Amazon Web Services (AWS) contributes novel causal machine learning algorithms to DoWhy. . .

Source: https://www.linkedin.com/feed/update/urn%3Ali%3Ashare%3A6937471434118111232

Amazon Web Services (AWS) contributes novel causal machine learning algorithms to DoWhy Python library: https://lnkd.in/gAedBWEJ:
New features go beyond conventional effect estimation by attributing events to individual components of complex systems.
#Causality #Models #Math

We are excited to announce that we are #opensourcing #causal #machinelearning ( #ML ) #algorithms that are the result of years of #Amazon research on #graphical #causalmodels. The algorithms enable a variety of #complex #causal #queries in addition to the usual #effect #estimation, including but not limited to root-cause #analysis of #outliers and #distribution #changes, #causal-#structure #learning, and #diagnosis of #causal #structures. Internally, they have been used by #Amazon teams ranging from #SupplyChain to #AWS.

We are also excited that, in a joint effort with #Microsoft, we have created a new #GitHub organization called #PyWhy. PyWhy serves as the new home of #DoWhy, a #causal #ML #library from Microsoft, which we are merging our #algorithms into. DoWhy is one of the most popular causality libraries on GitHub. Amazon and Microsoft are delighted to be working together with the #DoWhy #community.

IN-DEPTH REFERENCE: #Graphical #causal #models: https://lnkd.in/gMVEpcEn

Most real-world systems, be they #distributed-#computing #systems, #supply #chain #systems, or #manufacturing #processes, can be described using variables that may or may not exert #causal #influence on each other.

Think, for instance, of a #microservice #architecture consisting of many different #web #services. What is the cause of increased #website #loading times? Is it a slow #database in the back end? A malfunctioning #loadbalancer? A slow #network?

GCMs are a formal #framework developed by #Turing #Award winner #JudeaPearl to model cause-effect relationships between variables in a system. A key ingredient of GCMs is the #causal #diagrams, which visually represent the #cause-#effect #relationships among the observed #variables, with an arrow from a #cause to its #effect.

AWS Cloud Network Partner: Silicon Valley-Wall Street-Pentagon: Global Risk Management Network LLC:

• Building #Causal #Structural #Equation #Models Applied by #Global #Organizations such as #NASA and #Big #Banks Since early 1990s: R&D Impact Ranked among AI-Quant-FinTech Nobel Laureates: https://lnkd.in/epx6zV3

• Global #ComplexSystems & #ChaosTheory Practices #Pioneer Since early 1990s:
#SelfAdaptive & #ComplexSystems World #Industry #Practices #Leader On Advancing #MachineLearning & #DeepLearning #Systems: https://lnkd.in/gcYqAWqm:

AWS Cloud Network Partner: Silicon Valley-Wall Street-Pentagon: Global Risk Management Network LLC
We create the Digital Future™… And You Can Too!
Know-Build-Monetize™: https://lnkd.in/gMR596dj
We have been Doing So Since the Beginning of the WWW…
Let’s Do It Together! ABC:
AIMLExchange.com BRINT.com C4I-Cyber.com

Amazon Web Services (AWS) contributes novel causal machine learning algorithms to DoWhy Python library: https://lnkd.in/gAedBWEJ: New features go beyond conventional effect estimation by attributing events to individual components of complex systems. #Causality #Models #Math "We are excited to announce that we are #opensourcing #causal #machinelearning ( #ML ) #algorithms that are the result of years of #Amazon research on #graphical #causalmodels. The algorithms enable a variety of #complex #causal #queries in addition to the usual #effect #estimation, including but not limited to root-cause #analysis of #outliers and #distribution #changes, #causal-#structure #learning, and #diagnosis of #causal #structures. Internally, they have been used by #Amazon teams ranging from #SupplyChain to #AWS." "We are also excited that, in a joint effort with #Microsoft, we have created a new #GitHub organization called #PyWhy. PyWhy serves as the new home of #DoWhy, a #causal #ML #library from Microsoft, which we are merging our #algorithms into. DoWhy is one of the most popular causality libraries on GitHub. Amazon and Microsoft are delighted to be working together with the #DoWhy #community." IN-DEPTH REFERENCE: #Graphical #causal #models: https://lnkd.in/gMVEpcEn "Most real-world systems, be they #distributed-#computing #systems, #supply #chain #systems, or #manufacturing #processes, can be described using variables that may or may not exert #causal #influence on each other." "Think, for instance, of a #microservice #architecture consisting of many different #web #services. What is the cause of increased #website #loading times? Is it a slow #database in the back end? A malfunctioning #loadbalancer? A slow #network?" "GCMs are a formal #framework developed by #Turing #Award winner #JudeaPearl to model cause-effect relationships between variables in a system. A key ingredient of GCMs is the #causal #diagrams, which visually represent the #cause-#effect #relationships among the observed #variables, with an arrow from a #cause to its #effect." AWS Cloud Network Partner: Silicon Valley-Wall Street-Pentagon: Global Risk Management Network LLC: • Building #Causal #Structural #Equation #Models Applied by #Global #Organizations such as #NASA and #Big #Banks Since early 1990s: R&D Impact Ranked among AI-Quant-FinTech Nobel Laureates: https://lnkd.in/epx6zV3 • Global #ComplexSystems & #ChaosTheory Practices #Pioneer Since early 1990s: #SelfAdaptive & #ComplexSystems World #Industry #Practices #Leader On Advancing #MachineLearning & #DeepLearning #Systems: https://lnkd.in/gcYqAWqm: AWS Cloud Network Partner: Silicon Valley-Wall Street-Pentagon: Global Risk Management Network LLC We create the Digital Future™... And You Can Too! Know-Build-Monetize™: https://lnkd.in/gMR596dj We have been Doing So Since the Beginning of the http://WWW... Let's Do It Together! ABC: AIMLExchange.com BRINT.com C4I-Cyber.com
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Global Post AI-Quantum Finance & Trading Networks Pioneer Dr.-Eng.-Prof. Yogesh Malhotra is the “Singular Post AI-Quantum Pioneer” identified by Grok AI with R&D impact recognized among Artificial Intelligence (AI) and Quantitative Finance Nobel Laureates. As MIT-Princeton AI-ML-Cyber-Crypto-Quantum Finance & Trading and FinTech-Crypto Faculty-Industry Expert, and U.S. and Global Hedge Funds Advisory & Venture Capital CEO-CTO Teams Mentor, he has pioneered Silicon Valley-Wall Street-Pentagon Digital CEO-CTO Practices, Technologies, and Networks from world’s first-foremost-largest Global Digital Transformation Networks to New York State IDEA Award recognized Pentagon-USAF MVP Global Post AI-Quantum Networks pioneering Future of Finance and Trading practices as Trillion-Dollar Wall Street Hedge Funds and Investment Banks leader.