Collected here are a few insights and lessons learned that I gathered during my recent time working with CloudMade in the realm of Machine Learning in automotive applications.
The slide has details on below points:
1. Introduction to Machine Learning
2. What are the challenges in acceptance of Machine Learning in Banks
3. How to overcome the challenges in adoption of Machine Learning in Banks
4. How to find new use cases of Machine Learning
5. Few current interesting use cases of Machine Learning
Please contact me (shekup@gmail.com) or connect with me on LinkedIn (https://www.linkedin.com/in/shekup/) for more explanation on ML and how it may help your business.
The slides are inspired by:
Survey & interviews done by me with Bankers & Technology Professionals
Presentation from Google NEXT 2017
Presentation by DATUM on Youtube
Royal Society Machine Learning
Big Data & Social Analytics Course from MIT & GetSmarter
Computers and powers to see the future Prof. Yakub aliyuYakub Aliyu
Computers are increasingly powerful and can now perform predictive analytics using large data sets. This allows them to predict human behavior and future outcomes by discovering hidden patterns in data. Data mining techniques are used to analyze large amounts of data to find useful patterns and insights. As computers continue advancing, they are predicted to replace humans in more jobs and activities by using big data and machine learning to perform tasks like driving cars, translation between languages, and making medical diagnoses. However, computers still lag behind human brains in aspects like learning new concepts and common sense reasoning.
Keep It Simple Series AI Machine Learning liteOuahid Seghir
AI Machine Learning 101 introductory presentation
Understanding Machine Learning in 30 minutes for operational purposes such as building use cases, business impacts and possibilities for business improvements
Designing Digital Change, Synopsis Hong Kong, April 2016:
In this session Mr. Nigel Green shares his experience of preparing organisations for the Digital World. He introduces key concepts that will help open-up the discussion of the implications, risks, and opportunities, of a digital strategy. Whilst the popular definition of “Going Digital” is often focused on digital channels for Marketing purposes, Mr. Green explains why it also impacts many areas of the organisation, and explains why it is not simply the CMO’s, CDO’s, or CIO’s challenge alone. He will also share tools and techniques used in the design & execution of the transformation to a digitally enabled business. In addition, he will discuss pragmatic next steps to take, and share ideas on how to contribute to a business-wide discussion on the subject.
This session should be of interest to anyone trying to get to grips with what “Going Digital” means to their organization, and how to start planning the change:
- The components of a digitally-enabled Business Model
- The implications & risks of adopting “Bi-modal IT”
- How to design for the protection of existing core business systems whilst embracing the new
- Dealing with an unknown future, and adaptive long-range planning
- The dangers of “Big Design Up Front”, and perhaps paradoxically, why “Adaptive Design” is ever more crucial
- The business and technology architecture implications - including a perspective on the applicability of a pattern adopted by the “born digitals” (e.g. Netflix, Google, and Amazon)
- Suggested subject matter experts to track, follow-up research material, and next steps to take.
From computational Thinking to computational Action - Dr. Hal Abelson, MIT Ap...CAVEDU Education
This document discusses the history and development of computational thinking and computational action through the MIT App Inventor project. It provides examples of how students from around the world have used App Inventor to create apps that address real-world issues in their communities, such as finding clean water sources and improving healthcare. The document also outlines the growing global use of App Inventor and how it enables students to engage in computational thinking and action through designing and building mobile apps.
Infrastructure Designed for Cognitive Workloads: Why is it Crucial? - Xavier ...WithTheBest
In the IT infrastructure for the cognitive era that we live in today, you have to think differently about you design, build, and deliver services. Artificial Intelligence can help you improve your designs for your cognitive business. Discover how you can deliver through cloud platforms. This infrastructure sets up your business with new computing frontiers.
Xavier Vasques, Technical Director, Systems Hardware, IBM France
Jeannette M. Wing argues that computational thinking will be a fundamental skill used by everyone globally by mid-21st century, just like reading, writing and arithmetic. She defines computational thinking as involving abstraction, automation, and problem-solving approaches from computer science. Wing provides examples of computational thinking in various disciplines and calls for reforming curricula to teach computational thinking concepts from K-12 through graduate levels.
Horses & Unicorns: Britchamber july 2016Nigel Green
This story was first told to the British Chamber in Hong Kong in May 2016. It's about a real business that wishes to remain anonymous. It is just a short teaser that begs questions and much more discussion, but it did generate lively Q&A on the day.
Please visit the Horses & Unicorns blog: http://horsesunicorns.blogspot.co.uk/
The slide has details on below points:
1. Introduction to Machine Learning
2. What are the challenges in acceptance of Machine Learning in Banks
3. How to overcome the challenges in adoption of Machine Learning in Banks
4. How to find new use cases of Machine Learning
5. Few current interesting use cases of Machine Learning
Please contact me (shekup@gmail.com) or connect with me on LinkedIn (https://www.linkedin.com/in/shekup/) for more explanation on ML and how it may help your business.
The slides are inspired by:
Survey & interviews done by me with Bankers & Technology Professionals
Presentation from Google NEXT 2017
Presentation by DATUM on Youtube
Royal Society Machine Learning
Big Data & Social Analytics Course from MIT & GetSmarter
Computers and powers to see the future Prof. Yakub aliyuYakub Aliyu
Computers are increasingly powerful and can now perform predictive analytics using large data sets. This allows them to predict human behavior and future outcomes by discovering hidden patterns in data. Data mining techniques are used to analyze large amounts of data to find useful patterns and insights. As computers continue advancing, they are predicted to replace humans in more jobs and activities by using big data and machine learning to perform tasks like driving cars, translation between languages, and making medical diagnoses. However, computers still lag behind human brains in aspects like learning new concepts and common sense reasoning.
Keep It Simple Series AI Machine Learning liteOuahid Seghir
AI Machine Learning 101 introductory presentation
Understanding Machine Learning in 30 minutes for operational purposes such as building use cases, business impacts and possibilities for business improvements
Designing Digital Change, Synopsis Hong Kong, April 2016:
In this session Mr. Nigel Green shares his experience of preparing organisations for the Digital World. He introduces key concepts that will help open-up the discussion of the implications, risks, and opportunities, of a digital strategy. Whilst the popular definition of “Going Digital” is often focused on digital channels for Marketing purposes, Mr. Green explains why it also impacts many areas of the organisation, and explains why it is not simply the CMO’s, CDO’s, or CIO’s challenge alone. He will also share tools and techniques used in the design & execution of the transformation to a digitally enabled business. In addition, he will discuss pragmatic next steps to take, and share ideas on how to contribute to a business-wide discussion on the subject.
This session should be of interest to anyone trying to get to grips with what “Going Digital” means to their organization, and how to start planning the change:
- The components of a digitally-enabled Business Model
- The implications & risks of adopting “Bi-modal IT”
- How to design for the protection of existing core business systems whilst embracing the new
- Dealing with an unknown future, and adaptive long-range planning
- The dangers of “Big Design Up Front”, and perhaps paradoxically, why “Adaptive Design” is ever more crucial
- The business and technology architecture implications - including a perspective on the applicability of a pattern adopted by the “born digitals” (e.g. Netflix, Google, and Amazon)
- Suggested subject matter experts to track, follow-up research material, and next steps to take.
From computational Thinking to computational Action - Dr. Hal Abelson, MIT Ap...CAVEDU Education
This document discusses the history and development of computational thinking and computational action through the MIT App Inventor project. It provides examples of how students from around the world have used App Inventor to create apps that address real-world issues in their communities, such as finding clean water sources and improving healthcare. The document also outlines the growing global use of App Inventor and how it enables students to engage in computational thinking and action through designing and building mobile apps.
Infrastructure Designed for Cognitive Workloads: Why is it Crucial? - Xavier ...WithTheBest
In the IT infrastructure for the cognitive era that we live in today, you have to think differently about you design, build, and deliver services. Artificial Intelligence can help you improve your designs for your cognitive business. Discover how you can deliver through cloud platforms. This infrastructure sets up your business with new computing frontiers.
Xavier Vasques, Technical Director, Systems Hardware, IBM France
Jeannette M. Wing argues that computational thinking will be a fundamental skill used by everyone globally by mid-21st century, just like reading, writing and arithmetic. She defines computational thinking as involving abstraction, automation, and problem-solving approaches from computer science. Wing provides examples of computational thinking in various disciplines and calls for reforming curricula to teach computational thinking concepts from K-12 through graduate levels.
Horses & Unicorns: Britchamber july 2016Nigel Green
This story was first told to the British Chamber in Hong Kong in May 2016. It's about a real business that wishes to remain anonymous. It is just a short teaser that begs questions and much more discussion, but it did generate lively Q&A on the day.
Please visit the Horses & Unicorns blog: http://horsesunicorns.blogspot.co.uk/
Building machine learning models is challenging, requiring many steps from data ingestion to deployment. Feature engineering, which transforms raw data into more useful representations, is often the most important step for model performance. Automating less important steps like data cleaning frees up time to focus on feature engineering through multiple iterations of the OODA loop of observe, orient, decide, and act. This allows generating better models through more experimentation and domain knowledge application to extract the most informative features.
Emerging Technologies & Trends That Matter Now
Regus has teamed up with Muhammad Jameel (PMP), an independent technology delivery strategist and consultant, to hold a workshop on Emerging Technologies & Trends That Matter Now. The workshop is for all Regus clients across town.
Technology is all around us. Muhammad uses his United States & GCC experience in helping enterprises strategize and deliver emerging technologies. He has consulted clients on key issues: How does technology align with corporate strategy? How do we best deliver technology for enterprises?
We invite you to join us in a 30 min session where Muhammad walks us through the emerging trends that enterprises in Qatar, and globally, are following, and how your business may also come across some of these trends.
Knowledge is power. Don’t tell me sky is the limit; I’ve seen footsteps on the moon.
Role of Unified AI and ML in Cloud Technologies. Which Cloud Service Provider...Denodo
Watch full webinar here: https://bit.ly/3hpTRep
AI and ML help automate many of the enterprise tasks. What role do they play in cloud technologies? And, different cloud service providers (CSP) claim AI and ML capabilities within their technologies. But which one has better support for data science? Does any one CSP provide better tools and automation for data scientists to perform their analysis with ease and speed? The Chief AI Architect from UST will elaborate on the differences between cloud technologies for supporting AI, ML, and data science. Do you have additional questions that you want answered on this subject? Then bring them on.
How could OpenAI, a small organization of just 200 employees, managed to shake the foundations of large companies like Google and Meta? Everyone dreams about being a unicorn – having razor sharp focus, high talent-density , rapid speed of innovation but in reality, even startups end up becoming slow organizations very quickly. Why does this happen?
Transforming Insurance Analytics with Big Data and Automated Machine Learning Cloudera, Inc.
This document discusses how machine learning and big data analytics can transform the insurance industry. It provides an overview of how automated machine learning works and its benefits for insurers, including higher returns on investment. Specific use cases discussed include underwriting triage, pricing, claims management, and fraud prevention. The document also addresses key data challenges for insurers and how a unified data platform can help bring different data sources together for machine learning. It promotes the idea that automated machine learning solutions can make machine learning more accessible, affordable and inclusive for organizations.
Cw13 cloud computing & big data by ahmed aamerinevitablecloud
The document discusses emerging technologies like cloud computing and big data from a CIO's perspective. It covers topics such as cloud maturity models, challenges of cloud services, and characteristics of big data including the need to capture, correlate, coordinate and corporatize large amounts of data from various sources. The document provides an overview of how new technologies are impacting business and IT experiences.
(In)convenient truths about applied machine learningMax Pagels
This document provides observations and recommendations for reconciling machine learning with business needs. Some key points made include:
- In many cases, machine learning is not needed to solve a problem and simpler solutions like collecting missing data can work better.
- The data companies already have is sometimes useless for machine learning problems. Domain expertise alone also often means less than expected.
- Not understanding technical constraints can cause machine learning projects to fail. Always create a proof-of-concept first before full development.
- It is important to establish causality through proper testing like A/B testing, as this validates models and addresses financial risks of implementations.
- Framing learning problems is challenging due to issues like lous
Executive Perspective Building an OT Security Program from the Top Downaccenture
Designed for executives, this non-technical track addresses key components of a successful OT security program. The discussions are intended to spark conversation and this guide highlights key takeaways on what works, what doesn’t and what’s next. https://accntu.re/3N7KmiZ
My personal view of the top learning technologies for 2016. Taken from a range of academic and industry sources, with a point of view on how they can be used in Learning and Development.
This document provides an overview of artificial intelligence (AI) and machine learning. It begins by defining AI as computer systems able to perform cognitive tasks like reasoning, decision making, perception, and language understanding. It then discusses what AI is good at, including classification, pattern recognition, prediction, and information retrieval. The document also covers different types of machine learning algorithms like supervised and unsupervised learning. It aims to demystify key AI concepts and discuss opportunities for applying AI in the chemical industry.
Machine Learning: Need of Machine Learning, Its Challenges and its ApplicationsArpana Awasthi
BCA Department of JIMS Vasant Kunj-II is one of the best BCA colleges in Delhi NCR. The curriculum is well updated and the subjects included all the latest technologies which are in demand.
JIMS BCA course teaches Python to II semester students and Artificial Intelligence Using Python to Sixth Semester students.
Here is a small article on the Future of Machine Learning, hope you will find it useful.
Machine Learning is a field of Computer science in which computer systems are able to learn from past experiences, examples, environments. With help of various Machine Learning Algorithms, Computers are provided with the ability to sense the data and produce some relevant results.
Machine learning Algorithms provide the technique of predicting the future outcomes or classifying information from the given input to the Machines so that the appropriate decisions can be taken.
Generative AI - The New Reality: How Key Players Are Progressing Vishal Sharma
The document discusses key players in generative AI and their progress. It provides an overview of generative AI including its evolution since 1950, where the spending is focused, how the technology works, and deployment models. It then profiles several major companies leading advancements in generative AI, including their strategies, growth areas, and risks. These companies are TSMC, Nvidia, Microsoft, Google, Amazon, Tesla, Oracle, Salesforce, SAP, and Palo Alto Networks.
The document discusses how cloud computing can meet the needs of enterprise architecture. It provides scenarios of requirements from different business leaders for an airline transportation application. This poses challenges for the CIO to fulfill all the dynamic and changing requirements. The document then discusses viewing cloud computing from different perspectives and how it can help address the CIO's challenges by transferring risks, focusing on core business needs, and allowing flexibility and scalability. It also discusses principles for cloud and enterprise architecture around requirements, service expectations, governance, and business models.
The document outlines nine key steps that companies can take as part of a digital transformation journey to disrupt themselves before competitors do. The steps include: 1) designing an end-game disruptive business model, 2) analyzing gaps between the current and future models, 3) determining how to execute the transition, 4) architecting new technology, 5) auditing legacy systems, 6) building out a dual-speed IT architecture, 7) establishing a data security strategy, 8) maintaining security during transformation, and 9) using transformation as an opportunity to escalate security standards across the enterprise. Taking these steps can help traditional firms successfully transition to competing in the new digital landscape.
The document summarizes lessons learned from adopting open standards and cloud computing at North Carolina State University. It discusses how NCSU was able to break down silos and disrupt the status quo by focusing on business outcomes and operational ROI improvements. This allowed them to embrace change and increase control over spending. The summary also notes some insidious threats to cloud adoption like software licensing and security issues.
This document discusses Microsoft's efforts in artificial intelligence and machine learning. It provides context on the current state of AI, highlighting how machine learning has progressed from addressing specific tasks to becoming more general. It outlines Microsoft's investments in AI, including forming a new 5,000-person division and making AI pervasive across its products. The document also discusses challenges around developing machine learning programs and ensuring AI is developed in a responsible, trustworthy manner.
- Managing engineering organizations today is more difficult due to projects being understaffed and shorter schedules as well as generational differences between employees.
- CEOs are more involved in product development and focused on innovation for competitive advantage. They want more visibility into engineering operations.
- Talent management must be integrated into business strategy and implemented throughout the organization, not just in HR, to attract and retain employees.
- Different approaches are needed to develop and document products to make design decisions and provide definitions for manufacturing. Changing IT landscapes and tools also impact engineering organizations.
Kalyan chart DP boss guessing matka results➑➌➋➑➒➎➑➑➊➍
8328958814Satta Matka is a number-based game. There are several markets, each with its owner responsible for releasing the lottery satta Matka market results on time. Kalyan market, Worli market, main Mumbai market, Rajdhani market, and Milan market are some of the main markets or bazaars involved in the satta Matka game. The oldest and most legitimate markets are in Kalyan and Main Mumbai. Every Satta Market has an open and close time. The satta results for these markets are published on or shortly after the open and close times. During the open result, two numbers are decoded, one of which is a three-digit number and the other a single-digit number. Similarly, three-digit and single-digit numbers are declared during the satta market's close. The last digit after adding the three digits of the open or close result is usually the single digit declared during the open and close results.KALYAN MATKA | MATKA RESULT | KALYAN MATKA TIPS | SATTA MATKA | MATKA.COM | MATKA PANA JODI TODAY | BATTA SATKA | MATKA PATTI JODI NUMBER | MATKA RESULTS | MATKA CHART | MATKA JODI | SATTA COM | FULL RATE GAME | MATKA GAME | MATKA WAPKA | ALL MATKA RESULT LIVE ONLINE | MATKA RESULT | KALYAN MATKA RESULT | DPBOSS MATKA 143 | MAINSATTA MATKA SATTA FAST RESULT KALYAN TOP MATKA RESULT KALYAN SATTA MATKA FAST RESULT MILAN RATAN RAJDHANI MAIN BAZAR MATKA FAST TIPS RESULT MATKA CHART JODI CHART PANEL CHART FREE FIX GAME SATTAMATKA ! MATKA MOBI SATTA 143 spboss.in TOP NO1 RESULT FULL RATE MATKA ONLINE GAME PLAY BY APP SPBOSSdp boss net, dp satta, dpboss dpboss, indian satta matka, kalyan matkà result today , matka boss, matka result live, matka satta result today, satamatka com, satta boss, satta matka king, sattamatkà, sattamatkà result, sattamatta com, sattmatka sattmatka, star matka, tara matka, tara satta matka, worli matka, indian matka, matka live, kalyan guessing, satta fix, kalyan final ank, dp matka, dpboss net, sata mata com, सट्टा मटका, sattamatkà 143, golden matka, satta matta matka 143, satta fast, kalyan open, satta 143, dpboss 143 guessing, dpboss satta, golden satta matka, satta bajar
Satta Matka Market is India's leading website providing the quickest sattamatka outcome, experienced in Satta Matka game. Our services include free Satta Matka Trick and Tips for Kalyan Matka and Disawar Satta King, as well as satta matka graphs, online play, tips and more. Our team of experts strive to help you recoup your losses quickly through our proposals such as Free Satta Matka Tips and Kalyan Bazar Tips. We are known as India's best Matka DpBoss portal site, here to deliver updates on all sorts of Satta Market like Kalyan Bazar, Milan, Rajdhani, Time Bazaar, Main and the most current charts. Stay tuned with us for more live updates on the Satta market
Building machine learning models is challenging, requiring many steps from data ingestion to deployment. Feature engineering, which transforms raw data into more useful representations, is often the most important step for model performance. Automating less important steps like data cleaning frees up time to focus on feature engineering through multiple iterations of the OODA loop of observe, orient, decide, and act. This allows generating better models through more experimentation and domain knowledge application to extract the most informative features.
Emerging Technologies & Trends That Matter Now
Regus has teamed up with Muhammad Jameel (PMP), an independent technology delivery strategist and consultant, to hold a workshop on Emerging Technologies & Trends That Matter Now. The workshop is for all Regus clients across town.
Technology is all around us. Muhammad uses his United States & GCC experience in helping enterprises strategize and deliver emerging technologies. He has consulted clients on key issues: How does technology align with corporate strategy? How do we best deliver technology for enterprises?
We invite you to join us in a 30 min session where Muhammad walks us through the emerging trends that enterprises in Qatar, and globally, are following, and how your business may also come across some of these trends.
Knowledge is power. Don’t tell me sky is the limit; I’ve seen footsteps on the moon.
Role of Unified AI and ML in Cloud Technologies. Which Cloud Service Provider...Denodo
Watch full webinar here: https://bit.ly/3hpTRep
AI and ML help automate many of the enterprise tasks. What role do they play in cloud technologies? And, different cloud service providers (CSP) claim AI and ML capabilities within their technologies. But which one has better support for data science? Does any one CSP provide better tools and automation for data scientists to perform their analysis with ease and speed? The Chief AI Architect from UST will elaborate on the differences between cloud technologies for supporting AI, ML, and data science. Do you have additional questions that you want answered on this subject? Then bring them on.
How could OpenAI, a small organization of just 200 employees, managed to shake the foundations of large companies like Google and Meta? Everyone dreams about being a unicorn – having razor sharp focus, high talent-density , rapid speed of innovation but in reality, even startups end up becoming slow organizations very quickly. Why does this happen?
Transforming Insurance Analytics with Big Data and Automated Machine Learning Cloudera, Inc.
This document discusses how machine learning and big data analytics can transform the insurance industry. It provides an overview of how automated machine learning works and its benefits for insurers, including higher returns on investment. Specific use cases discussed include underwriting triage, pricing, claims management, and fraud prevention. The document also addresses key data challenges for insurers and how a unified data platform can help bring different data sources together for machine learning. It promotes the idea that automated machine learning solutions can make machine learning more accessible, affordable and inclusive for organizations.
Cw13 cloud computing & big data by ahmed aamerinevitablecloud
The document discusses emerging technologies like cloud computing and big data from a CIO's perspective. It covers topics such as cloud maturity models, challenges of cloud services, and characteristics of big data including the need to capture, correlate, coordinate and corporatize large amounts of data from various sources. The document provides an overview of how new technologies are impacting business and IT experiences.
(In)convenient truths about applied machine learningMax Pagels
This document provides observations and recommendations for reconciling machine learning with business needs. Some key points made include:
- In many cases, machine learning is not needed to solve a problem and simpler solutions like collecting missing data can work better.
- The data companies already have is sometimes useless for machine learning problems. Domain expertise alone also often means less than expected.
- Not understanding technical constraints can cause machine learning projects to fail. Always create a proof-of-concept first before full development.
- It is important to establish causality through proper testing like A/B testing, as this validates models and addresses financial risks of implementations.
- Framing learning problems is challenging due to issues like lous
Executive Perspective Building an OT Security Program from the Top Downaccenture
Designed for executives, this non-technical track addresses key components of a successful OT security program. The discussions are intended to spark conversation and this guide highlights key takeaways on what works, what doesn’t and what’s next. https://accntu.re/3N7KmiZ
My personal view of the top learning technologies for 2016. Taken from a range of academic and industry sources, with a point of view on how they can be used in Learning and Development.
This document provides an overview of artificial intelligence (AI) and machine learning. It begins by defining AI as computer systems able to perform cognitive tasks like reasoning, decision making, perception, and language understanding. It then discusses what AI is good at, including classification, pattern recognition, prediction, and information retrieval. The document also covers different types of machine learning algorithms like supervised and unsupervised learning. It aims to demystify key AI concepts and discuss opportunities for applying AI in the chemical industry.
Machine Learning: Need of Machine Learning, Its Challenges and its ApplicationsArpana Awasthi
BCA Department of JIMS Vasant Kunj-II is one of the best BCA colleges in Delhi NCR. The curriculum is well updated and the subjects included all the latest technologies which are in demand.
JIMS BCA course teaches Python to II semester students and Artificial Intelligence Using Python to Sixth Semester students.
Here is a small article on the Future of Machine Learning, hope you will find it useful.
Machine Learning is a field of Computer science in which computer systems are able to learn from past experiences, examples, environments. With help of various Machine Learning Algorithms, Computers are provided with the ability to sense the data and produce some relevant results.
Machine learning Algorithms provide the technique of predicting the future outcomes or classifying information from the given input to the Machines so that the appropriate decisions can be taken.
Generative AI - The New Reality: How Key Players Are Progressing Vishal Sharma
The document discusses key players in generative AI and their progress. It provides an overview of generative AI including its evolution since 1950, where the spending is focused, how the technology works, and deployment models. It then profiles several major companies leading advancements in generative AI, including their strategies, growth areas, and risks. These companies are TSMC, Nvidia, Microsoft, Google, Amazon, Tesla, Oracle, Salesforce, SAP, and Palo Alto Networks.
The document discusses how cloud computing can meet the needs of enterprise architecture. It provides scenarios of requirements from different business leaders for an airline transportation application. This poses challenges for the CIO to fulfill all the dynamic and changing requirements. The document then discusses viewing cloud computing from different perspectives and how it can help address the CIO's challenges by transferring risks, focusing on core business needs, and allowing flexibility and scalability. It also discusses principles for cloud and enterprise architecture around requirements, service expectations, governance, and business models.
The document outlines nine key steps that companies can take as part of a digital transformation journey to disrupt themselves before competitors do. The steps include: 1) designing an end-game disruptive business model, 2) analyzing gaps between the current and future models, 3) determining how to execute the transition, 4) architecting new technology, 5) auditing legacy systems, 6) building out a dual-speed IT architecture, 7) establishing a data security strategy, 8) maintaining security during transformation, and 9) using transformation as an opportunity to escalate security standards across the enterprise. Taking these steps can help traditional firms successfully transition to competing in the new digital landscape.
The document summarizes lessons learned from adopting open standards and cloud computing at North Carolina State University. It discusses how NCSU was able to break down silos and disrupt the status quo by focusing on business outcomes and operational ROI improvements. This allowed them to embrace change and increase control over spending. The summary also notes some insidious threats to cloud adoption like software licensing and security issues.
This document discusses Microsoft's efforts in artificial intelligence and machine learning. It provides context on the current state of AI, highlighting how machine learning has progressed from addressing specific tasks to becoming more general. It outlines Microsoft's investments in AI, including forming a new 5,000-person division and making AI pervasive across its products. The document also discusses challenges around developing machine learning programs and ensuring AI is developed in a responsible, trustworthy manner.
- Managing engineering organizations today is more difficult due to projects being understaffed and shorter schedules as well as generational differences between employees.
- CEOs are more involved in product development and focused on innovation for competitive advantage. They want more visibility into engineering operations.
- Talent management must be integrated into business strategy and implemented throughout the organization, not just in HR, to attract and retain employees.
- Different approaches are needed to develop and document products to make design decisions and provide definitions for manufacturing. Changing IT landscapes and tools also impact engineering organizations.
Kalyan chart DP boss guessing matka results➑➌➋➑➒➎➑➑➊➍
8328958814Satta Matka is a number-based game. There are several markets, each with its owner responsible for releasing the lottery satta Matka market results on time. Kalyan market, Worli market, main Mumbai market, Rajdhani market, and Milan market are some of the main markets or bazaars involved in the satta Matka game. The oldest and most legitimate markets are in Kalyan and Main Mumbai. Every Satta Market has an open and close time. The satta results for these markets are published on or shortly after the open and close times. During the open result, two numbers are decoded, one of which is a three-digit number and the other a single-digit number. Similarly, three-digit and single-digit numbers are declared during the satta market's close. The last digit after adding the three digits of the open or close result is usually the single digit declared during the open and close results.KALYAN MATKA | MATKA RESULT | KALYAN MATKA TIPS | SATTA MATKA | MATKA.COM | MATKA PANA JODI TODAY | BATTA SATKA | MATKA PATTI JODI NUMBER | MATKA RESULTS | MATKA CHART | MATKA JODI | SATTA COM | FULL RATE GAME | MATKA GAME | MATKA WAPKA | ALL MATKA RESULT LIVE ONLINE | MATKA RESULT | KALYAN MATKA RESULT | DPBOSS MATKA 143 | MAINSATTA MATKA SATTA FAST RESULT KALYAN TOP MATKA RESULT KALYAN SATTA MATKA FAST RESULT MILAN RATAN RAJDHANI MAIN BAZAR MATKA FAST TIPS RESULT MATKA CHART JODI CHART PANEL CHART FREE FIX GAME SATTAMATKA ! MATKA MOBI SATTA 143 spboss.in TOP NO1 RESULT FULL RATE MATKA ONLINE GAME PLAY BY APP SPBOSSdp boss net, dp satta, dpboss dpboss, indian satta matka, kalyan matkà result today , matka boss, matka result live, matka satta result today, satamatka com, satta boss, satta matka king, sattamatkà, sattamatkà result, sattamatta com, sattmatka sattmatka, star matka, tara matka, tara satta matka, worli matka, indian matka, matka live, kalyan guessing, satta fix, kalyan final ank, dp matka, dpboss net, sata mata com, सट्टा मटका, sattamatkà 143, golden matka, satta matta matka 143, satta fast, kalyan open, satta 143, dpboss 143 guessing, dpboss satta, golden satta matka, satta bajar
Satta Matka Market is India's leading website providing the quickest sattamatka outcome, experienced in Satta Matka game. Our services include free Satta Matka Trick and Tips for Kalyan Matka and Disawar Satta King, as well as satta matka graphs, online play, tips and more. Our team of experts strive to help you recoup your losses quickly through our proposals such as Free Satta Matka Tips and Kalyan Bazar Tips. We are known as India's best Matka DpBoss portal site, here to deliver updates on all sorts of Satta Market like Kalyan Bazar, Milan, Rajdhani, Time Bazaar, Main and the most current charts. Stay tuned with us for more live updates on the Satta market
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Ensure your Mini Cooper stays cool and comfortable with our top-quality AC service. Our expert technicians provide comprehensive maintenance, repairs, and performance optimization, guaranteeing reliable cooling and peak efficiency. Trust us for quick, professional service that keeps your Mini Cooper's air conditioning system in top condition, ensuring a pleasant driving experience year-round.
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3. First, a Misconception
Having a vision is important-- there should always
be a goal to strive for.
But… we should take a sober, rational approach.
“If it’s built in Python, it’s Machine Learning.
If it’s built in PowerPoint, it’s Artificial Intelligence”
-- CloudMade Data Scientist
The idea of “Artificial Intelligence” in an automobile.
It is still a long, long way in the future.
4.
5. Making predictions based on
the behavioral patterns and
preferences of an individual
Making predictions based on
large-scale aggregation of
behavior (speed, feature use,
etc.)
Making predictions based on
similarities to others
Cohort Learning
Personal LearningFleet Learning
Behavioral Learning
7. Applications Today
Led by Luxury, but moving mainstream
JLR Smart Settings
Audi MIB2+
BMW Connected+
Hyundai Smart Cruise
Ford Mach-E
Mercedes MBUX
8. Typical Use Cases
Focused on the Journey
• Destination
• Route
• Departure time
• Arrival time
What they do on the way
• Media preferences
• Climate preferences
Built around the driver’s profile
9. Why has it taken so
long?
Connectivity
In-vehicle hardware
Backend systems at carmakers
Readiness of users
But mostly: Because it’s hard.
10. Configuration
Data Collection
Feature Extraction
ML
Data
Verification
Machine
Resource
Management
Analysis Tools
Process Management
Tools
Serving
Infrastructure
Monitoring
“Only a small fraction of real-world ML systems is composed of the ML code, as shown by the small
black box in the middle. The required surrounding infrastructure is vast and complex. “
Hidden Technical Debt in Machine Learning Systems
Google, Inc.
Why it’s so Hard
11. Challenges and Pitfalls
Going Rogue
Dangers: Feedback Loops, Non-
homogenous data, non-declared
consumers, etc.
à Technical debt. Potentially massive
amounts.
Privacy
• Trust – avoiding creepiness
• Data protection regulations
(GDPR, CCPA)
• Consent management
Disillusionment
• Expectations management
• Some people have predictable
behaviors. Others do not.
• UX Design is critical here
Coherency
ML must be managed across the entire
ecosystem (car, cloud, companion app)
• Consistency of predictions
• Holistic approach.
12. Big Challenge: The Business Case
What’s the ROI?
If there is a significant investment to
properly deploy ML, what’s the payback?
What is a prediction worth?
Valuing ML Solutions is not easy.
What did we learn?
• There are very few standalone “killer use-
cases”.
• High level “elegance” features are difficult
to value or monetize
• Instead it’s the accumulation of many
features that add up to provide synergy
Solve real problems
• Look further down Maslow’s Automaker’s
hierarchy of needs
à Focus on Safety, Emissions, Efficiency, Cost
of ownership, Retention, Costs of
development, manufacture, etc.
13. Deployment
Considerations
Architecture
• Where should the learning be done?
• Storage, memory, CPU resources, transmission,
cloud costs
• Where should the predictions be done?
• Latency? Context?
Managing data
• Validation
• Extraction
• Normalization across entire fleet
• Privacy
14. UX Design Paradigm Shift
Answer the ‘W’ questions:
• Where are they going? Who will go?
When will they leave? How will they
get there? What will they do on the
way?
Confidence? Predictions are
probabilities, not absolutes.
Integration: Non-deterministic
predictions must be integrated
into your deterministic UX logic.
This is where your skill enters in.
CONTEXT CONTEXT + INTENT
16. Future Applications
Adaptive UX
• Familiarity with area
• Cognitive workload
Feature on-boarding
• Cohort, Fleet, Personal
Preconditioning
• Climate
• EV batteries
Maintenance functions
• Performed at ideal time based on
predicted route, speed, etc.
Powertrain Efficiency
• Lean burn, ICE/BEV, Transmission
optimization
And on…
17. Take-Aways
Machine Learning is real.
Adds real value to the automaker and their customers.
Building a proper foundation is a must.
Maturity level is about to make a big jump.
It is truly a new frontier.
18. “To a man with a
hammer, everything
looks like a nail”
-- Mark Twain (?)
“AI is not magic pixie dust.
Predictions can provide value,
but there is no substitute for
good well-thought design.”
--Jeff
19. References
Yogi Berra quote
https://quoteinvestigator.com/2013/10/20/no-predict/
Land Rover's Self-Learning Intelligent Vehicle Video
https://www.youtube.com/watch?v=F923EuB06CI
Mercedes MBUX
https://www.mercedes-
benz.com/en/innovation/connected/mbux-mercedes-
benz-user-experience-revolution-in-the-cockpit/
Audi MIB2+ Infotainment
https://www.audi-mediacenter.com/en/the-new-audi-rs-
q8-the-most-sporty-q-12422/infotainment-and-audi-
connect-12432
Hyundai Smart Cruise (SCC-ML)
https://www.hyundainews.com/en-us/releases/2887
Ford Mustang Mach-E Launch
https://youtu.be/o0F9Uktpgtk?t=1309
Hidden Technical Debt in Machine Learning Systems
D. Sculley, Gary Holt, Daniel Golovin, et al , Google Inc.
https://papers.nips.cc/paper/5656-hidden-technical-debt-
in-machine-learning-systems.pdf
Mark Twain Quote
https://quoteinvestigator.com/2014/05/08/hammer-nail/