The document discusses several important considerations for companies looking to implement artificial intelligence, including developing an AI transformation playbook, assessing an organization's AI maturity, anticipating costs and timing, deciding whether to build or buy AI solutions, and addressing important legal and ethical issues around explainability, privacy, fairness, and safety. The document provides guidance on how companies can effectively lead their organization into the AI era by establishing the right strategies, processes, and safeguards.
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
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
Intro to Data Science for Non-Data ScientistsSri Ambati
Erin LeDell and Chen Huang's presentations from the Intro to Data Science for Non-Data Scientists Meetup at H2O HQ on 08.20.15
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
A Hybrid Approach to Data Science Project ManagementElaine K. Lee
A talk about how Civis Analytics, a data science consultancy and software company, does project management using a blend of approaches from academia, consulting, and software engineering.
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
How To Interview a Data Scientist
Daniel Tunkelang
Presented at the O'Reilly Strata 2013 Conference
Video: https://www.youtube.com/watch?v=gUTuESHKbXI
Interviewing data scientists is hard. The tech press sporadically publishes “best” interview questions that are cringe-worthy.
At LinkedIn, we put a heavy emphasis on the ability to think through the problems we work on. For example, if someone claims expertise in machine learning, we ask them to apply it to one of our recommendation problems. And, when we test coding and algorithmic problem solving, we do it with real problems that we’ve faced in the course of our day jobs. In general, we try as hard as possible to make the interview process representative of actual work.
In this session, I’ll offer general principles and concrete examples of how to interview data scientists. I’ll also touch on the challenges of sourcing and closing top candidates.
Machine Learning: Opening the Pandora's Box - Dhiana Deva @ QCon São Paulo 2019Dhiana Deva
Introducing Machine Learning is like opening the Pandora's Box - it unveils important issues in your data, metrics, and product. In order to deal with such complexity, pragmatic practices are required to obtain reliable results. In this talk, we will go through learnings gained from introducing Machine Learning in different contexts, from academia, start-ups, consulting to tech giants - covering practices for experimentation, infrastructure, planning, performance evaluation and product vision in the context of machine learning products.
Curious about Data Science? Self-taught on some aspects, but missing the big picture? Well, you’ve got to start somewhere and this session is the place to do it.
This session will cover, at a layman’s level, some of the basic concepts of Data Science. In a conversational format, we will discuss: What are the differences between Big Data and Data Science – and why aren’t they the same thing? What distinguishes descriptive, predictive, and prescriptive analytics? What purpose do predictive models serve in a practical context? What kinds of models are there and what do they tell us? What is the difference between supervised and unsupervised learning? What are some common pitfalls that turn good ideas into bad science?
During this session, attendees will learn the difference between k-nearest neighbor and k-means clustering, understand the reasons why we do normalize and don’t overfit, and grasp the meaning of No Free Lunch.
Operationalizing Machine Learning in the Enterprisemark madsen
TDWI Munich 2019
What does it take to operationalize machine learning and AI in an enterprise setting?
Machine learning in an enterprise setting is difficult, but it seems easy. All you need is some smart people, some tools, and some data. It’s a long way from the environment needed to build ML applications to the environment to run them in an enterprise.
Most of what we know about production ML and AI come from the world of web and digital startups and consumer services, where ML is a core part of the services they provide. These companies have fewer constraints than most enterprises do.
This session describes the nature of ML and AI applications and the overall environment they operate in, explains some important concepts about production operations, and offers some observations and advice for anyone trying to build and deploy such systems.
Day 2 (Lecture 1): Introduction to Statistical Machine Learning and ApplicationsAseda Owusua Addai-Deseh
Presentation on " Introduction to Statistical Machine Learning and Applications" given by Shakir Mohamed, PhD, Research Scientist at DeepMind, London, UK.
What your employees need to learn to work with data in the 21 st century Human Capital Media
The data revolution is well underway. Regardless of the industry or department you manage, working with data will soon be an essential part of all of our jobs, if it isn’t already. This could take the form of basic data analytics, data science, machine learning or artificial intelligence. This can be overwhelming: what do all these terms mean and how can they be leveraged to impact your employees’ work, whether that be in finance, healthcare, tech or the public sector, among many others? This webinar will give you a primer for understanding how data can impact your employees’ work, what they need to know and how to go about educating them on it.
Data science is having a growing effect on our lives, from the content we see on social media feeds to the decisions businesses are making. Along with successes, data science has inspired much hype about what it is and what it can do. So I plan to try and demystify data science and have a discussion about what it really is. What does a day-in-the-life look like? What tools and skills are needed? How is data science successfully applied in the real world? In this talk, I’ll be providing insight into these questions and also speculate the future of data science and its place in business and technology.
Presented at OpenWest 2018
Agile Data Science is a lean methodology that is adopted from Agile Software Development. At the core it centers around people, interactions, and building minimally viable products to ship fast and often to solicit customer feedback. In this presentation, I describe how this work was done in the past with examples. Get started today with our help by visiting http://www.alpinenow.com
The Black Box: Interpretability, Reproducibility, and Data Managementmark madsen
The growing complexity of data science leads to black box solutions that few people in an organization understand. You often hear about the difficulty of interpretability—explaining how an analytic model works—and that you need it to deploy models. But people use many black boxes without understanding them…if they’re reliable. It’s when the black box becomes unreliable that people lose trust.
Mistrust is more likely to be created by the lack of reliability, and the lack of reliability is often the result of misunderstanding essential elements of analytics infrastructure and practice. The concept of reproducibility—the ability to get the same results given the same information—extends your view to include the environment and the data used to build and execute models.
Mark Madsen examines reproducibility and the areas that underlie production analytics and explores the most frequently ignored and yet most essential capability, data management. The industry needs to consider its practices so that systems are more transparent and reliable, improving trust and increasing the likelihood that your analytic solutions will succeed.
This talk will treat the black boxed of ML the way management perceives them, as black boxes.
There is much work on explainable models, interpretability, etc. that are important to the task of reproducibility. Much of that is relevant to the practitioner, but the practitioner can become too focused on the part they are most familiar with and focused on. Reproducing the results needs more.
How the Analytics Translator can make your organisation more AI drivenSteven Nooijen
Today, about 80% of companies considers data as an essential part of their strategy. However, although most of these companies are taking models into production, they still have trouble turning their data and insights into valuable AI solutions. With businesses heavily invested in data and AI, what is it that actually makes the difference for being successful with AI?
In this talk, I will argue that the extent to which AI is embedded in the organisation is crucial to success. Furthermore, I will show why the Analytics Translator is the designated person to drive AI adoption by the business and what his or her tasks should look like. The insights shared come from our own experience as consultants as well as interviews with top Dutch enterprises about their AI maturity.
AI Maturity Levels and the Analytics TranslatorGoDataDriven
Buzzwords like Big Data, Cloud, and AI have been out there now for a couple of years. But today, businesses have a clear focus on the application of data use cases and the challenges around that such as metadata management, governance, security, and maintainability in general. Everybody seems to have some version of a data lake and wants to consolidate it into something (more) useful, or move from an on-premise version to the cloud. There is a general need to streamline current practices while also attempting to give multiple segments of users (data scientists, analysts, marketeers, business people, and HR) access in a way that is tailored to their needs and skills. In other words: businesses today are heavily invested in data and AI, but many have a hard time knowing how to mature it to the next level.
This is exactly where a "maturity model" comes into play. The goal of a maturity model is to help businesses in understanding their current and target competencies. This helps organisations in defining a roadmap for improving their competency. A maturity model is therefore one way of structuring progression, whether the company already embraces data science as a core competency, or, if it is just getting started.
In this presentation on maturity models, we answer the following questions:
1. What exactly is a maturity model and why would you need it? We address this by sharing GoDataDriven's maturity model and describing the different phases we have identified based on our experience in the field.
2. How can you use a maturity model to advance your organisation? Having a maturity model alone is not enough, in order for it to be valuable you need to act upon it. This paper provides concrete examples on how to do act based on practical stories and experiences from our clients and ourselves.
Intro to Data Science for Non-Data ScientistsSri Ambati
Erin LeDell and Chen Huang's presentations from the Intro to Data Science for Non-Data Scientists Meetup at H2O HQ on 08.20.15
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
A Hybrid Approach to Data Science Project ManagementElaine K. Lee
A talk about how Civis Analytics, a data science consultancy and software company, does project management using a blend of approaches from academia, consulting, and software engineering.
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
How To Interview a Data Scientist
Daniel Tunkelang
Presented at the O'Reilly Strata 2013 Conference
Video: https://www.youtube.com/watch?v=gUTuESHKbXI
Interviewing data scientists is hard. The tech press sporadically publishes “best” interview questions that are cringe-worthy.
At LinkedIn, we put a heavy emphasis on the ability to think through the problems we work on. For example, if someone claims expertise in machine learning, we ask them to apply it to one of our recommendation problems. And, when we test coding and algorithmic problem solving, we do it with real problems that we’ve faced in the course of our day jobs. In general, we try as hard as possible to make the interview process representative of actual work.
In this session, I’ll offer general principles and concrete examples of how to interview data scientists. I’ll also touch on the challenges of sourcing and closing top candidates.
Machine Learning: Opening the Pandora's Box - Dhiana Deva @ QCon São Paulo 2019Dhiana Deva
Introducing Machine Learning is like opening the Pandora's Box - it unveils important issues in your data, metrics, and product. In order to deal with such complexity, pragmatic practices are required to obtain reliable results. In this talk, we will go through learnings gained from introducing Machine Learning in different contexts, from academia, start-ups, consulting to tech giants - covering practices for experimentation, infrastructure, planning, performance evaluation and product vision in the context of machine learning products.
Curious about Data Science? Self-taught on some aspects, but missing the big picture? Well, you’ve got to start somewhere and this session is the place to do it.
This session will cover, at a layman’s level, some of the basic concepts of Data Science. In a conversational format, we will discuss: What are the differences between Big Data and Data Science – and why aren’t they the same thing? What distinguishes descriptive, predictive, and prescriptive analytics? What purpose do predictive models serve in a practical context? What kinds of models are there and what do they tell us? What is the difference between supervised and unsupervised learning? What are some common pitfalls that turn good ideas into bad science?
During this session, attendees will learn the difference between k-nearest neighbor and k-means clustering, understand the reasons why we do normalize and don’t overfit, and grasp the meaning of No Free Lunch.
Operationalizing Machine Learning in the Enterprisemark madsen
TDWI Munich 2019
What does it take to operationalize machine learning and AI in an enterprise setting?
Machine learning in an enterprise setting is difficult, but it seems easy. All you need is some smart people, some tools, and some data. It’s a long way from the environment needed to build ML applications to the environment to run them in an enterprise.
Most of what we know about production ML and AI come from the world of web and digital startups and consumer services, where ML is a core part of the services they provide. These companies have fewer constraints than most enterprises do.
This session describes the nature of ML and AI applications and the overall environment they operate in, explains some important concepts about production operations, and offers some observations and advice for anyone trying to build and deploy such systems.
Day 2 (Lecture 1): Introduction to Statistical Machine Learning and ApplicationsAseda Owusua Addai-Deseh
Presentation on " Introduction to Statistical Machine Learning and Applications" given by Shakir Mohamed, PhD, Research Scientist at DeepMind, London, UK.
What your employees need to learn to work with data in the 21 st century Human Capital Media
The data revolution is well underway. Regardless of the industry or department you manage, working with data will soon be an essential part of all of our jobs, if it isn’t already. This could take the form of basic data analytics, data science, machine learning or artificial intelligence. This can be overwhelming: what do all these terms mean and how can they be leveraged to impact your employees’ work, whether that be in finance, healthcare, tech or the public sector, among many others? This webinar will give you a primer for understanding how data can impact your employees’ work, what they need to know and how to go about educating them on it.
Data science is having a growing effect on our lives, from the content we see on social media feeds to the decisions businesses are making. Along with successes, data science has inspired much hype about what it is and what it can do. So I plan to try and demystify data science and have a discussion about what it really is. What does a day-in-the-life look like? What tools and skills are needed? How is data science successfully applied in the real world? In this talk, I’ll be providing insight into these questions and also speculate the future of data science and its place in business and technology.
Presented at OpenWest 2018
Agile Data Science is a lean methodology that is adopted from Agile Software Development. At the core it centers around people, interactions, and building minimally viable products to ship fast and often to solicit customer feedback. In this presentation, I describe how this work was done in the past with examples. Get started today with our help by visiting http://www.alpinenow.com
The Black Box: Interpretability, Reproducibility, and Data Managementmark madsen
The growing complexity of data science leads to black box solutions that few people in an organization understand. You often hear about the difficulty of interpretability—explaining how an analytic model works—and that you need it to deploy models. But people use many black boxes without understanding them…if they’re reliable. It’s when the black box becomes unreliable that people lose trust.
Mistrust is more likely to be created by the lack of reliability, and the lack of reliability is often the result of misunderstanding essential elements of analytics infrastructure and practice. The concept of reproducibility—the ability to get the same results given the same information—extends your view to include the environment and the data used to build and execute models.
Mark Madsen examines reproducibility and the areas that underlie production analytics and explores the most frequently ignored and yet most essential capability, data management. The industry needs to consider its practices so that systems are more transparent and reliable, improving trust and increasing the likelihood that your analytic solutions will succeed.
This talk will treat the black boxed of ML the way management perceives them, as black boxes.
There is much work on explainable models, interpretability, etc. that are important to the task of reproducibility. Much of that is relevant to the practitioner, but the practitioner can become too focused on the part they are most familiar with and focused on. Reproducing the results needs more.
How the Analytics Translator can make your organisation more AI drivenSteven Nooijen
Today, about 80% of companies considers data as an essential part of their strategy. However, although most of these companies are taking models into production, they still have trouble turning their data and insights into valuable AI solutions. With businesses heavily invested in data and AI, what is it that actually makes the difference for being successful with AI?
In this talk, I will argue that the extent to which AI is embedded in the organisation is crucial to success. Furthermore, I will show why the Analytics Translator is the designated person to drive AI adoption by the business and what his or her tasks should look like. The insights shared come from our own experience as consultants as well as interviews with top Dutch enterprises about their AI maturity.
AI Maturity Levels and the Analytics TranslatorGoDataDriven
Buzzwords like Big Data, Cloud, and AI have been out there now for a couple of years. But today, businesses have a clear focus on the application of data use cases and the challenges around that such as metadata management, governance, security, and maintainability in general. Everybody seems to have some version of a data lake and wants to consolidate it into something (more) useful, or move from an on-premise version to the cloud. There is a general need to streamline current practices while also attempting to give multiple segments of users (data scientists, analysts, marketeers, business people, and HR) access in a way that is tailored to their needs and skills. In other words: businesses today are heavily invested in data and AI, but many have a hard time knowing how to mature it to the next level.
This is exactly where a "maturity model" comes into play. The goal of a maturity model is to help businesses in understanding their current and target competencies. This helps organisations in defining a roadmap for improving their competency. A maturity model is therefore one way of structuring progression, whether the company already embraces data science as a core competency, or, if it is just getting started.
In this presentation on maturity models, we answer the following questions:
1. What exactly is a maturity model and why would you need it? We address this by sharing GoDataDriven's maturity model and describing the different phases we have identified based on our experience in the field.
2. How can you use a maturity model to advance your organisation? Having a maturity model alone is not enough, in order for it to be valuable you need to act upon it. This paper provides concrete examples on how to do act based on practical stories and experiences from our clients and ourselves.
Building a 360 Degree View of Your Customers on BICSPerficient, Inc.
Why there is a need for Customer 360 and what the proposed cloud based solution is. We cover the stages of strategic marketing and how Oracle BI can help.
For the next 40 minutes, I’d like to share with you our experience leveraging AI for businesses.
We’ll first do a tiny little quiz to check your AI knowledge - don’t worry it’s not technical at all.
Then we discuss the common challenges that startups face and give examples on how you can navigate them.
From here, you can do a self-assessment of where you are in the AI maturity journey.
Then we go to through 3 case studies in detail based on their AI maturity. At the end, we also discuss how you can spot opportunities to use AI in your company!
Finally, we close off with a summary and a list of recommendations of no-code AI tools that you can take a look at :)
It’s a loot of content, but the idea is that you will be able to walk away with a renewed understanding of what it takes to build an AI-enabled business but more importantly, how you can be in the driver seat and do it yourself.
We’ll take Q&As at the end and if you have any questions please add them onto Slido :)
[DSC Europe 22] The Making of a Data Organization - Denys HolovatyiDataScienceConferenc1
Data teams often struggle to deliver value. KPIs, data pipelines, or ML driven predictions aren't inherently useful - unless the data team enables the business to use them. Having worked on 37 data projects over the past 5 years, with total client revenue clocking at about $350B, I started noticing simple success factors - and summarized those in the Operating Model Canvas & the Value Delivery Process. With those, I branched out into what I call data organization consulting and help clients build their data teams for success, the one you see not only on paper but also in your P&L. In this talk, I'll share some insight with you.
Course 8 : How to start your big data project by Eric Rodriguez Betacowork
For more info about our Big Data courses, check out our website ➡️ https://www.betacowork.com/big-data/
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"Data is the new oil" - Many companies and professionals do not know how to use their data or are not aware of the added value they could gain from it.
It is in response to these problems that the project “Brussels: The Beating Heart of Big Data” was born.
This project, financed by the Region of Brussels Capital and organised by Betacowork, offers 3 training cycles of 10 courses on big data, at both beginner and advanced levels. These 3 cycles will be followed by a Hackathon weekend.
No prerequisites are required to start these courses. The aim of these courses is to familiarize participants with the principles of Big Data.
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For more info about our Big Data courses, check out our website ➡️ https://www.betacowork.com/big-data/
Ethical AI at VDAB, presented by Vincent Buekenhout (Ethical AI Lead, VDAB) a...Patrick Van Renterghem
Vincent Buekenhout presented the various AI initiatives at VDAB, its AI4Good strategy, the way applications are designed, and most of all, the way ethics, measurements through KPI's, explainability and fairness play a role in this. Vincent also explained how ethics-by-design works at VDAB.
Speaker: Venkatesh Umaashankar
LinkedIn: https://www.linkedin.com/in/venkateshumaashankar/
What will be discussed?
What is Data Science?
Types of data scientists
What makes a Data Science Team? Who are its members?
Why does a DS team need Full Stack Developer?
Who should lead the DS Team
Building a Data Science team in a Startup Vs Enterprise
Case studies on:
Evolution Of Airbnb’s DS Team
How Facebook on-boards DS team and trains them
Apple’s Acqui-hiring Strategy to build DS team
Spotify -‘Center of Excellence’ Model
Who should attend?
Managers
Technical Leaders who want to get started with Data Science
How to classify documents automatically using NLPSkyl.ai
About the webinar
Documents come in different shapes and sizes - From technical documents, customer support chat, emails, reviews to news articles - all of them contain information that is valuable to the business.
Managing these large volume data documents in a traditional manual way has been a complex and time-consuming task that requires enormous human efforts.
In this webinar, we will discuss how Machine learning can be used to identify and automatically label news articles into categories like business, politics, music, etc. This can be applied in another context like categorizing emails, reviews, and processing text documents, etc.
What you will learn
- How businesses are leveraging document classification to their advantage
- Best practice to automate machine learning models in hours not months
- Demo: Classify news articles into the right category using convolution neural network
GCC Analytics Best Practices" is a comprehensive guide outlining the most effective strategies for leveraging analytics in GCC (Gulf Cooperation Council) countries. This PDF highlights key methodologies, tools, and case studies, empowering organizations to harness data-driven insights and make informed business decisions. From data collection and analysis to visualization and reporting, this resource offers invaluable guidance for optimizing analytics processes and maximizing ROI in the GCC region.
Tableau Drive, A new methodology for scaling your analytic cultureTableau Software
Tableau Drive is a methodology for scaling out self-service analytics. Drive is based on best practices from successful enterprise deployments. The methodology relies on iterative, agile methods that are faster and more effective than traditional long-cycle deployment. A cornerstone of the approach is a new model of a partnership between business and IT.
The Drive Methodology is available for free. Some organizations will choose to execute Drive themselves; others will look to Tableau Services or Tableau Partners for expert help.
So Now You’re a UiPath Developer – What’s Next?” What Role do You Play as Dev...DianaGray10
As a UiPath Developer, what are the important tasks you should consider to be part of your job requirements? Join this session to find out more and ask questions from experienced experts. Topics include:
Where's your starting point?
Are you using a broad use case or a detailed PDD?
Are you involved in the Definition/Brainstorming?
Are you building and deploying or are you building and sending?
Are you in charge of maintaining? What should the maintenance-to-build ratio be?
What's your interaction with the C-Suite? What are the KPI requirements?
Speakers:
Chris Bolin, Senior RPA Engineer @ Gamestop and 2X UiPath MVP
Mason Turvey, Intelligent Automation Lead, Academy Bank and UiPath MVP
1.0 how to empower audit through data analytics for icai kolkataeirc_icai
Hidden truth in data and the power of data analytics •
How does Data Analytics Impact Audit process?
• How to use Data Analytics for various types of Audit?
• Practical examples of data analytics in audit – walk-through • How to build competencies and skill set for using data analytics?
• Case studies of using Data Analytics for providing better assurance
Agile and CMMI: Yes, They Can Work TogetherTechWell
There is a common misconception that agile and CMMI cannot work together. CMMI is viewed as a documentation heavy, slow, process-driven model—the polar opposite of agile principles. The cost of documentation for an appraisal is viewed as another drawback. Join Ed Weller to see why a large organization chose to use the practices in the CMMI to complement agile, and a formal appraisal to improve and evaluate their performance. When mixing approaches that seem contradictory, the first step is to understand the benefits, drawbacks, and cost of each approach and then identify complementary additions. This includes myth busting the misperceptions about both agile and CMMI. The second step, using a formal CMMI appraisal to evaluate organizational performance, requires an understanding of the CMMI model that goes beyond a “checklist approach” requiring extensive documentation. Using lean principles, the appraisal team minimized “appraisal documentation” by using the day-to-day team output. Ed shows that agile and CMMI can be complementary due to executive leadership, lean implementation, and organization training, as demonstrated by a formal appraisal and business results.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
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This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
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Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
1. Build AI in your company
management, culture, legal, ethics, governance
Orange Belt - Session 3
1
2. What we have seen so far
1. What is AI
2. What can AI do and what it can’t do
3. How to select a project
4. What are the steps necessary for a first successful ML
project
2
3. AI is not traditional software
A totally different lifecycle
3
6. We saw this last week
01
03
02
06
04
05
Monitoring & Updates
Have the right talents & solutions
Maintenance
Select the right question
Choose the performance metric
Decide the level of explainability
Identify
Use the right architecture
Have the talents in place
Deploy
Find the right data
Structure annotate data
Clean Data
Data
Decide on an acceptable error
Test on the right scope
Evaluate
Select the right algorithm
Tune the model
Model
6
8. Plan for today
0 (Base on the case seen in the assignment) What does it feel like to work on a
complex ML project ?
1. AI Transformation Playbook
2. AI Maturity of your company
3. Perspectives on costs & roadmap
4. Build vs Buy
5. Legal & ethics considerations
6. Human and AI interactions
8
9. 1. AI Transformation Playbook
How to lead your company into the AI era
9
Source : https://landing.ai/ai-transformation-playbook/
10. AI Transformation Playbook
1. Execute pilot projects to gain momentum
2. Build an in-house AI team
3. Provide broad AI training
4. Develop an AI strategy
5. Develop internal and external communications
10
11. 1. Execute pilot project to gain
momentum
• More important for the initial project to succeed rather than
be the most valuable
• Show traction within 6-12 months
• Can be in-house or outsourced
11
12. 2. Build an in-house AI team
Centralized AI Platform
12
13. 3. Provide broad AI training
Role What they should learn Nb hours
of training
Executives and senior
business leaders
- What AI can do for the company ?
- AI Strategy
- Resource allocation
>= 4 hours
Leaders of divisions
working on AI projects
- Set project direction (both technical and
business diligence)
- Resource allocation
- Monitor progress
>= 12
hours
AI engineer trainees - Build and ship AI software
- Gather data
- Execute on specific AI projects
>= 100
hours
13
14. How to
document
yourselves
AI for Everyone, by Andrew Ng
Deep Learning Specialization
3blue1brown
Siraj
OpenAI
ImportAI
https://towardsdatascience.c
om/
14
15. 4. Develop an AI strategy
• Build several difficult AI assets that are broadly aligned with
a coherent strategy
• Leverage AI to create an advantage specific to your industry
sector.
• Design strategy aligned with the “Virtuous Cycle of AI”
AI plays a role here
15
16. 4. Develop an AI strategy
• Consider creating a data strategy
• Strategic data acquisition
• Unified data warehouse
• Create network effects and platform advantages
• In industries with “winner take all” dynamics, AI can be an
accelerator
• What about more traditional strategy framework ?
• AI can allow a low cost strategy
• AI can allow a high value product strategy
16
17. 5. Develop internal and external
communications
• Investor relations
• Government relations
• Consumer / user education
• Talen / recruitment
• Internal communications
17
18. AI pitfalls to avoid
Don’t : Do :
- Expect AI to solve everything - Be realistic about what AI can and cannot do,
given limitations of technology, data and
engineering resources
- Hire 2-3 ML engineers and count solely on
them to come up with use cases
- Pair engineers with business talent and work
across cross-functional team to find valuable
projects
- Expect the AI project to work the first time - Plan for AI development to be an iterative
process, with multipe attemps needed to
succeed.
- Expect traditional planning processes to apply
without changes
- Work with AI team to establish timeline
estimates, milestones, KPIs, etc.
- Think you need superstar AI engineers before
you can do anything
- Keep building the team but get going with the
team you already have 18
19. Some initial steps you can take
• Start learning (with this course)
• Start brainstorming projets
• Hire a few ML/DS people to help
• Hire or appoint an AI leader (VP AI, CAIO, …)
• Discuss with CEO/Board possibilities of AI Transformation
• Will your company be much more valuable and/or more effective if it
were good at AI ?
19
20. Exercise
What is the first step in the AI Transformation Playbook for helping
your company become good at AI?
20
21. Exercise
Of the following options, which is the most important trait of your
first pilot project?
A) Succeed and show traction within 6-10 months
B) Drive extremely high value for the business
C) Be executed by an in-house team
D) None of the above
21
22. Exercise
Say you are building the DropBox OCR system, and want to
accumulate data for your product through having many users.
Which of these represents the “Virtuous circle of AI” for this
product?
22
27. 3-steps roadmap to maturity
Part 1
EXPLORING
Part 2
EXPERIMENTING
Part 3
INTEGRATING
STRATEGY
DATA
PEOPLE
LEGALÐICS
PRODUCT
27
28. Part 1
EXPLORING
Part 2
EXPERIMENTING
Part 3
INTEGRATING
STRATEGY
No use case
No objectives / metrics
No budget
DATA
No infrastructure
Data Silos Descriptive analytics
PEOPLE
No data scientists
No education
LEGALÐICS
No legal compliance No
principles or processes
PRODUCT
Business cases but no
development
28
29. Part 1
EXPLORING
Part 2
EXPERIMENTING
Part 3
INTEGRATING
STRATEGY
No use case
No objectives / metrics
No budget
Use it for optim & prediction
Know a few use cases
Core product
Use case bank
Competitive advantage
DATA
No infrastructure
Data Silos Descriptive analytics
PEOPLE
No data scientists
No education
LEGALÐICS
No legal compliance No
principles or processes
PRODUCT
Business cases but no
development
29
31. Part 1
EXPLORING
Part 2
EXPERIMENTING
Part 3
INTEGRATING
STRATEGY
No use case
No objectives / metrics
No budget
Use it for optim & prediction
Know a few use cases
Core product
Use case bank
Competitive advantage
DATA
No infrastructure
Data Silos
Descriptive analytics
descriptive → prescriptive
No data consolidation
ETL
Data Lake
Compounding value
PEOPLE
No data scientists
No education
LEGALÐICS
No legal compliance No
principles or processes
PRODUCT
Business cases but no
development
31
35. Data annotation pipeline
To go at scale, you need guidelines, internal and external annotators.
Even pre-annotation with machine learning that can be validated
DOG
CAT
35
36. Standardise quality check
Find the relevant metrics
Exemple Sound: signal to noise ratio / cross-talk / silence detection
36
39. Part 1
EXPLORING
Part 2
EXPERIMENTING
Part 3
INTEGRATING
STRATEGY
No use case
No objectives / metrics
No budget
Use it for optim & prediction
Know a few use cases
Core product
Use case bank
Competitive advantage
DATA
No infrastructure
Data Silos Descriptive analytics
descriptive → prescriptive
No data consolidation
ETL
Data Lake
Compounding AOV
PEOPLE
No data scientists
No education
A few data scientists
Global acculturation / education
Chief Data Officer
SWAT Team
Use case specialists
LEGALÐICS
No legal compliance No
principles or processes
PRODUCT
Business cases but no
development
39
40. Example roles
• Software Engineer
• Build user interface, web & mobile applications, back end
operations, …
• Machine Learning Engineer
• Input (A) to Output (B)
• Machine Learning Researcher
• Extend state-of-the-art in ML
40
41. Example roles
• Data Scientist
• Examine data and provide insights
• Make presentation and communicate to team / executives
• Data Engineer
• Organize Data
• Make sure data is saved in an easily accessible, documented and
cost effective way
• More and more required as the amount of data managed by
companies increases
• AI Product Manager
• Help decide what to build; what’s feasible and valuable
41
42. Getting started with a small team
• 1 Software Engineer, or
• 1 Machine Learning Engineer / Data Scientist, or
• Nobody by yourself
42
47. Exercise
Suppose you are building a trigger word detection system, and want
to hire someone to build a system to map from Inputs A (audio clip) to
Outputs B (whether the trigger word was said), using existing AI
technology. Out of the list below, which of the following hires would
be most suitable for writing this software?
47
48. Part 1
EXPLORING
Part 2
EXPERIMENTING
Part 3
INTEGRATING
STRATEGY
No use case
No objectives / metrics
No budget
Use it for optim & prediction
Know a few use cases
Core product
Use case bank
Competitive advantage
DATA
No infrastructure
Data Silos Descriptive analytics
descriptive → prescriptive
No data consolidation
ETL
Data Lake
Compounding AOV
PEOPLE
No data scientists
No education
A few data scientists
Global acculturation / education
Chief Data Officer
SWAT Team
Use case specialists
LEGALÐICS
No legal compliance No
principles or processes
GDPR Compliant
Core explanations & policies
Ethical principles
Corporate practice
Part of incentive programs
Core product advantage
PRODUCT
Business cases but no
development
48
49. Part 1
EXPLORING
Part 2
EXPERIMENTING
Part 3
INTEGRATING
STRATEGY
No use case
No objectives / metrics
No budget
Use it for optim & prediction
Know a few use cases
Core product
Use case bank
Competitive advantage
DATA
No infrastructure
Data Silos Descriptive analytics
descriptive → prescriptive
No data consolidation
ETL
Data Lake
Compounding AOV
PEOPLE
No data scientists
No education
A few data scientists
Global acculturation / education
Chief Data Officer
SWAT Team
Use case specialists
LEGALÐICS
No legal compliance No
principles or processes
GDPR Compliant
Core explanations & policies
Ethical principles
Corporate practice
Part of incentive programs
Core product advantage
PRODUCT
Business cases but no
development
Use of APIs
Discrete proof of concepts
Pilots
AI is core product
& core competency
49
50. Exercise : create your own roadmap
Part 1
EXPLORING
Part 2
EXPERIMENTING
Part 3
INTEGRATING
STRATEGY
No use case
No objectives / metrics
No budget
Use it for optim & prediction
Know a few use cases
Core product
Use case bank
Competitive advantage
DATA
No infrastructure
Data Silos Descriptive analytics
descriptive → prescriptive
No data consolidation
ETL
Data Lake
Compounding AOV
PEOPLE
No data scientists
No education
A few data scientists
Global acculturation / education
Chief Data Officer
SWAT Team
Use case specialists
LEGALÐICS
No legal compliance No
principles or processes
GDPR Compliant
Core explanations & policies
Ethical principles
Corporate practice
Part of incentive programs
Core product advantage
PRODUCT
Business cases but no
development
Use of APIs
Discrete proof of concepts
Pilots
AI is core product
& core competency
50
62. To sum up
1. Anticipate the cost drivers (data, accuracy, problem difficulty)
2. There is a big upfront cost to anticipate as opposed to regular projects
3. The cost scales nonlinearily with the accuracy requirements
4. The time it takes to get that accuracy is nonlinear as well
62
63. Exercise
What is the biggest cost of your current idea?
What do you think will be the bottleneck?
What type of strategy could you devise to
anticipate that?
63
66. With a consultant you don’t know, always look to start with a small
proof of concept deliverable to prove to yourself that this consultant
knows their stuff. Work with the consultant to come up with a project
that is a low hanging fruit. Something that they can deliver on quickly
without much development effort (e.g. based on existing code they
already have, and data you have already collected). If this first step
goes well, then you can confidently move to a bigger project scope.
66
68. Access to resources
(cloud, ML libraries,
production systems)
Access to talent Proven success
stories to get you up
and running in no time
So why would you do everything yourselves ?
Don’t try to run faster than the train
Collaborate with Startups!
68
69. 5. Legal & Ethics
What should you be careful about
69
82. 1. AI systems must be transparent.
2. An AI must have a “deeply rooted” right to the
information it is collecting.
3. Consumers must be able to opt out of the system.
4. The data collected and the purpose of the AI must be
limited by design.
5. Data must be deleted upon consumer request.
Bernhard DebatinProfessor and Director of the Institute for Applied and Professional Ethics
Guidelines for ethical privacy
83
83. 1. Clear consent requests to customer (and easy out)
2. Breach notifications within 72h
3. Right to access all personal data upon request
4. Right to be forgotten
in practice
84
91. ‘build a face recognition service that can detect male/female genders, with pre-
defind specific age groups, and these specific subset of races, and ethnicities in
the requirements document (which is grounded in a standard taxonomy from a
neutral organization such as the United Nations Race and Ethnicity taxonomy))
with at least 90% accuracy on ‘these’ given specific test datasets’ where ‘these’
test datasets were carefully crafted by the offering management team to have an
even distribution of all the genders, age groups, specific races, and ethnicities for
which the model is supposed do well’
Specific, focus & measurable
92
98. To summarise
1. Specify a robust goal
2. Make sure you respect privacy
3. Make it explainable
4. Make it secure
5. Make it transparent
99
99. Exercise
Can you identify the most sensitive data in your organizations?
Do you already have a strategy to be compliant?
Do you need a big explainability or would you rather get better
performance?
100
100. 6. Towards better HCI
Human-Computer interactions have to be reconsidered
101
107. Human vs Machine
• Machines never forget
• Crunch numbers and scan fast
• Never bored or impatient or tired
• People have better emotional nuances + humanity
• People have better common sense
• People can tackle new tasks easily
108
112. Keep human psychology in mind
Lots of false positive = irritating
Introducing without consultation = resentment
Most often good decision = overtrust (automation bias)
…
113
113. Exercise
Try to imagine the end product of your project, leveraging both AI and
humans using the type of collaborations we just talked about
What would the user be the most sensitive about ?
114