This talk covers the differences between being Data Driven and Data Informed based on Booking.com's design principle "Informed by data, driven by enmpathy"
This document contains contact information and a summary of the skills and experience of Jennifer Doty. It lists her education as a Bachelor of Architecture from Illinois Institute of Technology with a 3.7 GPA. It provides details of her various internships and roles in architecture, urban planning, and research organizations where she gained experience in areas like BIM/CAD modeling, graphics creation, project documentation, and content management. References are also provided.
6 Reasons Technophiles Make Better Graphic DesignersDesignMantic
Technophilia refers to a strong enthusiasm for technology, especially new technologies such. Technophiles are individuals who are enthusiastic about technology and view technology's interaction with society as creating an utopia, cyber or otherwise, and a strong indescribable futuristic feeling.
We're all technophiles in that matter. But Graphic Designers, who are often needed to juggle between different tasks, are bound to love tech. Technology gives them the power to take on and succeed as they grow more and more into design. Here are 6 compelling reasons why technophiles make better designers.
Enjoy the read and do tell us if you agree!
http://www.ianlivingstone.ca/2015/11/17/enabling-autonomy/
The drastic increase in the importance of knowledge workers has turned traditional management structures and philosophy upside down. Previously, all of the information and authority was centralized in management and workers simply operated according to some proscribed procedure with limited ability to make their own decisions. However, the rise of the knowledge worker such as developers, designers, and product managers has thrown these structures out the window as they've proven unable to deliver incredible products and technology.
The new name of the game is enabling teams to operate autonomously and build towards a vision that is seeded by the leadership but authored by the team. How do we enable teams to operate autonomously while ensuring that they are held accountable? How does this change as organizations grow? Why does this matter and what are the results?
The 3 Key Barriers Keeping Companies from Deploying Data Products Dataiku
Getting from raw data to deploying data-driven solutions requires technology, data, and people. All of which exist. So why aren’t we seeing more truly data-driven companies: what's missing and why? During Strata Hadoop World Singapore 2015, Pauline Brown, Director of Marketing at Dataiku, explains how lack of collaboration is what is keeping companies from building and deploying data products effectively. Learn more about Dataiku and Data Science Studio: www.dataiku.com
Catalina Butnaru, London Ambassador at City.ai - Working with AI - future-pro...Codiax
The document proposes an AI-compatible process for developing software that focuses on defining problems, researching use cases, and mapping skills upfront, then auditing data for quality and privacy before running parallel training experiments, benchmarking performance, and implementing live training with ongoing feedback in an agile manner overseen by roles like a data owner, coordinator, and ethical board.
This document discusses trends in artificial intelligence (AI) funding and applications. It notes that AI funding has grown unprecedentedly, reaching over $15 billion in 2017. Popular investment areas for startups include healthcare diagnostics, automation, cybersecurity, and autonomous vehicles. Corporations are also increasingly investing in AI, focusing on platforms, business process automation, and understanding customers. The document provides recommendations for companies looking to adopt AI, such as starting small with cost savings projects, creating a product team using agile practices, and identifying key data needs.
From Lab to Factory: Or how to turn data into valuePeadar Coyle
We've all heard of 'big data' or data science, but how do we convert these trends into actual business value. I share case studies, and technology tips and talk about the challenges of the data science process. This is all based on two years of in-the-field research of deploying models, and going from prototypes to production.
These are slides from my talk at PyCon Ireland 2015
This document contains contact information and a summary of the skills and experience of Jennifer Doty. It lists her education as a Bachelor of Architecture from Illinois Institute of Technology with a 3.7 GPA. It provides details of her various internships and roles in architecture, urban planning, and research organizations where she gained experience in areas like BIM/CAD modeling, graphics creation, project documentation, and content management. References are also provided.
6 Reasons Technophiles Make Better Graphic DesignersDesignMantic
Technophilia refers to a strong enthusiasm for technology, especially new technologies such. Technophiles are individuals who are enthusiastic about technology and view technology's interaction with society as creating an utopia, cyber or otherwise, and a strong indescribable futuristic feeling.
We're all technophiles in that matter. But Graphic Designers, who are often needed to juggle between different tasks, are bound to love tech. Technology gives them the power to take on and succeed as they grow more and more into design. Here are 6 compelling reasons why technophiles make better designers.
Enjoy the read and do tell us if you agree!
http://www.ianlivingstone.ca/2015/11/17/enabling-autonomy/
The drastic increase in the importance of knowledge workers has turned traditional management structures and philosophy upside down. Previously, all of the information and authority was centralized in management and workers simply operated according to some proscribed procedure with limited ability to make their own decisions. However, the rise of the knowledge worker such as developers, designers, and product managers has thrown these structures out the window as they've proven unable to deliver incredible products and technology.
The new name of the game is enabling teams to operate autonomously and build towards a vision that is seeded by the leadership but authored by the team. How do we enable teams to operate autonomously while ensuring that they are held accountable? How does this change as organizations grow? Why does this matter and what are the results?
The 3 Key Barriers Keeping Companies from Deploying Data Products Dataiku
Getting from raw data to deploying data-driven solutions requires technology, data, and people. All of which exist. So why aren’t we seeing more truly data-driven companies: what's missing and why? During Strata Hadoop World Singapore 2015, Pauline Brown, Director of Marketing at Dataiku, explains how lack of collaboration is what is keeping companies from building and deploying data products effectively. Learn more about Dataiku and Data Science Studio: www.dataiku.com
Catalina Butnaru, London Ambassador at City.ai - Working with AI - future-pro...Codiax
The document proposes an AI-compatible process for developing software that focuses on defining problems, researching use cases, and mapping skills upfront, then auditing data for quality and privacy before running parallel training experiments, benchmarking performance, and implementing live training with ongoing feedback in an agile manner overseen by roles like a data owner, coordinator, and ethical board.
This document discusses trends in artificial intelligence (AI) funding and applications. It notes that AI funding has grown unprecedentedly, reaching over $15 billion in 2017. Popular investment areas for startups include healthcare diagnostics, automation, cybersecurity, and autonomous vehicles. Corporations are also increasingly investing in AI, focusing on platforms, business process automation, and understanding customers. The document provides recommendations for companies looking to adopt AI, such as starting small with cost savings projects, creating a product team using agile practices, and identifying key data needs.
From Lab to Factory: Or how to turn data into valuePeadar Coyle
We've all heard of 'big data' or data science, but how do we convert these trends into actual business value. I share case studies, and technology tips and talk about the challenges of the data science process. This is all based on two years of in-the-field research of deploying models, and going from prototypes to production.
These are slides from my talk at PyCon Ireland 2015
From Lab to Factory: Creating value with dataPeadar Coyle
The document discusses lessons learned in developing data products and deploying data science projects from lab to production. It covers challenges such as integrating with other teams, managing stakeholders, monitoring models in production, and ensuring projects are supported by the necessary infrastructure, tools, and culture. Recommendations include focusing on clean, small data problems first, adopting DevOps practices like monitoring and pipelines, and improving communication between data scientists and other roles.
Design + Development + Devices: The 3 Ds of 3-D ProductsCourtney Hemphill
Creating products today requires considering more than just screens and a basic web stack. Hear how the best companies are pushing the boundaries by leveraging a variety of technologies and collecting data across a variety of channels and devices. Understand how designers and developers can work together to efficiently craft relevant and engaging user experiences that take advantage of all the opportunities that are available in our increasingly complex technical landscape. This talk will outline techniques that establish a weekly cadence of experiment driven development. These are specific exercises and tools that can be used to iteratively design, build, deploy and test physical products in real world scenarios. This process allows teams to move fast to release products in weeks versus months and use those releases to better inform future design and development sprints.
Customer Centricity - It Starts With 1-1 User ResearchPRWD
In this talk, PRWD CEO Paul Rouke shares why speaking to 10 customers 1-1 in user research is the start of becoming a customer-centric organisation. The talk explains why 1-1 user research uncovers the truth, why 1-1 user research uncovers game-changing business ideas, and how video clips from user research can change decision-makers mindsets.
The document discusses the optimal use of prototyping in the product design process, outlining different prototyping techniques including traditional prototyping methods like woodworking, casting, and thermoforming as well as rapid prototyping. It emphasizes using prototyping early and throughout the design process to explore concepts and gather feedback, and matching the appropriate prototyping technique to the design stage and purpose. Examples are provided of quick, functional prototypes created for an ventilation system and assistive technology project.
THE SIX TRAITS OF IT-DRIVEN BUSINESS INNOVATORS- Does Your Organization Share...Aneel Mitra
Leading IT executives react to a Harvard Business Review survey outlining the six behaviors of companies with innovative IT. Does your organization share any of these characteristics?
Lean Design Research - Why There’s No Excuse Wasting Money on Bad Products A...Dialexa
In the age of the consumer and consumerism of IT, there’s no question that design thinking is critical to new product success. The importance of design thinking has become so clear that there has been a surge in demand for design at the executive table.
http://by.dialexa.com/lean-design-research-no-excuse-wasting-money-on-bad-products
1. The document discusses various prototyping techniques that can be used at different stages of the design process to improve chances of success, including traditional prototyping methods like cardboard mockups, workshops, and casting as well as rapid prototyping techniques like 3D printing, laser sintering, and polyjet printing.
2. It notes that while CAD, simulation, and visualization tools are useful later in the design process, they do not help in the early conceptual stages or allow for divergent ideas, so prototyping needs to be incorporated throughout the entire process.
3. The document advocates an approach called "design by prototyping" where prototypes of varying fidelity are created at each stage to explore
How Great PMs Can Come From Anywhere by ICX Media CPOProduct School
Main takeaways:
- 5 Different Personalities of Product Managers
- Product Managers Can Come from Many Different Functions
- Shared Traits of Successful Product Managers
Building Data Teams:data scientists, engineers, and product managers working together to create innovative data products by Anu Tewary Director Of Product Management at Intuit.
Talent42 2017: Building the Best Recruiting Tech Stack - Nick Mailey and Will...Talent42
This document discusses reimagining technology to enable talent acquisition. It outlines moving from significant complexity and tribal knowledge to flexibility and speed by supporting core business processes through a single talent acquisition system. The document recommends following four steps: 1) Know the problem you're solving 2) Identify the customer 3) Think outside the box 4) Experiment once you have a foundation. It also lists emerging technologies and trends in talent acquisition to watch.
What Forces Drive Towards Product Innovation by Realeyes PMProduct School
Main takeaways:
- What is the JOB that your product is hired for? Why does it matter?
- How Product Managers personally benefit from structured communication of customer needs
- Real market example of what we discovered while using these approaches
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.
Ai revolution for human capital for individuals 2nd feb 2018Liew Wei Da Andrew
The world is crazy about Artificial Intelligence (AI). Whether you are for it or against it – there’s no doubt that AI will change our lives. To educate ourselves about AI, some questions which we should ask are:
Is AI an assistive tool or a substitutive tool?
What are the life hacks for an individual to adapt to this new world?
What are the possibilities for the individuals in the world of AI?
This talk will reveal facts and possibilities about AI to create hope and awareness for the society that we live in. There's no time for ignorance. Have the courage to find out and learn.
Emerging Tech as an Opportunity / Melanie Dreser & Giuseppe de CesareService Experience Camp
This document discusses emerging technologies and their opportunities and challenges from an ethical perspective. It provides design principles for companies to consider, such as making sure technologies respect people as ends and not just means, requiring accountability, and considering unintended consequences. It also discusses the importance of diversity, accessibility, and having the right company culture to ensure technologies are developed and applied responsibly and for the benefit of humanity. Continuous learning, prototyping, and putting users first are emphasized as important for responsible development of technologies.
This document outlines the schedule and topics for an advanced entrepreneurship course. It includes the date, time, location and topic for 10 sessions. Session #8 will focus on "Financing a Start-up" and will feature a guest speaker, Sebastian Bärhold, Co-founder & CFO of Amiando GmbH, to discuss insights on financing startup growth. The document also provides guidance on preparing final presentations, including expected content and likely questions from the jury members, who include an investor and technology entrepreneur.
How to build a data science team 20115.03.13v6Zhihao Lin
Teralytics provides real-time insights into human behavior globally using data from 350 million profiles and 180 billion daily events. They have built a data science team in Singapore that develops one of their three products deployed worldwide. The presentation outlines how to build an effective data science team, including finding team members through diverse sources, evaluating them through a multi-stage interview process, convincing them to join by emphasizing the work, data, and team environment, and getting the team working cohesively through collaborative projects with clear goals and deadlines.
The Marketing Technology Myth - Connecting Systems and ExperiencesMarTech Conference
From the MarTech Conference in San Francisco, California, March 31-April 1, 2015. SESSION: The Marketing Technology Myth: Connecting Systems & Experiences - Given by Jeff Cram, @jeffcram - ISITE Design, Chief Strategy Officer
Tim Cermak is a senior portfolio advisor at Advisicon who discusses the buzz word "SharePoint". SharePoint is a platform for enterprise collaboration that allows workers to better share information. It can provide 80% of business needs out of the box with no customization. Successful companies will use SharePoint to drive growth, enhance efficiency, and reduce costs by laying the foundation for future innovation through a unified collaboration platform.
PredictLeads - AI Platform for Market Intelligence - 2017 DeckMiha
PredictLeads helps corporate development & sales teams spend less time researching their prospective customers, partners or competitors. Using proprietary technology we can do it cost effective & at scale.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
From Lab to Factory: Creating value with dataPeadar Coyle
The document discusses lessons learned in developing data products and deploying data science projects from lab to production. It covers challenges such as integrating with other teams, managing stakeholders, monitoring models in production, and ensuring projects are supported by the necessary infrastructure, tools, and culture. Recommendations include focusing on clean, small data problems first, adopting DevOps practices like monitoring and pipelines, and improving communication between data scientists and other roles.
Design + Development + Devices: The 3 Ds of 3-D ProductsCourtney Hemphill
Creating products today requires considering more than just screens and a basic web stack. Hear how the best companies are pushing the boundaries by leveraging a variety of technologies and collecting data across a variety of channels and devices. Understand how designers and developers can work together to efficiently craft relevant and engaging user experiences that take advantage of all the opportunities that are available in our increasingly complex technical landscape. This talk will outline techniques that establish a weekly cadence of experiment driven development. These are specific exercises and tools that can be used to iteratively design, build, deploy and test physical products in real world scenarios. This process allows teams to move fast to release products in weeks versus months and use those releases to better inform future design and development sprints.
Customer Centricity - It Starts With 1-1 User ResearchPRWD
In this talk, PRWD CEO Paul Rouke shares why speaking to 10 customers 1-1 in user research is the start of becoming a customer-centric organisation. The talk explains why 1-1 user research uncovers the truth, why 1-1 user research uncovers game-changing business ideas, and how video clips from user research can change decision-makers mindsets.
The document discusses the optimal use of prototyping in the product design process, outlining different prototyping techniques including traditional prototyping methods like woodworking, casting, and thermoforming as well as rapid prototyping. It emphasizes using prototyping early and throughout the design process to explore concepts and gather feedback, and matching the appropriate prototyping technique to the design stage and purpose. Examples are provided of quick, functional prototypes created for an ventilation system and assistive technology project.
THE SIX TRAITS OF IT-DRIVEN BUSINESS INNOVATORS- Does Your Organization Share...Aneel Mitra
Leading IT executives react to a Harvard Business Review survey outlining the six behaviors of companies with innovative IT. Does your organization share any of these characteristics?
Lean Design Research - Why There’s No Excuse Wasting Money on Bad Products A...Dialexa
In the age of the consumer and consumerism of IT, there’s no question that design thinking is critical to new product success. The importance of design thinking has become so clear that there has been a surge in demand for design at the executive table.
http://by.dialexa.com/lean-design-research-no-excuse-wasting-money-on-bad-products
1. The document discusses various prototyping techniques that can be used at different stages of the design process to improve chances of success, including traditional prototyping methods like cardboard mockups, workshops, and casting as well as rapid prototyping techniques like 3D printing, laser sintering, and polyjet printing.
2. It notes that while CAD, simulation, and visualization tools are useful later in the design process, they do not help in the early conceptual stages or allow for divergent ideas, so prototyping needs to be incorporated throughout the entire process.
3. The document advocates an approach called "design by prototyping" where prototypes of varying fidelity are created at each stage to explore
How Great PMs Can Come From Anywhere by ICX Media CPOProduct School
Main takeaways:
- 5 Different Personalities of Product Managers
- Product Managers Can Come from Many Different Functions
- Shared Traits of Successful Product Managers
Building Data Teams:data scientists, engineers, and product managers working together to create innovative data products by Anu Tewary Director Of Product Management at Intuit.
Talent42 2017: Building the Best Recruiting Tech Stack - Nick Mailey and Will...Talent42
This document discusses reimagining technology to enable talent acquisition. It outlines moving from significant complexity and tribal knowledge to flexibility and speed by supporting core business processes through a single talent acquisition system. The document recommends following four steps: 1) Know the problem you're solving 2) Identify the customer 3) Think outside the box 4) Experiment once you have a foundation. It also lists emerging technologies and trends in talent acquisition to watch.
What Forces Drive Towards Product Innovation by Realeyes PMProduct School
Main takeaways:
- What is the JOB that your product is hired for? Why does it matter?
- How Product Managers personally benefit from structured communication of customer needs
- Real market example of what we discovered while using these approaches
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.
Ai revolution for human capital for individuals 2nd feb 2018Liew Wei Da Andrew
The world is crazy about Artificial Intelligence (AI). Whether you are for it or against it – there’s no doubt that AI will change our lives. To educate ourselves about AI, some questions which we should ask are:
Is AI an assistive tool or a substitutive tool?
What are the life hacks for an individual to adapt to this new world?
What are the possibilities for the individuals in the world of AI?
This talk will reveal facts and possibilities about AI to create hope and awareness for the society that we live in. There's no time for ignorance. Have the courage to find out and learn.
Emerging Tech as an Opportunity / Melanie Dreser & Giuseppe de CesareService Experience Camp
This document discusses emerging technologies and their opportunities and challenges from an ethical perspective. It provides design principles for companies to consider, such as making sure technologies respect people as ends and not just means, requiring accountability, and considering unintended consequences. It also discusses the importance of diversity, accessibility, and having the right company culture to ensure technologies are developed and applied responsibly and for the benefit of humanity. Continuous learning, prototyping, and putting users first are emphasized as important for responsible development of technologies.
This document outlines the schedule and topics for an advanced entrepreneurship course. It includes the date, time, location and topic for 10 sessions. Session #8 will focus on "Financing a Start-up" and will feature a guest speaker, Sebastian Bärhold, Co-founder & CFO of Amiando GmbH, to discuss insights on financing startup growth. The document also provides guidance on preparing final presentations, including expected content and likely questions from the jury members, who include an investor and technology entrepreneur.
How to build a data science team 20115.03.13v6Zhihao Lin
Teralytics provides real-time insights into human behavior globally using data from 350 million profiles and 180 billion daily events. They have built a data science team in Singapore that develops one of their three products deployed worldwide. The presentation outlines how to build an effective data science team, including finding team members through diverse sources, evaluating them through a multi-stage interview process, convincing them to join by emphasizing the work, data, and team environment, and getting the team working cohesively through collaborative projects with clear goals and deadlines.
The Marketing Technology Myth - Connecting Systems and ExperiencesMarTech Conference
From the MarTech Conference in San Francisco, California, March 31-April 1, 2015. SESSION: The Marketing Technology Myth: Connecting Systems & Experiences - Given by Jeff Cram, @jeffcram - ISITE Design, Chief Strategy Officer
Tim Cermak is a senior portfolio advisor at Advisicon who discusses the buzz word "SharePoint". SharePoint is a platform for enterprise collaboration that allows workers to better share information. It can provide 80% of business needs out of the box with no customization. Successful companies will use SharePoint to drive growth, enhance efficiency, and reduce costs by laying the foundation for future innovation through a unified collaboration platform.
PredictLeads - AI Platform for Market Intelligence - 2017 DeckMiha
PredictLeads helps corporate development & sales teams spend less time researching their prospective customers, partners or competitors. Using proprietary technology we can do it cost effective & at scale.
Similar to Informed by data driven by empathy - Pedro Marques (20)
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Marlon Dumas
This webinar discusses the limitations of traditional approaches for business process simulation based on had-crafted model with restrictive assumptions. It shows how process mining techniques can be assembled together to discover high-fidelity digital twins of end-to-end processes from event data.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
06-20-2024-AI Camp Meetup-Unstructured Data and Vector DatabasesTimothy Spann
Tech Talk: Unstructured Data and Vector Databases
Speaker: Tim Spann (Zilliz)
Abstract: In this session, I will discuss the unstructured data and the world of vector databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture. Wrapping up with an overview of Milvus.
Introduction
Unstructured data, vector databases, traditional databases, similarity search
Vectors
Where, What, How, Why Vectors? We’ll cover a Vector Database Architecture
Introducing Milvus
What drives Milvus' Emergence as the most widely adopted vector database
Hi Unstructured Data Friends!
I hope this video had all the unstructured data processing, AI and Vector Database demo you needed for now. If not, there’s a ton more linked below.
My source code is available here
https://github.com/tspannhw/
Let me know in the comments if you liked what you saw, how I can improve and what should I show next? Thanks, hope to see you soon at a Meetup in Princeton, Philadelphia, New York City or here in the Youtube Matrix.
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/141-10June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
https://www.meetup.com/unstructured-data-meetup-new-york/events/301383476/?slug=unstructured-data-meetup-new-york&eventId=301383476
https://www.aicamp.ai/event/eventdetails/W2024062014
06-18-2024-Princeton Meetup-Introduction to MilvusTimothy Spann
06-18-2024-Princeton Meetup-Introduction to Milvus
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://github.com/milvus-io/milvus
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/142-17June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
Expand LLMs' knowledge by incorporating external data sources into LLMs and your AI applications.
2. What is Booking.com?
It’s basically just a yellow box
With a
But its really just about the yellow box
Search field
Stuart Frisby
And some Other stuff
Director of design
HQ in Amsterdam
15k staff
1.4m rooms per day
1.3m properties
150+ designers
We test everything
@stuartfrisby Building a test culture https://youtu.be/_sx5LV23hIE
3. É designer uai
É design não é?
7.3
Better than No Man’s Sky
02:47
Who is Pedro Marques
- The Wife®
- Dad - IGN
- Mom on WhatsApp
14. Data Driven
You might end up optmizing the wrong thing
[…] not everything is an
optimization problem.
http://andrewchen.co/know-the-difference-between-data-informed-and-versus-data-driven/
- Andrew Chen
15. Data is one of the tools
It helps filling the gaps
16. Data Informed
You can tweak things here and there but wel...
http://andrewchen.co/know-the-difference-between-data-informed-and-versus-data-driven/
Quantitative
A little instinctQualitative