nondeterministic data-dependent programs, we’ll also need a new development stack to help us write, test, deploy, and monitor machine learning software.
Presented at OSCON Portland July 18, 2019
How to analyze text data for AI and ML with Named Entity RecognitionSkyl.ai
About the webinar
The Internet is a rich source of data, mainly textual data. But making use of huge quantities of data is a complex and time-consuming task. NLP can help with this problem through the use of Named Entity Recognition systems. Named entities are terms that refer to names, organizations, locations, values etc. NER annotates texts – marking where and what type of named entities occurred in it. This step significantly simplifies further use of such data, allowing for easy categorization of documents, analyze sentiments, improving automatically generated summaries etc.
Further, in many industries, the vocabulary keeps changing and growing with new research, abbreviations, long and complex constructions, and makes it difficult to get accurate results or use rule-based methods. Named Entity Recognition and Classification can help to effectively extract, tag, index, and manage this fast and ever-growing knowledge.
Through this webinar, we will understand how NER can be used to extract key entities from large volumes of text data
What you will learn
- How organizations are leveraging Named Entity Recognition across various industries
- Live demo - Identify & classify complex terms & with NERC (Named Entity Recognition & Categorization)
- Best practice to automate machine learning models in hours not months
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.
Companies that understand how to apply AI will scale and win their respective markets over the next decade. That said, delivering on this promise and managing machine learning projects is much harder than most people anticpate. Many organizations hire teams of PhDs and data scientists, then fail to ship products that move business metrics. The root cause is often a lack of product strategy for AI, or the failure to adapt their product development processes to the needs of machine learning systems. This talk will cover some of the common ways machine learning fails in practice, the tactical responsibilities of AI product managers, and how to approach product strategy for AI.
Peter Skomoroch, former Head of Data Products at Workday and LinkedIn, will describe how you can navigate these challenges to ship metric moving AI products that matter to your business.
Peter will provide practical advice on:
* The role of an AI Product Manager
* How to evaluate and prioritize your AI projects
* The ways AI product management differs from traditional product management
* Bridging the worlds of design and machine learning
* Making trade offs between data quality and other business metrics
How to deliver a successful product when technology landscape is new and rapidly changing? How to identify technology limitations before moving to production? What if there are no technology experts to answer your questions?
Strategic prototyping can help development teams respond to these issues instead of blindly building full-scale products. I will not be offering silver bullets of simple recipes for success. Instead, you will learn about the practical guidelines for prototyping, combining architecture analysis and a variety of prototyping techniques. With some Big Data systems development flavor on top of it.
Recent Gartner and Capgemini studies predict only around 25% of data science projects are successful and only around 15% make it to full-scale production. Of these, many degrade in performance and produce disappointing results within months of implementation. How can focusing on the desired business outcomes and business use cases throughout a data science project help overcome the odds?
Valdas Maksimavičius - Reducing Technology Risks through PrototypingAgile Lietuva
Topic: Reducing Technology Risks through Prototyping
How to deliver a successful product when technology landscape is new and rapidly changing? How to identify technology limitations before moving to production? What if there are no technology experts to answer your questions?
Strategic prototyping can help development teams respond to these issues instead of blindly building full-scale products. I will not be offering silver bullets of simple recipes for success. Instead, you will learn about the practical guidelines for prototyping, combining architecture analysis and a variety of prototyping techniques. With some Big Data systems development flavor on top of it.
About Valdas:
IT Solutions Architect and Team Lead working at Cognizant, where he leads a Big Data team and enables a paradigm shift in how financial companies use data.
LinkedIn: https://www.linkedin.com/in/valdasm/
Blog: http://blog.thevaldas.com
Top Business Intelligence Trends for 2016 by Panorama SoftwarePanorama Software
10 top BI trends for 2016 – by Panorama
Its all about the insight
Visual perception rules
The learning suggestive system - AI gets real
The data product chain becomes democratized
Cloud (finally)
“Mobile”
Automated data integration
Interned of things data accelerating into reality
Hadoop accelerators are the last chance for Hadoop
Fading of the centralized on–premise DWH
As the adoption of AI technologies increases and matures, the focus will shift from exploration to time to market, productivity and integration with existing workflows. Governing Enterprise data, scaling AI model development, selecting a complete, collaborative hybrid platform and tools for rapid solution deployments are key focus areas for growing data scientist teams tasked to respond to business challenges. This talk will cover the challenges and innovations for AI at scale for the Enterprise focusing on the modernization of data analytics, the AI ladder and AI life cycle and infrastructure architecture considerations. We will conclude by viewing the benefits and innovation of running your modern AI and Data Analytics applications such as SAS Viya and SAP HANA on IBM Power Systems and IBM Storage in hybrid cloud environments.
Big Data: InterConnect 2016 Session on Getting Started with Big Data AnalyticsCynthia Saracco
Learn how to get started with Big Data using a platform based on Apache Hadoop, Apache Spark, and IBM BigInsights technologies. The emphasis here is on free or low-cost options that require modest technical skills.
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?
We explain the history of our agile organization with a focus on the latest round of evolution of our Product and Engineering organization, moving from business-oriented feature teams to mission teams.
Intro to Artificial Intelligence w/ Target's Director of PMProduct School
Given that Machine Learning (ML) is on every product enthusiast’s mind, this talk gave a broad view of the investment landscape for future innovation. Director of Product Management at Target, Aarthi Srinivasan, talked about macro AI themes & trends, how you can build your AI team and how to create a ML backed product vision.
Additionally, this talk armed the attendees with enough information to create your Point of View (POV) on how to incorporate AI into your business.
ICP for Data- Enterprise platform for AI, ML and Data ScienceKaran Sachdeva
IBM Cloud Private for Data, an ultimate platform for all AI, ML and Data Science workloads. Integrated analytics platform based on Containers and micro services. Works with Kubernetes and dockers, even with Redhat openshift. Delivers the variety of business use cases in all industries- FS, Telco, Retail, Manufacturing etc
An AI Maturity Roadmap for Becoming a Data-Driven OrganizationDavid Solomon
The initial version of a maturity roadmap to help guide businesses when adopting AI technology into their workflow. IBM Watson Studio is referenced as an example of technology that can help in accelerating the adoption process.
Using Data Science to Build an End-to-End Recommendation SystemVMware Tanzu
We get recommendations everyday: Facebook recommends people we should connect with; Amazon recommends products we should buy; and Google Maps recommends routes to take. What all these recommendation systems have in common are data science and modern software development.
Recommendation systems are also valuable for companies in industries as diverse as retail, telecommunications, and energy. In a recent engagement, for example, Pivotal data scientists and developers worked with a large energy company to build a machine learning-based product recommendation system to deliver intelligent and targeted product recommendations to customers to increase revenue.
In this webinar, Pivotal data scientist Ambarish Joshi will take you step-by-step through the engagement, explaining how he and his Pivotal colleagues worked with the customer to collect and analyze data, develop predictive models, and operationalize the resulting insights and surface them via APIs to customer-facing applications. In addition, you will learn how to:
- Apply agile practices to data science and analytics.
- Use test-driven development for feature engineering, model scoring, and validating scripts.
- Automate data science pipelines using pyspark scripts to generate recommendations.
- Apply a microservices-based architecture to integrate product recommendations into mobile applications and call center systems.
Presenters: Ambarish Joshi and Jeff Kelly, Pivotal
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.
Companies that understand how to apply AI will scale and win their respective markets over the next decade. That said, delivering on this promise and managing machine learning projects is much harder than most people anticpate. Many organizations hire teams of PhDs and data scientists, then fail to ship products that move business metrics. The root cause is often a lack of product strategy for AI, or the failure to adapt their product development processes to the needs of machine learning systems. This talk will cover some of the common ways machine learning fails in practice, the tactical responsibilities of AI product managers, and how to approach product strategy for AI.
Peter Skomoroch, former Head of Data Products at Workday and LinkedIn, will describe how you can navigate these challenges to ship metric moving AI products that matter to your business.
Peter will provide practical advice on:
* The role of an AI Product Manager
* How to evaluate and prioritize your AI projects
* The ways AI product management differs from traditional product management
* Bridging the worlds of design and machine learning
* Making trade offs between data quality and other business metrics
How to deliver a successful product when technology landscape is new and rapidly changing? How to identify technology limitations before moving to production? What if there are no technology experts to answer your questions?
Strategic prototyping can help development teams respond to these issues instead of blindly building full-scale products. I will not be offering silver bullets of simple recipes for success. Instead, you will learn about the practical guidelines for prototyping, combining architecture analysis and a variety of prototyping techniques. With some Big Data systems development flavor on top of it.
Recent Gartner and Capgemini studies predict only around 25% of data science projects are successful and only around 15% make it to full-scale production. Of these, many degrade in performance and produce disappointing results within months of implementation. How can focusing on the desired business outcomes and business use cases throughout a data science project help overcome the odds?
Valdas Maksimavičius - Reducing Technology Risks through PrototypingAgile Lietuva
Topic: Reducing Technology Risks through Prototyping
How to deliver a successful product when technology landscape is new and rapidly changing? How to identify technology limitations before moving to production? What if there are no technology experts to answer your questions?
Strategic prototyping can help development teams respond to these issues instead of blindly building full-scale products. I will not be offering silver bullets of simple recipes for success. Instead, you will learn about the practical guidelines for prototyping, combining architecture analysis and a variety of prototyping techniques. With some Big Data systems development flavor on top of it.
About Valdas:
IT Solutions Architect and Team Lead working at Cognizant, where he leads a Big Data team and enables a paradigm shift in how financial companies use data.
LinkedIn: https://www.linkedin.com/in/valdasm/
Blog: http://blog.thevaldas.com
Top Business Intelligence Trends for 2016 by Panorama SoftwarePanorama Software
10 top BI trends for 2016 – by Panorama
Its all about the insight
Visual perception rules
The learning suggestive system - AI gets real
The data product chain becomes democratized
Cloud (finally)
“Mobile”
Automated data integration
Interned of things data accelerating into reality
Hadoop accelerators are the last chance for Hadoop
Fading of the centralized on–premise DWH
As the adoption of AI technologies increases and matures, the focus will shift from exploration to time to market, productivity and integration with existing workflows. Governing Enterprise data, scaling AI model development, selecting a complete, collaborative hybrid platform and tools for rapid solution deployments are key focus areas for growing data scientist teams tasked to respond to business challenges. This talk will cover the challenges and innovations for AI at scale for the Enterprise focusing on the modernization of data analytics, the AI ladder and AI life cycle and infrastructure architecture considerations. We will conclude by viewing the benefits and innovation of running your modern AI and Data Analytics applications such as SAS Viya and SAP HANA on IBM Power Systems and IBM Storage in hybrid cloud environments.
Big Data: InterConnect 2016 Session on Getting Started with Big Data AnalyticsCynthia Saracco
Learn how to get started with Big Data using a platform based on Apache Hadoop, Apache Spark, and IBM BigInsights technologies. The emphasis here is on free or low-cost options that require modest technical skills.
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?
We explain the history of our agile organization with a focus on the latest round of evolution of our Product and Engineering organization, moving from business-oriented feature teams to mission teams.
Intro to Artificial Intelligence w/ Target's Director of PMProduct School
Given that Machine Learning (ML) is on every product enthusiast’s mind, this talk gave a broad view of the investment landscape for future innovation. Director of Product Management at Target, Aarthi Srinivasan, talked about macro AI themes & trends, how you can build your AI team and how to create a ML backed product vision.
Additionally, this talk armed the attendees with enough information to create your Point of View (POV) on how to incorporate AI into your business.
ICP for Data- Enterprise platform for AI, ML and Data ScienceKaran Sachdeva
IBM Cloud Private for Data, an ultimate platform for all AI, ML and Data Science workloads. Integrated analytics platform based on Containers and micro services. Works with Kubernetes and dockers, even with Redhat openshift. Delivers the variety of business use cases in all industries- FS, Telco, Retail, Manufacturing etc
An AI Maturity Roadmap for Becoming a Data-Driven OrganizationDavid Solomon
The initial version of a maturity roadmap to help guide businesses when adopting AI technology into their workflow. IBM Watson Studio is referenced as an example of technology that can help in accelerating the adoption process.
Using Data Science to Build an End-to-End Recommendation SystemVMware Tanzu
We get recommendations everyday: Facebook recommends people we should connect with; Amazon recommends products we should buy; and Google Maps recommends routes to take. What all these recommendation systems have in common are data science and modern software development.
Recommendation systems are also valuable for companies in industries as diverse as retail, telecommunications, and energy. In a recent engagement, for example, Pivotal data scientists and developers worked with a large energy company to build a machine learning-based product recommendation system to deliver intelligent and targeted product recommendations to customers to increase revenue.
In this webinar, Pivotal data scientist Ambarish Joshi will take you step-by-step through the engagement, explaining how he and his Pivotal colleagues worked with the customer to collect and analyze data, develop predictive models, and operationalize the resulting insights and surface them via APIs to customer-facing applications. In addition, you will learn how to:
- Apply agile practices to data science and analytics.
- Use test-driven development for feature engineering, model scoring, and validating scripts.
- Automate data science pipelines using pyspark scripts to generate recommendations.
- Apply a microservices-based architecture to integrate product recommendations into mobile applications and call center systems.
Presenters: Ambarish Joshi and Jeff Kelly, Pivotal
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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.
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
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
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:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
5. Decision Making
Insight:
Speeding through the loop
is more important than
quality of the decisions
Sir John Boyd
Distinguished
fighter pilot,
developed military
theories in ‘60s
7. Economic Value Created by AI
99% of the EVC by
AI today is through
Supervised Learning
Input Output
Picture Is it you or not
Loan application Will you repay (%)
Ad User Will the user click?
Speech Recognition Text Transcript
Translation (English) French
Image/ Lidar Position
8. AI Product Management
Why is it different?
•Non-deterministic Product (F1 score)
•Atypical Product Testing
• Output changes with use
•Data science is not engineering
• AI models aren’t like databases
• Significant time spent on data prep
•Still very ‘Researchy’
9. Strategic AI Product Manager
• Corporate Strategy
• PM should drive the Emergent strategy
• Few companies adopting Deliberate strategy
• Data Strategy
• Centralized and secure
• Avoid inaccurate, incomplete, out-of-date
• Kickstart “Data network effects”
10. Data Network Effects
Data network effects occur when your
product, generally powered by machine
learning, becomes smarter as it gets more
data from your users.
http://mattturck.com/the-power-of-data-network-effects/
Network Effect
+
Data Network Effect
11. Strategic AI Product Manager
• Corporate Strategy
• PM should drive the Emergent strategy
• Few companies adopting Deliberate strategy
• Data Strategy
• Centralized and secure
• Avoid inaccurate, incomplete, out-of-date
• Kickstart “Data network effects”
• Data acquisition strategies
12. Data acquisition strategies
• Manual work at least till data network effect kicks in
• Crawling the web (e.g., text summarization/ simplification)
• Narrow the domain (e.g., vertical chatbots)
• Crowdsourcing/ Outsourcing (e.g., Crowdflower, Amazon Mechanical Turk)
• Gamification/ Incentivizing user-in-the loop
• Data capture SDKs in third party apps
• Build “Data trap” (create/sell something valuable to gather data- Tesla?)
• Publicly available datasets
https://www.kdnuggets.com/2016/06/10-data-acquisition-strategies-startups.html
13. Strategic AI Product Manager
• Corporate Strategy
• PM should drive the Emergent strategy
• Few companies adopting Deliberate strategy
• Data Strategy
• Unified data warehouse and secure
• Avoid inaccurate, incomplete, out-of-date
• Kickstart “Data network effects”
• Data acquisition strategies
• Get over “Cold start” problem
14. AI Product Management
• Analyze – What to build
• Decide – How to build
• Build – The building Process
15. Observe Product Trends in the AI Market
• Develop market insights and macro trends
• McKinsey Global Institute (MGI):
• Only 12% use cases progressed beyond experimentation stage
• Adoption limited outside technology sector
• Best-practice is to adopt agile test and learn approach
• Free research from MGI, Gartner, CB Insights
16. Follow trends in Applied AI research
• Your true competitive advantage
• Not from expertise in algorithms
• Ability to shorten time-to-market of products
• Have good handle on latest algorithm advances
• Andrej Karpathy’s arxiv-sanity summarizing latest research
• Follow influencers
17. Cut through hype- focus on practical use cases
• Have insights into practical use cases
• Identify problem
• Perception, Prediction, Personalization
18. Identify Problem
• Perception
• If a typical person can do a mental task with < 1 sec of thought, we can probably
automate it using AI now or in the near future (Andrew Ng, HBR, Nov 2016)
• Prediction
• For any concrete, repeated event that we observe, we can reasonably try to predict
the outcome of the next such event (Andrew Ng, NIPS 2016)
• Personalization
• Serving content desired by users in a personalized manner (Spotify/ Netflix)
19. Cut through hype- focus on practical use cases
• Have insights into practical use cases
• Identify problem
• Perception, Prediction, Personalization
• The PAC Framework to build use cases
• Automate, Classify, Predict
• Customers, Product, Operations
20. The PAC Framework*
Customers Product Operations
Predict • Which customer will buy
• Which user will churn
• Sales Forecast
• Infrastructure Usage
• Employee Attrition
Classify • Who might upgrade
• Micro segmentation
• Customer Input
• Bug Classification
• Manufacturing
Automate • Lead Generation
• Call Follow-Up
• Bug resolution workflows
• Product Training
• Operational Workflows
• Supply chain
* Rob May
21. Cut through hype- focus on practical use cases
• Have insights into practical use cases
• Identify problem
• Perception, Prediction, Personalization
• The PAC Framework to build use cases
• Automate, Classify, Predict
• Customers, Product, Operations
• AI hierarchy of opportunities
• Building on Maslow’s hierarchy of needs
22. AI hierarchy of opportunities*
Superpowers
for humans
Customer Service,
Conversation
Analytics
Retail self-checkout, supply
chain optimization, Pricing
predictions
Security, Predictive Analytics,
Autonomous Vehicles, eDiscovery
Agricultural monitoring, Disease prevention,
Medical Imaging, Smart Home, Geospacial
Analytics, Drug Discovery
* Ankit Jain
(Gradient Ventures)
Transcendence
Esteem and Education
Operational Efficiency
Safety Needs
Physiological Needs
23. Cut through hype- focus on practical use cases
• Have insights into practical use cases
• Identify problem
• Perception, Prediction, Personalization
• The PAC Framework to build use cases
• Automate, Classify, Predict
• Customers, Product, Operations
• AI hierarchy of opportunities
• Focus on use cases that improve EBIT
• RoI, data network effects, data set, drift, tools required
24. Customer and Data obsession
• Customer obsession
• Going beyond product features & benefits
• Understanding meaning for customer’s jobs, their purpose, motivations and
the conscious choices they make
• Data obsession
• Being a champion of digitization while quantifying problems customers care
• Build comprehensive datasets needed for quality AI models
• Fetching data that reflects user’s jobs, behaviors & interaction patterns.
25. Build usable products with simple AI model
• Don’t be over obsessed with complexity of AI models
• Accuracy improvements vs user experience improvements
• AI MVP pyramid (adapted from Jussi Pasanen’s MVP pyramid)
26. • Be familiar with tools and techniques
• Influence AI Engineers, Data Scientists and Data Engineers
• API ecosystem that help serve end users
• Data ingestion tools (Kafka)
• Data processing systems (Spark)
• NoSQL DBMS (Cassandra)
• Commercial alternatives on AWS & GCP (cost structures)
• Avoid reinventing the wheel for commoditized AI techniques
Breadth first approach (Data/ Pipeline/ Model)
27. • Some crucial applications involve high liability
• Law, medicine and safety
• Output requires clear explanation for compliance purposes
• Use the approaches to explaining predictions from deep learning
• Eliminate Bias*
• Articulate organizational values of fairness and equality
• Communicate this to all employees (data scientists)
• Benchmark training data
• Validate algorithms periodically
Consider Model Explainability
* SAP Design Center
28. • Use validated learning loops for quick iterations
• Conceive use cases and map to capabilities of ML, Deep Learning
• Classification (Binary/ Multiclass)
• Regression (prediction)
• Clustering
• Universal approximation of Deep Learning
• Tie to a small set of metrics that matter
• Challenges of end-to-end AI models optimizing multiple objectives
• Agile deep learning
Iteratively build use cases with mapped AI models
31. • Technical language of AI researchers and data scientists.
• Artificial Intelligence, deep learning, machine learning — whatever you’re doing if
you don’t understand it — learn it. Because otherwise you’re going to be a
dinosaur within 3 years! (Mark Cuban)
Understand the fundamentals
Monica Rogati
32. AI Product Management
• Analyze – What to build
• Decide – How to build
• Build – The building Process
33. Influencing across the matrix
• Data scientists and AI Engineers
• Influencing-Up
• SCIPAB model
• Key Assertions based on realized benefits from AI Products
• Establish credibility
• Build Trust
34. Other Considerations
• Driverless AI/ Auto ML
• Automate laborious tasks- Feature Engineering, Model tuning
• Ensembling, Automatic cross-validation, Detect time-series
• AI Monetizing 101
35. AI Monetization models*
• Subscription models
• Freemium through monetizing data network effects
• Outcome-based
• Pay for the outcome (benefit) provided by the product/service
• Asset-Sharing
• Maximize utilization of product across multiple customers
• Revenue-sharing
• Sell product at cost, earn a percentage of client’s product sales
• Data monetization
• Product serves as a vehicle to collect and monetize quality data
• Win-win-win models
* Heiko Schmidt
36. Summary
• The current phase of AI is very promising
• Several opportunities to
• make elegant products that create tremendous value,
• delight customers and significantly transform the business.
• AI Product Manager is a catalyst in this transformation
37. Resources
• This slide deck available at bit.ly/managingAIproducts
• Blog covering salient points in this deck:
• blog.insightdatascience.com/moving-towards-managing-ai-products-5268c5e9ecf2
• Follow me:
• Twitter @prasadvsd
• Linkedin.com/in/pvelamuri