The document discusses Superfluid Labs, a data analytics firm that helps enterprises use data science and machine learning. It outlines their mission and vision, provides examples of case studies where they helped clients with predictive modeling and analytics. The presentation then covers developing a data science strategy, including building a data science team, prioritizing projects, and ensuring executive buy-in. Finally, it discusses the typical data science process and popular tools used.
How to set up an ai center of excellenceShranik Jain
Recently while exploring the field of "Artificial Intelligence in Organization context" able to create some content around "How Setting up an AI centre of excellence" can provide a leap in the dynamic environment of AI
#mba #organization #artificalintelligence
Career Development Programmes for Digital Health Practitioners (For Individuals)NUS-ISS
Specially designed for future Digital Health Practitioners, this session is for individuals (PMEs) who wish to know more about the Industry Transformation Programme landscape. We provide an overview of all NUS-ISS career development schemes and pathways.
We will also deep dive into the specific programme modules in detail.
Explainability for Natural Language ProcessingYunyao Li
Final deck for our popular tutorial on "Explainability for Natural Language Processing" at KDD'2021. See links below for downloadable version (with higher resolution) and recording of the live tutorial.
Title: Explainability for Natural Language Processing
Presenter: Marina Danilevsky, Shipi Dhanorkar, Yunyao Li and Lucian Popa and Kun Qian and Anbang Xu
Website: http://xainlp.github.io/
Recording: https://www.youtube.com/watch?v=PvKOSYGclPk&t=2s
Downloadable version with higher resolution: https://drive.google.com/file/d/1_gt_cS9nP9rcZOn4dcmxc2CErxrHW9CU/view?usp=sharing
@article{kdd2021xaitutorial,
title={Explainability for Natural Language Processing},
author= {Marina Danilevsky, Shipi Dhanorkar and Yunyao Li and Lucian Popa and Kun Qian and Anbang Xu},
journal={KDD},
year={2021}
}
Abstract:
This lecture-style tutorial, which mixes in an interactive literature browsing component, is intended for the many researchers and practitioners working with text data and on applications of natural language processing (NLP) in data science and knowledge discovery. The focus of the tutorial is on the issues of transparency and interpretability as they relate to building models for text and their applications to knowledge discovery. As black-box models have gained popularity for a broad range of tasks in recent years, both the research and industry communities have begun developing new techniques to render them more transparent and interpretable.Reporting from an interdisciplinary team of social science, human-computer interaction (HCI), and NLP/knowledge management researchers, our tutorial has two components: an introduction to explainable AI (XAI) in the NLP domain and a review of the state-of-the-art research; and findings from a qualitative interview study of individuals working on real-world NLP projects as they are applied to various knowledge extraction and discovery at a large, multinational technology and consulting corporation. The first component will introduce core concepts related to explainability inNLP. Then, we will discuss explainability for NLP tasks and reporton a systematic literature review of the state-of-the-art literaturein AI, NLP and HCI conferences. The second component reports on our qualitative interview study, which identifies practical challenges and concerns that arise in real-world development projects that require the modeling and understanding of text data.
How to set up an ai center of excellenceShranik Jain
Recently while exploring the field of "Artificial Intelligence in Organization context" able to create some content around "How Setting up an AI centre of excellence" can provide a leap in the dynamic environment of AI
#mba #organization #artificalintelligence
Career Development Programmes for Digital Health Practitioners (For Individuals)NUS-ISS
Specially designed for future Digital Health Practitioners, this session is for individuals (PMEs) who wish to know more about the Industry Transformation Programme landscape. We provide an overview of all NUS-ISS career development schemes and pathways.
We will also deep dive into the specific programme modules in detail.
Explainability for Natural Language ProcessingYunyao Li
Final deck for our popular tutorial on "Explainability for Natural Language Processing" at KDD'2021. See links below for downloadable version (with higher resolution) and recording of the live tutorial.
Title: Explainability for Natural Language Processing
Presenter: Marina Danilevsky, Shipi Dhanorkar, Yunyao Li and Lucian Popa and Kun Qian and Anbang Xu
Website: http://xainlp.github.io/
Recording: https://www.youtube.com/watch?v=PvKOSYGclPk&t=2s
Downloadable version with higher resolution: https://drive.google.com/file/d/1_gt_cS9nP9rcZOn4dcmxc2CErxrHW9CU/view?usp=sharing
@article{kdd2021xaitutorial,
title={Explainability for Natural Language Processing},
author= {Marina Danilevsky, Shipi Dhanorkar and Yunyao Li and Lucian Popa and Kun Qian and Anbang Xu},
journal={KDD},
year={2021}
}
Abstract:
This lecture-style tutorial, which mixes in an interactive literature browsing component, is intended for the many researchers and practitioners working with text data and on applications of natural language processing (NLP) in data science and knowledge discovery. The focus of the tutorial is on the issues of transparency and interpretability as they relate to building models for text and their applications to knowledge discovery. As black-box models have gained popularity for a broad range of tasks in recent years, both the research and industry communities have begun developing new techniques to render them more transparent and interpretable.Reporting from an interdisciplinary team of social science, human-computer interaction (HCI), and NLP/knowledge management researchers, our tutorial has two components: an introduction to explainable AI (XAI) in the NLP domain and a review of the state-of-the-art research; and findings from a qualitative interview study of individuals working on real-world NLP projects as they are applied to various knowledge extraction and discovery at a large, multinational technology and consulting corporation. The first component will introduce core concepts related to explainability inNLP. Then, we will discuss explainability for NLP tasks and reporton a systematic literature review of the state-of-the-art literaturein AI, NLP and HCI conferences. The second component reports on our qualitative interview study, which identifies practical challenges and concerns that arise in real-world development projects that require the modeling and understanding of text data.
Developer Velocity Series in association with Quest
DevOps: A compound of development (Dev) and operations (Ops), DevOps is the union of people, process, and technology to continually provide value to customers.
DataOps: DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics.
MLOps: MLOps […] enables data science and IT teams to collaborate and increase the pace of model development and deployment via monitoring, validation, and governance of machine learning models.
DevSecOps: DevSecOps automatically bakes in security at every phase of the software development lifecycle, enabling development of secure software at the speed of Agile and DevOps.
ChatOps: ChatOps is a collaboration model that connects people, tools, process, and automation into a transparent workflow. This flow connects the work needed, the work happening, and the work done in a persistent location staffed by the people, bots, and related tools.
NoOps: NoOps is the idea that the software environment can be so completely automated that there’s no need for an operations team to manage it.
GitOps: GitOps is a way of implementing Continuous Deployment for cloud native applications. It focuses on a developer-centric experience when operating infrastructure, by using tools developers are already familiar with, including Git and Continuous Deployment tools.
Developer Velocity is the Grand Unified Theory
Developer velocity: The ability to drive transformative business performance through software development
Top DVI companies are stronger financially
- 5x compound annual growth rate
- 60% more shareholder returns
- 20% higher operating margins
>Companies in the top quartile of the Developer Velocity Index (DVI) outperform others in the market by four to five times. Top-quartile companies also have 60 percent higher total shareholder returns and 20 percent higher operating margins.
Critical areas of focus
- People
Product management
Product management function
Product telemetry
Culture
Psychological safety
Collaboration and knowledge sharing
Continuous improvement culture
Talent management
Incentives
Capability building
- Processes
Working practices
Compliance practices
Security practices
Organisational enablement
Autonomous scoped teams
Dependency management
Culture
Continuous improvement
Talent management
Recruiting
Team health management
- Tooling
Planning tools
Collaboration tools
Development tools
DevOps tools
Cloud
Video at: https://www.quest.com/event/steph-lockes-developer-velocity-series-8148798/
Your Data Science Journey - Setting Up Analytics Units From ScratchNUS-ISS
Digitalisation has impacted the value of different skills in many industries. The search for digital talent to implement all kinds of enterprises' digital business strategies have been a constant challenge and a delicate balance between cost and value. Since Artificial Intelligence and Automation are certain to play important roles in our workforce, every organisation is looking at how to fully optimise the potential of human and machines working together to unleash new value for the businesses. This talk will cover topics on impact of Digitalisation on Skills, leverage on the Diversity in Digital Talent Pools, Job Redefined To Unleash New Value and redesign Talent Strategy for Digital Age.
Anil Kaul, CEO and Co-Founder, AbsolutData delivered a session on institutionalizing Big Data analytics for organizations, at the Big Data Innovation Summit, London on 1st May, 2013.
AbsolutData is a global leader in applying analytics to drive sales and increase profits for its customers. AbsolutData has built strong expertise and traction with Fortune 1000 companies across 40 countries. We specialize in big data, high end business analytics, predictive modeling, research, reporting, social media analytics and data management services. AbsolutData delivers world class analytics solutions by combining their expertise in industry domains, analytical techniques and sophisticated tools.
Visit us here : www.absolutdata.com
NC State's Poole College of Management presents the misconceptions of innovation's role in business and the benefits resulting from the effective implementation of innovation into your company.
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.
Devoteam itsmf 2021 - from business automation to continuous value-driven i...itSMF Belgium
The race for enterprise business process digitalization is raging. IT is often left behind as enterprise budgets for innovation are shifting towards business teams.
During this session, we will present the challenges and our field-tested approaches to catch-up and how to take this opportunity to create new app factories. All the while using low-code and RPA platforms.
You will discover how to capture business demands, and create an operating model for your IT department to stay in control of the applications being deployed, while bringing value at speed.
Research firm Gartner coined the term augmented analytics in its 2017 Hype Cycle for Emerging Technologies report and claimed it would be the “future of data analytics.”
Augmented analytics is set to become the dominant driver of new purchases and projects in the analytics and business intelligence space.
Join VIC Regional Manager, Andy Painter as you explore what this means for your organisation and how best to jump onboard this rapidly expanding trend.
Ironside's VP of Strategy & Innovation, Greg Bonnette, delivered a presentation on "How to Build a Winning Strategy for Data & Analytics" to provide a framework for data-driven decision making.
The customer journey could essentially be divided into 7 elements. We’ll touch upon the issue of ‘Privacy’ and how one balance social and commercial value. Practical examples of
customer analytics at its best will be discussed as well as the importance of the eco-system.
[AI Webinar Series P1] - How Advanced Text Analytics Can Increase the Operati...JK Tech
Digitization is considered as the next step-change that will have a bigger impact on businesses than even the internet. To win in the digital journey, companies must act now, or be left behind wondering what happened!
In this webinar series, JKT Smart Analytics demonstrates how they empower their customers to create maximum business value out of this eminent Digital data explosion through digital business empowerment by leveraging the digitization to increase their top-line revenue – customer experience, optimize the bottom-line costs – operational efficiency, enhancing the safety factor and reinventing the business process in line with the changing world.
This webinar is focused on how our AI-based text analytics solutions – First, JKT Social Media Radar; a SaaS-based AI NLP Platform, helping organizations to gain insights on market and customer perceptions on their brands, products & services. Secondly, Sales Promotion Recommendation Engine helps customers to enhance their top-line growth and streamline the bottom-line costs.
KEY TAKEAWAYS:
1) How should a business plan their journey through the Digital data revolution?
2) How can a company make use of digital data to create effective data strategies for the increased outcome(s)?
3) How IT practitioners can catalyst the digital data mining journey and attract business adoption?
4) JKT Social Media Radar solution – What, Why, Supporting Business applications, and more.
5) How can companies reduce operational costs by automating human effort-intensive tasks using cognitive Analytics?
How to build an it transformation roadmapInnesGerrard
An estimated 80 percent of #businesses will need to transform their current IT efforts to keep up with new business expectations and technological developments. These include investments such as cloud computing, IoT and BigData projects.
Developer Velocity Series in association with Quest
DevOps: A compound of development (Dev) and operations (Ops), DevOps is the union of people, process, and technology to continually provide value to customers.
DataOps: DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics.
MLOps: MLOps […] enables data science and IT teams to collaborate and increase the pace of model development and deployment via monitoring, validation, and governance of machine learning models.
DevSecOps: DevSecOps automatically bakes in security at every phase of the software development lifecycle, enabling development of secure software at the speed of Agile and DevOps.
ChatOps: ChatOps is a collaboration model that connects people, tools, process, and automation into a transparent workflow. This flow connects the work needed, the work happening, and the work done in a persistent location staffed by the people, bots, and related tools.
NoOps: NoOps is the idea that the software environment can be so completely automated that there’s no need for an operations team to manage it.
GitOps: GitOps is a way of implementing Continuous Deployment for cloud native applications. It focuses on a developer-centric experience when operating infrastructure, by using tools developers are already familiar with, including Git and Continuous Deployment tools.
Developer Velocity is the Grand Unified Theory
Developer velocity: The ability to drive transformative business performance through software development
Top DVI companies are stronger financially
- 5x compound annual growth rate
- 60% more shareholder returns
- 20% higher operating margins
>Companies in the top quartile of the Developer Velocity Index (DVI) outperform others in the market by four to five times. Top-quartile companies also have 60 percent higher total shareholder returns and 20 percent higher operating margins.
Critical areas of focus
- People
Product management
Product management function
Product telemetry
Culture
Psychological safety
Collaboration and knowledge sharing
Continuous improvement culture
Talent management
Incentives
Capability building
- Processes
Working practices
Compliance practices
Security practices
Organisational enablement
Autonomous scoped teams
Dependency management
Culture
Continuous improvement
Talent management
Recruiting
Team health management
- Tooling
Planning tools
Collaboration tools
Development tools
DevOps tools
Cloud
Video at: https://www.quest.com/event/steph-lockes-developer-velocity-series-8148798/
Your Data Science Journey - Setting Up Analytics Units From ScratchNUS-ISS
Digitalisation has impacted the value of different skills in many industries. The search for digital talent to implement all kinds of enterprises' digital business strategies have been a constant challenge and a delicate balance between cost and value. Since Artificial Intelligence and Automation are certain to play important roles in our workforce, every organisation is looking at how to fully optimise the potential of human and machines working together to unleash new value for the businesses. This talk will cover topics on impact of Digitalisation on Skills, leverage on the Diversity in Digital Talent Pools, Job Redefined To Unleash New Value and redesign Talent Strategy for Digital Age.
Anil Kaul, CEO and Co-Founder, AbsolutData delivered a session on institutionalizing Big Data analytics for organizations, at the Big Data Innovation Summit, London on 1st May, 2013.
AbsolutData is a global leader in applying analytics to drive sales and increase profits for its customers. AbsolutData has built strong expertise and traction with Fortune 1000 companies across 40 countries. We specialize in big data, high end business analytics, predictive modeling, research, reporting, social media analytics and data management services. AbsolutData delivers world class analytics solutions by combining their expertise in industry domains, analytical techniques and sophisticated tools.
Visit us here : www.absolutdata.com
NC State's Poole College of Management presents the misconceptions of innovation's role in business and the benefits resulting from the effective implementation of innovation into your company.
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.
Devoteam itsmf 2021 - from business automation to continuous value-driven i...itSMF Belgium
The race for enterprise business process digitalization is raging. IT is often left behind as enterprise budgets for innovation are shifting towards business teams.
During this session, we will present the challenges and our field-tested approaches to catch-up and how to take this opportunity to create new app factories. All the while using low-code and RPA platforms.
You will discover how to capture business demands, and create an operating model for your IT department to stay in control of the applications being deployed, while bringing value at speed.
Research firm Gartner coined the term augmented analytics in its 2017 Hype Cycle for Emerging Technologies report and claimed it would be the “future of data analytics.”
Augmented analytics is set to become the dominant driver of new purchases and projects in the analytics and business intelligence space.
Join VIC Regional Manager, Andy Painter as you explore what this means for your organisation and how best to jump onboard this rapidly expanding trend.
Ironside's VP of Strategy & Innovation, Greg Bonnette, delivered a presentation on "How to Build a Winning Strategy for Data & Analytics" to provide a framework for data-driven decision making.
The customer journey could essentially be divided into 7 elements. We’ll touch upon the issue of ‘Privacy’ and how one balance social and commercial value. Practical examples of
customer analytics at its best will be discussed as well as the importance of the eco-system.
[AI Webinar Series P1] - How Advanced Text Analytics Can Increase the Operati...JK Tech
Digitization is considered as the next step-change that will have a bigger impact on businesses than even the internet. To win in the digital journey, companies must act now, or be left behind wondering what happened!
In this webinar series, JKT Smart Analytics demonstrates how they empower their customers to create maximum business value out of this eminent Digital data explosion through digital business empowerment by leveraging the digitization to increase their top-line revenue – customer experience, optimize the bottom-line costs – operational efficiency, enhancing the safety factor and reinventing the business process in line with the changing world.
This webinar is focused on how our AI-based text analytics solutions – First, JKT Social Media Radar; a SaaS-based AI NLP Platform, helping organizations to gain insights on market and customer perceptions on their brands, products & services. Secondly, Sales Promotion Recommendation Engine helps customers to enhance their top-line growth and streamline the bottom-line costs.
KEY TAKEAWAYS:
1) How should a business plan their journey through the Digital data revolution?
2) How can a company make use of digital data to create effective data strategies for the increased outcome(s)?
3) How IT practitioners can catalyst the digital data mining journey and attract business adoption?
4) JKT Social Media Radar solution – What, Why, Supporting Business applications, and more.
5) How can companies reduce operational costs by automating human effort-intensive tasks using cognitive Analytics?
How to build an it transformation roadmapInnesGerrard
An estimated 80 percent of #businesses will need to transform their current IT efforts to keep up with new business expectations and technological developments. These include investments such as cloud computing, IoT and BigData projects.
Data Strategy - Executive MBA Class, IE Business SchoolGam Dias
For today's enterprise Data is now very much a corporate asset, vital to delivering products and services efficiently and cost effectively. There are few organizations that can survive without harnessing data in some way.
Viewed as a strategic asset, data can be a source of new internal efficiencies, improved competitive advantage or a source of entirely new products that can be targeted at your existing or new customers.
This slide deck contains the highlights of a one day course on Data Strategy taught as part of the Executive MBA Program at IE Business School in Madrid.
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
Big Data, Big Thinking: Untapped OpportunitiesSAP Technology
In this webinar factsheet, SAP’s Rohit Nagarajan and Suni Verma from Ernst & Young explore Big Data in India, adoption patterns across the globe, and how you can embark on your own Big Data journey.
Analytics plays a critical role in supporting strategic business initiatives. Despite the apparent value of providing the data infrastructure for these initiatives, many executives question the economic feasibility of business intelligence and analytics. This requires information professionals to calculate and present the business value in terms business executives can understand.
Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements.
This session provides a framework to help IT professionals research, measure, and present the economic value of a proposed or existing analytics initiative. The session will provide practical advice about how to calculate ROI, the formulas in use, and how to collect necessary information.
Business Intelligence, Data Analytics, and AIJohnny Jepp
Data is the new currency. In this session, best practices on data collection, management dashboards, and used cases will be shared using Azure Data Services.
Video accessible at bit.ly/APACSummitOnDemand
Shwetank Sheel
Chief Executive Officer
Just Analytics
Poonam Sampat
Cloud Solution Architect - Data & AI
Microsoft Asia Pacific
The Fast Fish Forum is an opportunity for challengers of convention and drivers of progress to come together for the benefit of South African business and society. The forum consists of purposeful, committed and open-minded people across industries, organisations and roles who collaborate and learn together; creating a critical mass that drives innovative change in our country.
At the second event, held at the BSG offices on 16 November 2016, we discussed two highly topical subjects:
1. Enhancing customer value using big data and actionable insights.
2. Driving innovation through customer insights.
To find out more and join the conversation follow us @FastFishForum and http://bit.ly/fastfishforum.
Building Your Own Modernization Roadmap - Emmanuel TzinevrakisFresche Solutions
Building Your Own Modernization Roadmap, presented by Emmanuel Tzinevrakis, VP Services at Fresche.
Presentation topics:
■ Value of Your IBM i
■ Business Value Creation
■ Modernization Strategies
■ Planning Your Modernization
■ How to Get Started
A Digital Enterprise is one that leverages customer, contextual and enterprise data and use new-age technologies to drive exponential business impact. To facilitate digital transformation, enterprises are increasingly setting up Digital Labs/Hubs in geographies with rich product capabilities, such as the Bay Area (US) and Bangalore (India).
This is the presentation I shared at the SAP Influencer Summit. The presentation discusses how we are seeing companies in APJ utilize our BI/Analytics solutions.
Developing a Modernization Strategy: Evaluating the Options by Chris KoppeFresche Solutions
Chris Koppe, VP of Corporate Strategy at Fresche Legacy presented Developing a Modernization Strategy: Evaluating the Options during iBelieve 2015.
This presentation covers:
- Modernization strategies
- Establishing goals and objectives
- Strategy definition
- Planning
- Getting funding and support
How Human Resources processes are improved by Advanced Analytics and Big DataCapgemini
Internal mobility, recruitment, career development, life balance : Big Data and Analytics provide new insights for HR processes. Discover the innovative solution developed by Capgemini and IBM to support companies of all sizes in the optimal management
of these challenges. This new approach is leveraged by natural language processing, machine learning and data visualization. The solution helps executives to streamline HR processes, save time and reduce costs. Presented at IBM Insight 2015.
Maximising likelihood of success: Applying Product Management to AI/ML/DS pr...Kevin Wong
According to stats, 85% of Artificial Intelligence (AI) / Machine Learning (ML) / data science (DS) projects fail, which hinders companies' appetite in investing in AI/ML/DS, and holds back data scientists from getting the recognition they deserve. In this talk dated 15 June 2019, Kevin Wong presented a gentle introduction on how he applied a re-invented Product Management approach to AI projects, in order to maximise their likelihood of success.
Zinnov examines the growing trend of enterprises setting up digital labs to drive the next leg of their digital journey. Geographies with rich product development capabilities and a talent pool with key skills are emerging as hot spots for the establishment of innovative digital labs
The modern enterprise is becoming an increasingly automated environment: technological advancements in AI, Machine Learning and RPA are allowing organisations to strip out layers of inefficiency, optimise process and enhance productivity. Right across the enterprise, operations are changing in line with new automation tools, from low-level administrative tasks to self-regulating Industrial IoT systems and customer service chatbots.
This conference will contextualise the role of intelligent automation within the enterprise, looking at how the increasing sophistication of AI, RPA and IoT technologies are transforming operations. The conference is geared towards senior IT and digital leaders, providing an insightful peer-led environment and a crucial forum for knowledge exchange, engagement and high-level networking
Similar to Scaling Your Enterprise With Data Science (20)
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdf
Scaling Your Enterprise With Data Science
1. 1
WIN WITH DATA
Scaling your Enterprise with Data Science
Germany | Ghana | Kenya
Superfluid Labs Limited
@SuperFluidLabs | www.superfluid.io | info@superfluid.io
Location: Germany, Ghana and Kenya
SUPERFLUID LABS LTD | Copyright (c) |
2. Speakers
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Timothy Kotin
Co-Founder & CEO
Superfluid Labs
Gilbert Langat
Data Scientist
Superfluid Labs
Yvette Titriku
Data Scientist
Superfluid Labs
3. 3
Speakers Timothy Kotin Co-Founder & CEO
BS Computer Science and Engineering Research Scientist
MPhil Engineering for Sustainable Development Specialized
Consultant
Yvette Titriku Data Scientist
BSc Actuarial Science Industry Experience
MS Applied Statistics
Gilbert Langat Data Scientist
MS Mathematical Sciences Industry
Experience
MS Mathematics
4. Session Outline
4
Introduction and Overview
•Superfluid Labs
•AI, Machine Learning and Data Science
•Selected Client Case Studies
Data Science Strategy
•Roadmap to Becoming a Full Data-Driven Enterprise
•Building Internal Capacity, Resources, and Tools
•Critical Success Factors and Pitfalls to Avoid
•Ensuring Sustainability of Data-Driven Transformation
Data Science Business Process
•Data Science Workflow
•Common Tools for Data Science
•Tips for Learning Data Science
6. We’re a data analytics firm that facilitates enterprises to develop digital platforms and
new customer solutions driven by data, machine-learning and AI
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Germany | Ghana | Kenya
OUR MISSION: To expand opportunity for people and businesses through the power of
data.
7. Our Vision
To be the preferred data-driven solutions partner for the most impactful
organizations
Industries and SDG Impact
Financial Services| Retail & Commerce| Agribusiness | Clean Energy |Technology
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9. Recognized as leader in Financial Services, AI and Big Data
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RecognitionsPartners&Compliance
10. What is data science, artificial intelligence
and machine learning?
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Data Analytics - evolutionary step in analytics combining computer science,
statistics, mathematics and machine learning to analyze large amounts of data
and extract useful knowledge
Artificial intelligence (AI) is a branch of computer science dealing with the
simulation of intelligent behavior in computers.
Machine learning (ML) is the scientific study of algorithms and statistical
models that computer systems use to effectively perform a specific task
without using explicit instructions, relying on patterns and inference instead.
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Why is data science important?
●Promotions
●Upsell
●Cross sell
●Pricing
●Shelf-space
optimization
●Risk Modelling
●Fraud
prediction
●Customer
segmentation
●Portfolio
optimization
●Market basket
analysis
●A/B testing
●Sales
forecasting
●Clinical trials
of new drugs
●Campaign
and sales
program
optimization
●Epidemic
forecasting
and control
●Chain
management
●Customer
acquisition
strategies
●Upsell/cross
sell
●Product
bundling
●Mobile user
location
analysis
●Customer
churn analysis
e-commerce Health TelcoBankingRetail
13. Predicting Customer Future Payment Behaviour
13
A distributed solar energy company that sells a wide range of solar off-grid units on credit using a pay-as-you-go (PAYGO) model.
Client sought a platform to harness the existing rich customer datasets to develop machine learning systems that can predict:
A. Future utilization rates of a portfolio of customers based on historical data
B. Customer upgrade propensities and outcomes for new accessories offered by the client
Challenge
SFL mined the dataset to build a customized model
that could (through prediction) enable…
Tree
N
Tree
2
Tree
3
Tree
4
Tree
1
Targeted
interventions
Early repossession Upsell to good
customers
✓ ✓ ✓
30% increase in Monthly revenues
Driven by early default
predictions alone
Success story 1 Success story 2 Success story 3
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14. Product Recommendation for Cross-sell of Electronics Devices
14
▪ Current qualification criteria upgrades a lot
of bad customers (44%) who have not yet
established meaningful repayment history.
▪ Losses from bad upgrades outweigh the gains from
good upgrades, leading to an average loss in LTV of
-13 USD per upgrade, and lowers overall
profitability
Current
situation
With SFL
Model
38USD
New average Impact of
upgrade with SFL Model
+51
increase
▪ By accurately predicting upgrade
outcomes (good or bad), our models
improved lifetime value of upgrades by
+51 USD
-13USD
Average impact of
each upgrade on LTV
Account Age at Upgrade
Account Age (days) before upgrade
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Success story 1 Success story 2 Success story 3
15. Online Lender focused on E-Commerce
Merchants
15
SITUATION
Our client disbursed 1445 pilot loans to
small merchants on popular e-commerce
platforms.
● Total disbursed: USD 350,000
● Financing fee: 6% per month
● Processing fee: USD 2-7
● Loan tenor: 30 days
PROBLEM OUR IMPACT
$ 140,000 $ 231,000
+$91,000
repayments
❏ Good rate: 27.68%
❏ Bad rate: 72.32%
❏ Good rate: 65.85%
❏ Bad rate: 34.15%
x 2.38
65.85%
34.15%
27.68%
72.32%
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Success story 1 Success story 2 Success story 3
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1. Culture – driving the change to a data-driven company
2. Buy-in – creating a corporate ambition for data science
3. Prioritization – choosing the right applications to deliver value
4. Team – building a data science capability
5. Speed – agile deployment
Key areas to focus on to become a data driven
enterprise
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Data Culture
● Principle established in the process of social
practice in both public and private sectors
● Requires all staffs and decision-makers
● Focus on the information conveyed by the
existing data
● Make decisions and changes according to
data results
19. Embracing the cultural shift to a data-driven
• Recruiting a team of diversified backgrounds
and experiences
• Embedding new skills and ways of thinking
within the business
• Role model new capabilities and approach
• Creation of Data Science and Analytics
community
• Involving everyone in new ideas and joining
projects
19
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Create a corporate ambition/ identify business
objectives
Deliver $100m benefit
over the next 5 years
using data science and
analytics.
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Prioritization - What matters most to your business?
Guiding principles
● Must generate real benefits
● Customer perspective
● Clear ability to execute necessary business change
● Data – sufficient volume, quality and understood
● Business sponsorship
● Reuse and scalability
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Building data science capability - define
your needs
● Be clear what you want – data analyst, data architect, data engineer,
data scientist, data artist(!)
● Hire for talent, train for tech skills
○ Analytical thinking and communication skills are harder to teach than SQL,
Python and R.
● Broaden your pool of candidates
○ Diversity-increases your revenue by promoting innovation and creative
thinking
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Building the Team-when starting out, a simple
team might look like this:
● Project Manager/Owner (existing management)
● Data Scientist/Data Engineer (one new hire, and one existing staff
member)
● Software Engineer (existing IT staff member)
Tip: If you are just getting started in data
science, it may be some time before you need
a true data scientist for predictive modeling or
machine learning — focus on hiring a data
engineer first.
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Speed - Agile development
MVP a product with just
enough features to satisfy
early customers, and to
provide feedback for future
product development.
(wikipedia)
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Superfluid Labs Project Success Factors
● Executive Sponsorship
● Business Sponsor (makes it happen on the ground)
● Over-communicate to all stakeholders
● IT – senior IT support to circumvent usual cycle times
● Data – early analysis for quality
● Business Change – run parallel alongside data / modelling – is there an existing
process to change?
● Team – right people, available, aligned and accountable
● Project Management – Agile methodology
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1. Include all parts of the organization and
stakeholders in the conversation
2. Create a corporate ambition/ business objectives
3. Build a data science capability
4. Prioritize all the things you could do to figure out
where to start
5. Define your roadmap with an end-point in mind
6. Adopt agile methodology
7. Embracing the cultural shift to a data-driven
company
Data-Driven
Strategy Checklist
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Data science workflow
● Business - understand business operations and business problem
● Data - acquire and understand data components
● Exploratory Analysis - visually and statistically explore data to generate hypothesis
● Data Preprocessing - clean and transform data
● Feature Engineering - generate additional features/ variables
● Model Development and Deployment - train, test, deploy and monitor predictive model
● Data Visualization - visualize and communicate key insights to inspire stakeholder action
● Business Analysis - analyze impact/value of model on business vs business-as-usual
32. SuperFluid Labs Data Analytics Platform
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The Superfluid’s platform mines data to deliver these capabilities using proprietary algorithms and
artificial intelligence
SuperScore™
Digital Credit Scoring Solution
powered by artificial intelligence
SuperML™
Automated Data Science and
Machine Learning Platform
SuperBI™
Business Intelligence, Customer
Insights and Analytics Platform
SUPER
ENTERPRISE
PLATFORM
SuperLife™
Intelligent Marketplace and
Business Ecosystem Platform
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Tips for learning and doing data science
● Understand the fundamentals
● Acquire domain knowledge
● Keep the data science problem in focus
● Learn the functions and modules frequently used during each stage of the
data science process
● Learn and improve programming skills
● Learn by doing. Create a data science portfolio
40. Join Win with Data community on
Facebook
• Join to participate in our periodic interactive
sessions where experts will be available to answer
questions from community members
40
Winwithdata
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Let’s expand opportunities for people and businesses
www.superfluid.io | info@superfluid.io
THANK YOU
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