This document describes building data science pipelines in Python using Luigi. It discusses the typical data science workflow, challenges with the current workflow approach, and how data science pipelines with Luigi can help address these challenges. Key features of Luigi that make it useful for data science pipelines are presented, including task templating, scheduling, monitoring, failure recovery, and enabling batch and parallel processing. The document concludes with a demonstration Luigi pipeline example to predict the performance score of mobile game users.
Qu speaker series 14: Synthetic Data Generation in FinanceQuantUniversity
In this master class, Stefan shows how to create synthetic time-series data using generative adversarial networks (GAN). GANs train a generator and a discriminator network in a competitive setting so that the generator learns to produce samples that the discriminator cannot distinguish from a given class of training data. The goal is to yield a generative model capable of producing synthetic samples representative of this class. While most popular with image data, GANs have also been used to generate synthetic time-series data in the medical domain. Subsequent experiments with financial data explored whether GANs can produce alternative price trajectories useful for ML training or strategy backtests.
Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor flow, IBM watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science role. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python,Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.
In 2009 author and motivational speaker Simon Sinek delivered the now-classic TED talk “Start with why”. Viewed by over 28 million people, “Start with Why” is the third most popular TED video of all time and it teaches us that great leaders and companies inspire us to take action by focusing on the WHY over the “what” or the “how”. In this talk we’ll ask how applied data and computational scientists can use the power of WHY to frame problems, inspire others, and give them answers to business questions they might never think of asking.
Bio
Jessica Stauth is a Managing Director in Fidelity Labs, an internal startup incubator with a mission to create new fintech businesses that drive growth for the firm. Dr. Stauth previously held roles as Managing Director of Portfolio Management, Research, and Trading at Quantopian, a crowd-sourced systematic hedge fund based in Boston, Director of Quant Product Strategy for Thomson Reuters (now Refinitiv), and as a Senior Quant Researcher at the StarMine Corporation, where she built global stock selection models including the design and implementation of the StarMine Short Interest model. Dr. Stauth holds a PhD in Biophysics from UC Berkeley, where her research focused on computational neuroscience.
Qu speaker series:Ethical Use of AI in Financial MarketsQuantUniversity
As AI and ML penetrates the financial industry, there are growing concerns about ethical use of AI in Finance. In this talk, Dan will focus on how the AI can be operationalized to help industry professionals and executive teams alike think about opportunities, risks as well as required actions factoring in ethics in our data-driven world.
Learn how artificial intelligence (AI) and machine learning are revolutionizing industries — this course will introduce key concepts and illustrate the role of machine learning, data science techniques, and AI through examples and case studies from the investment industry. The presentation uses simple mathematics and basic statistics to provide an intuitive understanding of machine learning, as used by firms, to augment traditional decision making.
https://quforindia.splashthat.com/
Robotics & Artificial (RAI) Intelligence webinar: Law & Regulation for RAI In...KTN
The Robotics & AI Innovation Network hosted a webinar addressing some of the legal and regulatory issues faced by the RAI community in the UK. Three legal experts provided their expertise to address these issues.
- Doug Bryden | Partner; Head of the Operational Risk & Environment Group, Travers Smith LLP
- Mark Richardson | Partner; IT, Telecoms and Electronics, Keltie
- Sébastien A. Krier | Founder & AI Ethics/Policy Expert, Dataphysix Ltd
Qu speaker series 14: Synthetic Data Generation in FinanceQuantUniversity
In this master class, Stefan shows how to create synthetic time-series data using generative adversarial networks (GAN). GANs train a generator and a discriminator network in a competitive setting so that the generator learns to produce samples that the discriminator cannot distinguish from a given class of training data. The goal is to yield a generative model capable of producing synthetic samples representative of this class. While most popular with image data, GANs have also been used to generate synthetic time-series data in the medical domain. Subsequent experiments with financial data explored whether GANs can produce alternative price trajectories useful for ML training or strategy backtests.
Learnbay provides industry accredited data science courses in Bangalore. We understand the conjugation of technology in the field of Data science hence we offer significant courses like Machine learning, Tensor flow, IBM watson, Google Cloud platform, Tableau, Hadoop, time series, R and Python. With authentic real time industry projects. Students will be efficient by being certified by IBM. Around hundreds of students are placed in promising companies for data science role. Choosing Learnbay you will reach the most aspiring job of present and future.
Learnbay data science course covers Data Science with Python,Artificial Intelligence with Python, Deep Learning using Tensor-Flow. These topics are covered and co-developed with IBM.
In 2009 author and motivational speaker Simon Sinek delivered the now-classic TED talk “Start with why”. Viewed by over 28 million people, “Start with Why” is the third most popular TED video of all time and it teaches us that great leaders and companies inspire us to take action by focusing on the WHY over the “what” or the “how”. In this talk we’ll ask how applied data and computational scientists can use the power of WHY to frame problems, inspire others, and give them answers to business questions they might never think of asking.
Bio
Jessica Stauth is a Managing Director in Fidelity Labs, an internal startup incubator with a mission to create new fintech businesses that drive growth for the firm. Dr. Stauth previously held roles as Managing Director of Portfolio Management, Research, and Trading at Quantopian, a crowd-sourced systematic hedge fund based in Boston, Director of Quant Product Strategy for Thomson Reuters (now Refinitiv), and as a Senior Quant Researcher at the StarMine Corporation, where she built global stock selection models including the design and implementation of the StarMine Short Interest model. Dr. Stauth holds a PhD in Biophysics from UC Berkeley, where her research focused on computational neuroscience.
Qu speaker series:Ethical Use of AI in Financial MarketsQuantUniversity
As AI and ML penetrates the financial industry, there are growing concerns about ethical use of AI in Finance. In this talk, Dan will focus on how the AI can be operationalized to help industry professionals and executive teams alike think about opportunities, risks as well as required actions factoring in ethics in our data-driven world.
Learn how artificial intelligence (AI) and machine learning are revolutionizing industries — this course will introduce key concepts and illustrate the role of machine learning, data science techniques, and AI through examples and case studies from the investment industry. The presentation uses simple mathematics and basic statistics to provide an intuitive understanding of machine learning, as used by firms, to augment traditional decision making.
https://quforindia.splashthat.com/
Robotics & Artificial (RAI) Intelligence webinar: Law & Regulation for RAI In...KTN
The Robotics & AI Innovation Network hosted a webinar addressing some of the legal and regulatory issues faced by the RAI community in the UK. Three legal experts provided their expertise to address these issues.
- Doug Bryden | Partner; Head of the Operational Risk & Environment Group, Travers Smith LLP
- Mark Richardson | Partner; IT, Telecoms and Electronics, Keltie
- Sébastien A. Krier | Founder & AI Ethics/Policy Expert, Dataphysix Ltd
Learn how Artificial Intelligence (“AI”) and Machine Learning (“ML”) are revolutionizing financial services
Introduction of key concepts and illustration of the role of ML, data science techniques, and AI through examples and case studies from the investment industry.
Uses simple math and basic statistics to provide an intuitive understanding of ML, as used by financial firms, to augment traditional investment decision making.
Careers in ML and AI and how professionals should prepare for careers in the 21st century, especially post Covid19.
This workshop will look into ways to create synthetic data from lending club loan record datasets alongside comparing characteristics and statistical properties of real and synthetic datasets. There will also be discussions into building machine learning models for predicting interest rates using real and synthetic datasets and evaluating the performance and discuss the advantages and disadvantages of using synthetic datasets as a proxy for real datasets
Frontiers in Alternative Data : Techniques and Use CasesQuantUniversity
QuantUniversity Summer School 2020 (https://qusummerschool.splashthat.com/)
https://quspeakerseries10.splashthat.com/
Lecture 1: Alexander Denev
In this talk, Alexander will introduce Alternative Data and discuss it's uses from his book, The Book of Alternative Data
- What is alternative data?
- Adoption of alternative data
- Information value chain
- Risks associated with alternative data
- Processes required to develop signals
- Valuation of alternative data
Lecture 2: Saeed Amen
In this talk, Saeed will discuss use cases in Alternative Data
-Deciphering Federal Reserve communications
- Using CLS flow data to trade FX
- Geospatial Insight satellite data to estimate retailers' EPS
- Saving "alpha" with transaction cost analysis
- Using Bloomberg News data to trade FX
Machine Learning in Finance: 10 Things You Need to Know in 2021QuantUniversity
Machine Learning and AI has revolutionized Finance! In the last five years, innovations in computing, technology and business models have created multiple products and services in Fintech prompting organizations to prioritize their data and AI strategies. What will 2021 bring and how should you prepare for it? Join Sri Krishnamurthy,CFA as we kickoff the QuantUniversity’s Winter school 2021. We will introduce you to the upcoming programs and have a masterclass on 10 innovations in AI and ML you need to know in 2021!
QU Summer school 2020 speaker Series - Session 7
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Managing Machine Learning Models in the Financial Industry
Lecture 1: Model Risk Management for AI and Machine Learning
Artificial intelligence and machine learning are part of today’s modeler’s toolbox for building challenger models and new innovative models that address business needs. However, AI presents new and unique challenges for risk management, particularly for assessing, controlling, and managing model risk for models of limited transparency. Another key consideration is the speed at which these models can be developed, validated, and then deployed into productive use to be competitive adhering to a robust model risk management program. This talk will highlight best practices for integrating AI into model risk practices and showcase examples across the model lifecycle.
The world has changed in the last six months with COVID-19! There have been a shakeup in business models and funding. As companies and customers change their behaviors, we are seeing changes on how companies are addressing new challenges.
Join Fintech experts, D.Shahrawat and Sarah Biller for a not to be missed conversation on Fintech in the Post-Covid age
Machine Learning: Considerations for Fairly and Transparently Expanding Acces...QuantUniversity
Machine Learning: Considerations for Fairly and Transparently Expanding Access to Credit
With Raghu Kulkarni and Steve Dickerson
Recently, machine learning has been used extensively in credit decision making. As ML proliferates the industry, issues of considerations for fair and transparent access to credit decision making is becoming important.
In this talk, Dr.Raghu Kulkarni and Dr.Steven Dickerson from Discover Financial Services will share their experiences at Discover. The talk will include:
- An overview of how ML models are used across financial life cycle
- Practical problems practitioners run into and why explainability and bias detection becomes important.
References:
1- https://www.h2o.ai/resources/white-paper/machine-learning-considerations-for-fairly-and-transparently-expanding-access-to-credit/
2- https://arxiv.org/abs/2011.03156
This workshop will look into ways to create synthetic data from lending club loan record datasets alongside comparing characteristics and statistical properties of real and synthetic datasets. There will also be discussions into building machine learning models for predicting interest rates using real and synthetic datasets and evaluating the performance and discuss the advantages and disadvantages of using synthetic datasets as a proxy for real datasets
The use of data science and machine learning in the investment industry is increasing. Financial firms are using artificial intelligence (AI) and machine learning to augment traditional investment decision making.
In this workshop, we aim to bring clarity on how AI and machine learning are revolutionizing financial services. We will introduce key concepts and, through examples and case studies, will illustrate the role of machine learning, data science techniques, and AI in the investment industry.
Agenda:
In Part 1, we will discuss key trends in AI and machine learning in the financial services industry, including the key use cases, challenges, and best practices.
In Part 2, we will illustrate two case studies where AI and machine learning techniques are applied in financial services.
Case studies:
Sentiment Analysis Using Natural Language Processing in Finance
In this case study, we will demonstrate the use of natural language processing techniques to analyze EDGAR call earnings transcripts that could be used to generate sentiment analysis scores using the Amazon Comprehend, IBM Watson, Google, and Azure APIs (application programming interfaces). We will illustrate how these scores can be used to augment traditional quantitative research and for trading decisions.
Credit Risk Decision Making Using Lending Club Data
In this case study, we will use the Lending Club data set to build a credit risk model using
machine learning techniques.
The use of Data Science and Machine learning in the investment industry is increasing, and investment professionals, both fundamental and quantitative, are taking notice. Financial firms are taking AI and machine learning seriously to augment traditional investment decision making. Alternative data sets including text analytics, cloud computing, and algorithmic trading are game changers for many firms who are adopting technology at a rapid pace. As more and more technologies penetrate enterprises, financial professionals are enthusiastic about the upcoming revolution and are looking for direction and education on data science and machine learning topics.
In this webinar, we aim to bring clarity to how AI and machine learning is revolutionizing financial services. We will introduce key concepts and through examples and case studies, we will illustrate the role of machine learning, data science techniques, and AI in the investment industry. At the end of this webinar, participants will see a concrete picture of how machine learning and AI techniques are fueling the Fintech wave!
The talk will have 3 parts. The overview of the practical applications of the AI and ML in the FinTech industry with a short explanation of the PSD2 directive and the disruption is caused. Application of the AI/ML from the perspective of the end-user, personal financial health, financial coach, etc. The overview of the architecture, technologies, and frameworks used with practical examples from the Zuper company.
Machine learning for factor investing - Tony Guida
https://quspeakerseries5.splashthat.com/
Topic: Machine Learning for Factor Investing: case study on "Trees"
In this presentation, Tony will first introduce the concept of supervised learning. Then he will cover the practitioner angle for constructing non linear multi factor signals using stock characteristics. He will show the added value of ML based signals over traditional linear stale factors blend in equity.
This master class is derived from Guillaume Coqueret and Tony Guida's latest book "Machine Learning for Factor Investing"
Artificial intelligent systems in finance have exploded over the last few years. Many institutions are struggling to leverage these new AI systems and machine learning approaches to risk management. This is particularly true for applications to risk models that are subject to regulatory scrutiny where transparency limits applications of these new approaches. Co-sponsored with PRMIA (Professional Risk Managers’ International Association), this session will provide an overview of the current state of applied machine learning and artificial intelligence for risk modeling and how it can be applied for monitoring risk and building new risk models.
An overview of Analytics Landscape
Structured and un-structured data
Key application areas
Instructors:
Mousum Dutta
Chief Data Scientist, Spotle.ai
Ex SAS
Computer Science, IIT KGP
Dr Avik Sarkar
Head, Data Analytics Cell, NITI Aayog
Officer on Special Duty, Govt of India
IIT Bombay
Rapid prototyping quant research ml models using the qu sandboxQuantUniversity
QU Summer school 2020 speaker Series - Session 7
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Managing Machine Learning Models in the Financial Industry
Synthetic data generation for machine learningQuantUniversity
As machine learning becomes more pervasive in the industry, data scientists and quants are realizing the challenges and limitations of machine learning models. One of the primary reasons machine learning applications fail is due to the lack of rich, diverse and clean datasets needed to build models. Datasets may have missing values, may not incorporate enough samples for all use cases (for example: availability of fraudulent transaction records to train a model) and may not be easily sharable due to privacy concerns. While there are many data cleansing techniques to fix data-related issues and we can always try and get new and rich datasets, the cost is at times prohibitive and at times impractical leading many institutions to abandon machine learning and go back to rule-based methods.
Synthetic data sets and simulations are used to enrich and augment existing datasets to provide comprehensive samples while training machine learning problems. In addition, synthetic datasets can be used for comprehensive scenario analysis, missing value filling and privacy protection of the datasets when building models. The advent of novel techniques like Deep Learning has rekindled interest in using techniques like GANs and Encoder-Decoder architectures in financial synthetic data generation.
In this workshop, we will discuss the state of the art in Synthetic data generation and will illustrate the various techniques and methods that can be used in practice. Through examples using QuSynthesize & QuSandbox, we will demonstrate how these techniques can be realized in practice.
Meg Mude, Intel - Data Engineering Lifecycle Optimized on Intel - H2O World S...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/cnU6sqd31JU
Developing meaningful AI applications requires complete data lifecycle management. Sourcing, harvesting, labelling and ensuring the conduit to consume data structures and repositories is critical for model accuracy....but, one of the least talked about subjects. Intel’s optimized technologies enable efficient delivery of complete data samples to develop (and deploy) meaningful outcomes. During this session, we’ll review the considerations and criticality of data lifecycle management for the AI production pipeline.
Bio: Meg brings more than 17 years of global product, engineering and solutions experience. She is presently a Solutions Architect with Intel Corporation specializing in Visual Compute and AAI (Analytics and AI) Architecture. She is passionate about the potential for technology to improve the quality of peoples’ lives and humanity on the whole.
How Data Virtualization Puts Machine Learning into Production (APAC)Denodo
Watch full webinar here: https://bit.ly/3mJJ4w9
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spend most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Attend this session to learn how companies can use data virtualization to:
- Create a logical architecture to make all enterprise data available for advanced analytics exercise
- Accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- Integrate popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc
Learn how Artificial Intelligence (“AI”) and Machine Learning (“ML”) are revolutionizing financial services
Introduction of key concepts and illustration of the role of ML, data science techniques, and AI through examples and case studies from the investment industry.
Uses simple math and basic statistics to provide an intuitive understanding of ML, as used by financial firms, to augment traditional investment decision making.
Careers in ML and AI and how professionals should prepare for careers in the 21st century, especially post Covid19.
This workshop will look into ways to create synthetic data from lending club loan record datasets alongside comparing characteristics and statistical properties of real and synthetic datasets. There will also be discussions into building machine learning models for predicting interest rates using real and synthetic datasets and evaluating the performance and discuss the advantages and disadvantages of using synthetic datasets as a proxy for real datasets
Frontiers in Alternative Data : Techniques and Use CasesQuantUniversity
QuantUniversity Summer School 2020 (https://qusummerschool.splashthat.com/)
https://quspeakerseries10.splashthat.com/
Lecture 1: Alexander Denev
In this talk, Alexander will introduce Alternative Data and discuss it's uses from his book, The Book of Alternative Data
- What is alternative data?
- Adoption of alternative data
- Information value chain
- Risks associated with alternative data
- Processes required to develop signals
- Valuation of alternative data
Lecture 2: Saeed Amen
In this talk, Saeed will discuss use cases in Alternative Data
-Deciphering Federal Reserve communications
- Using CLS flow data to trade FX
- Geospatial Insight satellite data to estimate retailers' EPS
- Saving "alpha" with transaction cost analysis
- Using Bloomberg News data to trade FX
Machine Learning in Finance: 10 Things You Need to Know in 2021QuantUniversity
Machine Learning and AI has revolutionized Finance! In the last five years, innovations in computing, technology and business models have created multiple products and services in Fintech prompting organizations to prioritize their data and AI strategies. What will 2021 bring and how should you prepare for it? Join Sri Krishnamurthy,CFA as we kickoff the QuantUniversity’s Winter school 2021. We will introduce you to the upcoming programs and have a masterclass on 10 innovations in AI and ML you need to know in 2021!
QU Summer school 2020 speaker Series - Session 7
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Managing Machine Learning Models in the Financial Industry
Lecture 1: Model Risk Management for AI and Machine Learning
Artificial intelligence and machine learning are part of today’s modeler’s toolbox for building challenger models and new innovative models that address business needs. However, AI presents new and unique challenges for risk management, particularly for assessing, controlling, and managing model risk for models of limited transparency. Another key consideration is the speed at which these models can be developed, validated, and then deployed into productive use to be competitive adhering to a robust model risk management program. This talk will highlight best practices for integrating AI into model risk practices and showcase examples across the model lifecycle.
The world has changed in the last six months with COVID-19! There have been a shakeup in business models and funding. As companies and customers change their behaviors, we are seeing changes on how companies are addressing new challenges.
Join Fintech experts, D.Shahrawat and Sarah Biller for a not to be missed conversation on Fintech in the Post-Covid age
Machine Learning: Considerations for Fairly and Transparently Expanding Acces...QuantUniversity
Machine Learning: Considerations for Fairly and Transparently Expanding Access to Credit
With Raghu Kulkarni and Steve Dickerson
Recently, machine learning has been used extensively in credit decision making. As ML proliferates the industry, issues of considerations for fair and transparent access to credit decision making is becoming important.
In this talk, Dr.Raghu Kulkarni and Dr.Steven Dickerson from Discover Financial Services will share their experiences at Discover. The talk will include:
- An overview of how ML models are used across financial life cycle
- Practical problems practitioners run into and why explainability and bias detection becomes important.
References:
1- https://www.h2o.ai/resources/white-paper/machine-learning-considerations-for-fairly-and-transparently-expanding-access-to-credit/
2- https://arxiv.org/abs/2011.03156
This workshop will look into ways to create synthetic data from lending club loan record datasets alongside comparing characteristics and statistical properties of real and synthetic datasets. There will also be discussions into building machine learning models for predicting interest rates using real and synthetic datasets and evaluating the performance and discuss the advantages and disadvantages of using synthetic datasets as a proxy for real datasets
The use of data science and machine learning in the investment industry is increasing. Financial firms are using artificial intelligence (AI) and machine learning to augment traditional investment decision making.
In this workshop, we aim to bring clarity on how AI and machine learning are revolutionizing financial services. We will introduce key concepts and, through examples and case studies, will illustrate the role of machine learning, data science techniques, and AI in the investment industry.
Agenda:
In Part 1, we will discuss key trends in AI and machine learning in the financial services industry, including the key use cases, challenges, and best practices.
In Part 2, we will illustrate two case studies where AI and machine learning techniques are applied in financial services.
Case studies:
Sentiment Analysis Using Natural Language Processing in Finance
In this case study, we will demonstrate the use of natural language processing techniques to analyze EDGAR call earnings transcripts that could be used to generate sentiment analysis scores using the Amazon Comprehend, IBM Watson, Google, and Azure APIs (application programming interfaces). We will illustrate how these scores can be used to augment traditional quantitative research and for trading decisions.
Credit Risk Decision Making Using Lending Club Data
In this case study, we will use the Lending Club data set to build a credit risk model using
machine learning techniques.
The use of Data Science and Machine learning in the investment industry is increasing, and investment professionals, both fundamental and quantitative, are taking notice. Financial firms are taking AI and machine learning seriously to augment traditional investment decision making. Alternative data sets including text analytics, cloud computing, and algorithmic trading are game changers for many firms who are adopting technology at a rapid pace. As more and more technologies penetrate enterprises, financial professionals are enthusiastic about the upcoming revolution and are looking for direction and education on data science and machine learning topics.
In this webinar, we aim to bring clarity to how AI and machine learning is revolutionizing financial services. We will introduce key concepts and through examples and case studies, we will illustrate the role of machine learning, data science techniques, and AI in the investment industry. At the end of this webinar, participants will see a concrete picture of how machine learning and AI techniques are fueling the Fintech wave!
The talk will have 3 parts. The overview of the practical applications of the AI and ML in the FinTech industry with a short explanation of the PSD2 directive and the disruption is caused. Application of the AI/ML from the perspective of the end-user, personal financial health, financial coach, etc. The overview of the architecture, technologies, and frameworks used with practical examples from the Zuper company.
Machine learning for factor investing - Tony Guida
https://quspeakerseries5.splashthat.com/
Topic: Machine Learning for Factor Investing: case study on "Trees"
In this presentation, Tony will first introduce the concept of supervised learning. Then he will cover the practitioner angle for constructing non linear multi factor signals using stock characteristics. He will show the added value of ML based signals over traditional linear stale factors blend in equity.
This master class is derived from Guillaume Coqueret and Tony Guida's latest book "Machine Learning for Factor Investing"
Artificial intelligent systems in finance have exploded over the last few years. Many institutions are struggling to leverage these new AI systems and machine learning approaches to risk management. This is particularly true for applications to risk models that are subject to regulatory scrutiny where transparency limits applications of these new approaches. Co-sponsored with PRMIA (Professional Risk Managers’ International Association), this session will provide an overview of the current state of applied machine learning and artificial intelligence for risk modeling and how it can be applied for monitoring risk and building new risk models.
An overview of Analytics Landscape
Structured and un-structured data
Key application areas
Instructors:
Mousum Dutta
Chief Data Scientist, Spotle.ai
Ex SAS
Computer Science, IIT KGP
Dr Avik Sarkar
Head, Data Analytics Cell, NITI Aayog
Officer on Special Duty, Govt of India
IIT Bombay
Rapid prototyping quant research ml models using the qu sandboxQuantUniversity
QU Summer school 2020 speaker Series - Session 7
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Managing Machine Learning Models in the Financial Industry
Synthetic data generation for machine learningQuantUniversity
As machine learning becomes more pervasive in the industry, data scientists and quants are realizing the challenges and limitations of machine learning models. One of the primary reasons machine learning applications fail is due to the lack of rich, diverse and clean datasets needed to build models. Datasets may have missing values, may not incorporate enough samples for all use cases (for example: availability of fraudulent transaction records to train a model) and may not be easily sharable due to privacy concerns. While there are many data cleansing techniques to fix data-related issues and we can always try and get new and rich datasets, the cost is at times prohibitive and at times impractical leading many institutions to abandon machine learning and go back to rule-based methods.
Synthetic data sets and simulations are used to enrich and augment existing datasets to provide comprehensive samples while training machine learning problems. In addition, synthetic datasets can be used for comprehensive scenario analysis, missing value filling and privacy protection of the datasets when building models. The advent of novel techniques like Deep Learning has rekindled interest in using techniques like GANs and Encoder-Decoder architectures in financial synthetic data generation.
In this workshop, we will discuss the state of the art in Synthetic data generation and will illustrate the various techniques and methods that can be used in practice. Through examples using QuSynthesize & QuSandbox, we will demonstrate how these techniques can be realized in practice.
Meg Mude, Intel - Data Engineering Lifecycle Optimized on Intel - H2O World S...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/cnU6sqd31JU
Developing meaningful AI applications requires complete data lifecycle management. Sourcing, harvesting, labelling and ensuring the conduit to consume data structures and repositories is critical for model accuracy....but, one of the least talked about subjects. Intel’s optimized technologies enable efficient delivery of complete data samples to develop (and deploy) meaningful outcomes. During this session, we’ll review the considerations and criticality of data lifecycle management for the AI production pipeline.
Bio: Meg brings more than 17 years of global product, engineering and solutions experience. She is presently a Solutions Architect with Intel Corporation specializing in Visual Compute and AAI (Analytics and AI) Architecture. She is passionate about the potential for technology to improve the quality of peoples’ lives and humanity on the whole.
How Data Virtualization Puts Machine Learning into Production (APAC)Denodo
Watch full webinar here: https://bit.ly/3mJJ4w9
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spend most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Attend this session to learn how companies can use data virtualization to:
- Create a logical architecture to make all enterprise data available for advanced analytics exercise
- Accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- Integrate popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc
How Data Virtualization Puts Enterprise Machine Learning Programs into Produc...Denodo
Watch full webinar here: https://bit.ly/3offv7G
Presented at AI Live APAC
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spend most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Watch this on-demand session to learn how companies can use data virtualization to:
- Create a logical architecture to make all enterprise data available for advanced analytics exercise
- Accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- Integrate popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc.
Top 10 Most Demand IT Certifications Course in 2020 - MildainTrainingsMildain Solutions
The professionals in the field of Information Technology understands the importance of certification to their career and growth.
The information provided in this guide is backed by real data. Let us look at the top IT certifications that will remain to be a trend in 2020.
Mildaintrainings https://mildaintrainings.com/ offers Several trainings all over the world.
Nadine Schöne, Dataiku. The Complete Data Value Chain in a NutshellIT Arena
Dr. Nadine Schöne is a Senior Solutions Architect at Dataiku in Berlin. In this role, she deals with all aspects of the data value chain for all users – including integration of data sources, ETL, cooperation, statistics, modelling, but also operationalization, monitoring, automatization and security during production. She regularly talks at conferences, holds webinars and writes articles.
Speech Overview:
How can you get the most out of your data – while staying flexible in your choice of infrastructure and without having to integrate a multitude of tools for the different personas involved? Maximizing the value you get out of your data is a necessity today. Looking at the whole picture as well as careful planning are the key for success. We will have a look at the complete data value chain from end to end: from the data stores, collaboration features, data preparation, visualization and automation capabilities, and external compute to scheduling, operationalization, monitoring and security.
London atlassian meetup 31 jan 2016 jira metrics-extract slidesRudiger Wolf
Slides for talk given to London Atlassian User Group Jan 2017. How to get started with Python to extract data from Jira and produce charts for your Agile team.
R, Spark, Tensorflow, H20.ai Applied to Streaming AnalyticsKai Wähner
Slides from my talk at Codemotion Rome in March 2017. Development of analytic machine learning / deep learning models with R, Apache Spark ML, Tensorflow, H2O.ai, RapidMinder, KNIME and TIBCO Spotfire. Deployment to real time event processing / stream processing / streaming analytics engines like Apache Spark Streaming, Apache Flink, Kafka Streams, TIBCO StreamBase.
How to Leverage Machine Learning (R, Hadoop, Spark, H2O) for Real Time Proces...Codemotion
Big Data is key for innovation in many industries today. Large amounts of historical data are stored and analyzed in Hadoop, Spark or other clusters to find patterns, e.g. for predictive maintenance or cross-selling. However: How do you increase revenue or reduce risks in new transactions proactively? Stream processing is the solution to embed patterns into future actions in real-time. This session discusses and demos how machine learning and analytic models with R, Spark MLlib, H2O, etc. can be build and integrated into real-time event processing frameworks. The session focuses on live demos
Maximize Big Data ROI via Best of Breed Patterns and PracticesJeff Bertman
******** Abstract: ********
Not long ago the question was whether your organization had big data. Did you have
the volume, the velocity, the technology. Now those basics are largely given for most of
the people attending this event. The path to success is still fuzzy, however, with so many
technologies to choose from – and so many ways to use them.
This presentation triangulates in a holistic manner on the modern business dilemma:
how can we leverage technology to improve revenue, profit, market share, and numerous
other success criteria. That said, this is not about the analytics or KPIs -- although it is
about measurable improvement. It’s about lining up the right technologies and using them
in effective, proven ways to maximize Return on Investment (ROI). Since the slant here
is holistic, we’ll show how to blend infrastructure, tools, methods, and talent to avoid and
constantly trim technical debt… and to produce success stories that are consistently
repeatable, not a byproduct of individual heroics.
Pinterest - Big Data Machine Learning Platform at PinterestAlluxio, Inc.
This was presented by the Yongsheng Wu, head of big data and ML platform at Pinterest, at the Alluxio bay area meetup.
Yongsheng shares Pinterest's journey to build a fast and scalable big data and ML platform in AWS for Pinterest to handle the requests and complexity in data at scale. In this talk, he will cover different aspects from the requirements of the platform, the challenges encountered, the technologies chosen, and the tradeoffs that were made.
Voxxed Days Cluj - Powering interactive data analysis with Google BigQueryMárton Kodok
Every company,
no matter how far from the tech they are,
is evolving into a software company,
and by extension a data company.
For a small company it’s important
to have access to modern BigData tools
without running a dedicated team for it.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
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We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
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We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
1. Engineering @ Exzeo
Building Data Science
Pipelines in Python
Pydata Delhi Meetup
Exzeo, Noida
Feb 10, 2018
Shivam Bansal
Shwet Kamal Mishra
2. Contents
● Introduction
● Typical Data Science Workflow
● Challenges in the Data Science Workflow
● Data Science Pipelines
● Why use a Data Science Pipeline
● Luigi - Pipeline in python
● Luigi Features
● Luigi Demo
3. Who We are
Exzeo is a software development company specialized in core tech products
and services that optimize human capital
It was registered with Registrar of Companies on 9th August 2012.
We are a part of HCI group (NYSE: HCI) , a multinational conglomerate based
at Tampa, FL,USA.
The key focus of Exzeo is to improve the Insurance Sector using technology,
analytics and data science
4. Our Products and Services
ATLAS VIEWER
A data visualization product to view real-time feeds
and massive datasets on a map.
EXZEO HQ
Cloud based process management and Intelligent
automation for the insurance industry.
PROPLET
Innovative policy quoting application leveraging
multiple proprietary data sources.
TYPTAP
A complete, quick and secure platform to access
user’s insurance policies, and loss information
JUSTER
An intelligent app which helps to organize the claim
inspections and sync information with Exzeo Cloud.
HARMONY
Project Harmony offers insurance solutions; right
from buying a policy to filing a claim.
13. Why use a pipeline
- Reuse the models
- Quick Implementation of Ideas
- Focus more on science instead of engineering
- Production ready products
14. Pipelines in Python - Luigi
● Python tool for workflow task management
● Developed and maintained by Spotify
● Open Source: https://github.com/spotify/luigi
pip install luigi
15. What’s so special about Luigi
● Tasks Templating
● Tasks Scheduling
● Tasks Monitoring
● Command Line Integration
● Batch and Parallel Processing
● Dependency Graphs
● Failure Recovery and Error Emails
20. Problem Statement:
Building a Pipeline to predict the Performance Score of a mobile game user.
The game consists of 120 different characters(heroes) and every hero has some capabilities.
Input Data
Training Data: User score for given characters
Independent Variables: User ID, Character ID, User-Character ID, Num Tries, Boost Used(0/1),
Attack Duration
Dependent Variable: Performance Score
Character Metadata: Data of each character
Variables: Character ID, Character Type, Hitpoints
21. Solution Pipeline
● Load Data
● Aggregate Data
● PreProcess Data
● Model Training
● Linear Regression
● Random Forest
● Model Selection
● Model Prediction