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Thank you for subscribing to Smash Notes weekly. Every update is a little different. If you see something you particularly like, please let me know. Click to see all the top choices for this week in one place. What is in this update? In no particular order:   Business:  Why would a multi billion dollar business be suing a small coffee shop, and what does it cost to defend yourself from a frivolous lawsuit? Startups:  Working on an idea, are you? What should you validate first? Ryan Hoover from Product Hunt shares his wisdom. It ain't easy. History: What's the big deal with the Second Amendment? Should we all have a gun already? Adam Conover started a new podcast called "Factually!" and on this episode he's invited a guest to talk about the Constitution, gun rights, and other fun history  things. Education: How do you educate your kids if you are a Billionaire? Josh Dahn, the head of Ad Astra, the school founded by Elon Musk, talks about their approach to creative teaching. It's fascinating, and I would almost work for Space X just to have my kids go there, almost. Machine Learning: Microsoft is tired of paying big bucks for data processing and is looking into new ways to do machine learning. They are calling it "Machine Teaching." What is it and why is it so much better?

MLOps community meetup #49! Last Wednesday we talked to Lak Lakshmanan, Data Analytics and AI Solutions, Google Cloud. // Abstract: Design patterns are formalized best practices to solve common problems when designing a software system. As machine learning moves from being a research discipline to a software one, it is useful to catalogue tried-and-proven methods to help engineers tackle frequently occurring problems that crop up during the ML process. In this talk, I will cover five patterns (Workflow Pipelines, Transform, Multimodal Input, Feature Store, Cascade) that are useful in the context of adding flexibility, resilience and reproducibility to ML in production. For data scientists and ML engineers, these patterns provide a way to apply hard-won knowledge from hundreds of ML experts to your own projects. Anyone designing infrastructure for machine learning will have to be able to provide easy ways for the data engineers, data scientists, and ML engineers to implement these, and other, design patterns. // Bio: Lak is the Director for Data Analytics and AI Solutions on Google Cloud. His team builds software solutions for business problems using Google Cloud's data analytics and machine learning products. He founded Google's Advanced Solutions Lab ML Immersion program and is the author of three O'Reilly books and several Coursera courses. Before Google, Lak was a Director of Data Science at Climate Corporation and a Research Scientist at NOAA. ----------- Connect With Us ✌️-------------    Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Lak on LinkedIn: https://www.linkedin.com/in/valliappalakshmanan/ Timestamps: [00:00] TWIML Con Debate announcement to be hosted by Demetrios on Friday [00:19] Should data scientists know about Kubernetes? Is it just one machine learning tool to rule them all? Or is it going to be the "best-in-class" tool? [00:35] Strong opinion of Lak about "Should data scientists know about Kubernetes?" [05:50] Lak's background into tech [08:07] Which ones you wrote in the book? Is the airport scenario yours? [09:25] Did you write ML Maturity Level from Google? [12:34] How do you know when to bring on perplexity for the sake of making things easier? [16:06] What are some of the best practices that you've seen being used in tooling?   [20:09] How did you come up with writing the book? [20:59] How did we decide that these are the patterns that we need to put in the book? [24:14] Why did I get the "audacity" to think that this is something that is worth doing? [31:29] What would be in your mind some of the hierarchy of design patterns? [38:05] Are there patterns out there that are yet to be discovered? How do you balance the exploitable vs the explorable ml patterns? [42:08] ModelOps vs MLOps [43:08] Do you feel that a DevOps engineer is better suited to make the transition into becoming a Machine Learning engineer? [46:07] Fundamental Machine Design Patterns vs Software Development Design Patterns [49:23] When you're working with the companies at Google, did you give them a toolchain and a better infrastructure or was there more to it? Did they have to rethink their corporate culture because DevOps is often mistaken as just a pure toolchain?

This is the first episode of a podcast series on Machine Learning and Data privacy.  Machine Learning is the key to the new revolution in many industries. Nevertheless, ML does not exist without data and a lot of it, which in many cases results in the use of sensitive information. With new privacy regulations, access to data is today harder and much more difficult but, does that mean that ML and Data Science has its days counted? Will the Machines beat privacy?   Don't forget to subscribe to the mlops.community slack ( https://go.mlops.community/slack ) and to give a star to the Synthetic data open-source repo ( https://github.com/ydataai/ydata-synt... ) Useful links:   Medium post with the podcast transcription - https://medium.com/@fabiana_clemente/... In case you're curious about GDPR fines - enforcementtracker.com   The Netflix Prize - https://www.nytimes.com/2010/03/13/technology/13netflix.html Tensorflow privacy - https://github.com/tensorflow/privacy

**AI and ethical dilemmas** Artificial Intelligence is seen by many as a vehicle for great transformation, but for others, it still remains a mystery, and many questions remain unanswered: will AI systems rule us one day? Can we trust AI to rule our criminal systems? Maybe create political campaigns and dominate political advertisements? Or maybe something less harmful, do our laundry? Some of these questions may sound absurd, but they are for sure making people shift from thinking purely about functional AI capabilities but also to look further to the ethics behind creating such powerful solutions.   For this episode we count with Charles Radclyffe as a guest, the data philosopher, to cover some of these dilemmas. You can reach out to Charles through LinkedIn or at ethicsgrade.io   Useful links:   - MLOps.Community slack - TEDx talk - Surviving the Robot Revolution - Digital Ethics whitepaper

The rapid shift in priorities among today's enterprises is leaving security professionals applying these zero trust- \"trust no-one, verify everything\"- principles to existing on-premises networks. In this episode, Sean's talking with Hed...

In this episode I am going to read Newfound's latest research paper, LIQUIDITY CASCADES: The Coordinated Risk of Uncoordinated Market Participants.This reading will refer to a number of figures within the paper, so I urge you to go to our website, thinknewfound.com, and download the PDF so you get better follow along.This paper is unlike any research we've shared in the past. Within we dive into the circumstantial evidence surrounding the \"weird\" behavior many investors believe markets are exhibiting. We tackle narratives such as the impact of central bank intervention, the growing scale of passive / indexed investing, and asymmetric liquidity provisioning.Spoiler: Individually, the evidence for these narratives may be nothing more than circumstantial. In conjunction, however, they share pro-cyclical patterns that put pressure upon the same latent risk: liquidity.In the last part of the paper we discuss some ideas for how investors might try to build portfolios that can both seek to exploit these dynamics as well as remain resilient to them.I hope you enjoy.

Tweet this Episode Tyler Renelle is a contractor and developer who has worked in various web technologies like Node, Angular, Rails, and much more. He's also build machine learning backends in Python (Flask), Tensorflow, and Neural Networks. The JavaScript Jabber panel dives into Machine Learning with Tyler Renelle. Specifically, they go into what is emerging in machine learning and artificial intelligence and what that means for programmers and programming jobs. This episode dives into: ? Whether machine learning will replace programming jobs ? Economic automation ? Which platforms and languages to use to get into machine learning ? and much, much more... Links: ? Raspberry Pi ? Arduino ? Hacker News ? Neural Networks (wikipedia) ? Deep Mind ? Shallow Algorithms ? Genetic Algorithms ? Crisper gene editing ? Wix ? thegrid.io ? Codeschool ? Codecademy ? Tensorflow ? Keras ? Machine Learning Guide ? Andrew Ng Coursera Course ? Python ? R ? Java ? Torch ? PyTorch ? Caffe ? Scikit learn ? Tensorfire ? DeepLearn.js ? The Singularity is Near by Ray Kurzweil ? Tensorforce ? Super Intelligence by Nick Bostrom Picks: Aimee ? Include media ? Nodevember ? Phone cases AJ ? Data Skeptic ? Ready Player One Joe ? Everybody Lies Tyler ? Ex Machina ? Philosophy of Mind: Brains, Consciousness, and Thinking Machines

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Objective: To map the evidence concerning the use of machine learning (ML) to predict treatment burden and outcomes in patients with nAMD, DMO, and RVO-associated macular oedema in order to understand the current state of the art and the gaps. Introduction: Neovascular age-related macular degeneration (nAMD), diabetic macular oedema (DMO), and retinal vein occlusion (RVO) associated macular oedema are some of the leading causes of visual loss globally.1 Intravitreal anti-vascular endothelial growth factor (VEGF) injections are currently used to treat such diseases.1 The treatment course and outcomes for each patient is variable and thus individualised treatment techniques are needed.2 Two of the most pressing issues are estimating treatment burden (e.g., patients who need fewer vs. more intravitreal injections) and visual prognosis (e.g., good vs. poor responders) in individual patients.2 This research aims to assess the potential of ML to predict treatment burden and outcomes to personalise anti-VEGF treatment in patients with nAMD, DMO, and RVO. We seek to understand the types of ML approaches that have been explored, their maturity, reported outcomes, and real-world applicability. Eligibility criteria: Studies will be included if they focus on ML algorithms that predict anti-VEGF treatment burden and/or outcomes in patients with nAMD, DMO, and RVO-associated macular oedema. The included studies will consist of full-length original research journal articles written or translated into English. We will exclude other types of literature (i.e., not full-length journal articles, not a report of original research and not centred on ML algorithms that predict anti-VEGF treatment burden and/or outcomes in patients with nAMD, DMO, and RVO-associated macular oedema). Publication dates of included studies will be from January 2000 onwards. Methods: Five academic databases will be searched (Ovid Medline, Embase, Web of Science Core Collection, IEEE, and ACM Digital Library). Search strings for each database were formulated in conjunction with a Massachusetts Eye and Ear, Harvard Medical School librarian. Studies will be uploaded to Covidence (literature management software), and duplicates will be removed. Studies will be screened by two independent screeners (SG & AYO). Disagreements will be resolved with a discussion with a third author (DM). SG will conduct a descriptive analysis of the data. We will use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) for reporting our study, and we will publish our protocol on the Open Science Framework (OSF).3 Results: We will report the number and type of included evidence, as well as any pertinent study characteristics. Main topics of interest and significant outcomes will also be presented. Inferences will be presented in an appropriate diagrammatic or tabular format. Conclusions: Based on the key concepts and topics extrapolated from the selected studies, the scoping review will aim to explore the breadth and depth of the current literature and address any knowledge gaps that can hold the potential to inform future research and future algorithm development. Keywords: Intravitreal injections, ophthalmology, artificial intelligence, systematic scoping review

ELI5: Explain Like I'm Five Year Old, More than anything we learn, the actual intuition frames up stronger when we could deliver to a 5-year-old. In this Unicast of ELI5, amplifying the content from the Book Grokking Machine Learning, Beautifully Unfolded the intuition behind Predictions and Machine learning with a cute story of a kid. Grab Grokking Machine Learning by Luis Serrano | Manning Publications . Start your free trial at sundog-education.com to kickstart your career in Data Science.

In this episode we discuss just one out of the 102 different posters which was presented on the first night of the 2015 Neural Information Processing Systems Conference. This presentation describes a system which can answer simple questions about images. Check out: www.learningmachines101.com for additional details!!

This is the first of a two-parter with Peter Varhol on both the promises and the hype surrounding AI and Machine Learning. Matt, Perze and Michael go down the rabbit hole on the Machine Learning topic with Peter as we try to wrap our heads around both the realities of Machine Learning, AI and the unique testing challenges such systems offer. From Facebook's Chatbots negotiating an agreement to systems making predictive suggestions in ways that are both intriguing and creepy. There is a lot to the machine learning puzzle that we are just starting to understand and also prepare ourselves to effectively test. Hint: the algorithms themselves are only part of the puzzle. Resource by QualiTest Group

Application Programming Interfaces (APIs) are interfaces that enable multiple software applications to send and retrieve data from one another. They are commonly used for retrieving, saving, editing, or deleting data from databases, transmitting data between apps, and embedding third-party services into apps. The company BaseTen helps companies build and deploy machine learning APIs and applications.

By: Kalyan Munjuluri & Karishma Babbar Event: PyConZA 2021 URL: https://za.pycon.org/talks/23-machine-learning-lifecycle-made-easy-with-mlflow/ ABSTRACT Beyond the usual concerns in software development, machine learning development comes with additional challenges. These include trying multiple algorithms and parameters to get the best results, tracking these runs for reproducibility, and moving the model to diverse deployment environments. This talk demonstrates the use of an open-source platform called MLflow for managing the complete machine learning lifecycle with Python. The talk requires a basic understanding of Python and Machine Learning concepts. DESCRIPTION In theory, the crux of machine learning (ML) development lies with data collection, model creation, model training, and deployment. In reality, machine learning projects are not so straightforward. They are a cycle iterating between improving the data, model, and evaluation that is never really finished. Unlike in traditional software development, ML developers experiment with multiple algorithms, tools, and parameters to optimize performance, and they need to track these experiments to reproduce work. Furthermore, developers need to use many distinct systems to productionize models. In this talk, we introduce MLflow, an open-source platform that aims at simplifying the entire ML lifecycle where we can use any ML library and development tool of our choice to reliably build and share ML applications. MLflow offers simple abstractions through lightweight APIs to package reproducible projects, track results, and encapsulate models that are compatible with existing tools, thereby, accelerating ML lifecycle of any size. With the help of an example, we will show how using MLflow can ease bookkeeping of experiment runs and results across frameworks, quickly reproducing runs on any platform (cloud or local execution), and productionizing models on diverse deployment tools. At the end of this talk, you will be familiar with – Key concepts, abstractions, and components of open-source MLflow How each component of MLflow addresses challenges of ML lifecycle How to use MLflow Tracking during model training to record experimental runs How to use MLflow Tracking User Interface to visualize experimental runs with different tuning parameters and evaluation metrics How to use MLflow Projects for packaging reusable and reproducible models How to use MLflow Models general format to serve models using MLflow REST API The purpose of the session is to introduce the audience to MLflow and give a taste of the ML development lifecycle. It is intended at providing a breadth than depth survey of MLflow platform, and we leave the audience to experiment with it further through takeaway exercises. PRE-REQUISITES Basic knowledge of Python programming language Basic understanding of machine learning concepts TRACK Data Science in Production, Machine Learning, Data Engineering or MLOps Room: Video Room 2 Scheduled start: 2021-10-08 14:30:00 Sponsors: Gold: SPAN Digital: https://spandigital.com/ Takealot: http://takealot.com/ Andela: https://www.andela.com/ Silver: Python Software Foundation: https://www.python.org/psf/membership OfferZen: https://www.offerzen.com/ Patron: Thinkst Canary: https://canary.tools/ Afrolabs: http://www.afrolabs.co.za/

Addison Snell and Michael Feldman discuss the week's top HPC stories.

We could all have predicted this with our magical Big Data analytics platforms, but it seems that Machine Learning is the new hotness in Information Security. A great number of startups with ‘cy’ and ‘threat’ in their names that claim that their product will defend or detect more effectively than their neighbour's product "because math". And it should be easy to fool people without a PhD or two that math just works. Indeed, math is powerful and large scale machine learning is an important cornerstone of much of the systems that we use today. However, not all algorithms and techniques are born equal. Machine Learning is a most powerful tool box, but not every tool can be applied to every problem and that’s where the pitfalls lie. This presentation will describe the different techniques available for data analysis and machine learning for information security, and discuss their strengths and caveats. The Ghost of Marketing Past will also show how similar the unfulfilled promises of deterministic and exploratory analysis were, and how to avoid making the same mistakes again.  Finally, the presentation will describe the techniques and feature sets that were developed by the presenter on the past year as a part of his ongoing research project on the subject, in particular present some interesting results obtained since the last presentation on DefCon 21, and some ideas that could improve the application of machine learning for use in information security, especially in its use as a helper for security analysts in incident detection and response. Alex Pinto is the Chief Data Scientist of MLSec Project. The goal of the project is to provide a platform for hypothesis testing for people interested in the development of machine learning algorithms to support the information security monitoring practice. He has over 14 years dedicated to information security solutions architecture, strategic advisory and monitoring. He has experience with a great range of security products, and has managed SOCs and SIEM implementations for way too long. Alex currently currently holds the CISSP-ISSAP, CISA, CISM and PMP certifications, not that anyone cares. He was also a PCI QSA for almost 7 years, but is almost fully recovered. Twitter: @alexcpsec

We could all have predicted this with our magical Big Data analytics platforms, but it seems that Machine Learning is the new hotness in Information Security. A great number of startups with ‘cy’ and ‘threat’ in their names that claim that their product will defend or detect more effectively than their neighbour's product "because math". And it should be easy to fool people without a PhD or two that math just works. Indeed, math is powerful and large scale machine learning is an important cornerstone of much of the systems that we use today. However, not all algorithms and techniques are born equal. Machine Learning is a most powerful tool box, but not every tool can be applied to every problem and that’s where the pitfalls lie. This presentation will describe the different techniques available for data analysis and machine learning for information security, and discuss their strengths and caveats. The Ghost of Marketing Past will also show how similar the unfulfilled promises of deterministic and exploratory analysis were, and how to avoid making the same mistakes again.  Finally, the presentation will describe the techniques and feature sets that were developed by the presenter on the past year as a part of his ongoing research project on the subject, in particular present some interesting results obtained since the last presentation on DefCon 21, and some ideas that could improve the application of machine learning for use in information security, especially in its use as a helper for security analysts in incident detection and response. Alex Pinto is the Chief Data Scientist of MLSec Project. The goal of the project is to provide a platform for hypothesis testing for people interested in the development of machine learning algorithms to support the information security monitoring practice. He has over 14 years dedicated to information security solutions architecture, strategic advisory and monitoring. He has experience with a great range of security products, and has managed SOCs and SIEM implementations for way too long. Alex currently currently holds the CISSP-ISSAP, CISA, CISM and PMP certifications, not that anyone cares. He was also a PCI QSA for almost 7 years, but is almost fully recovered. Twitter: @alexcpsec

We could all have predicted this with our magical Big Data analytics platforms, but it seems that Machine Learning is the new hotness in Information Security. A great number of startups with ‘cy’ and ‘threat’ in their names that claim that their product will defend or detect more effectively than their neighbour's product "because math". And it should be easy to fool people without a PhD or two that math just works. Indeed, math is powerful and large scale machine learning is an important cornerstone of much of the systems that we use today. However, not all algorithms and techniques are born equal. Machine Learning is a most powerful tool box, but not every tool can be applied to every problem and that’s where the pitfalls lie. This presentation will describe the different techniques available for data analysis and machine learning for information security, and discuss their strengths and caveats. The Ghost of Marketing Past will also show how similar the unfulfilled promises of deterministic and exploratory analysis were, and how to avoid making the same mistakes again.  Finally, the presentation will describe the techniques and feature sets that were developed by the presenter on the past year as a part of his ongoing research project on the subject, in particular present some interesting results obtained since the last presentation on DefCon 21, and some ideas that could improve the application of machine learning for use in information security, especially in its use as a helper for security analysts in incident detection and response. Alex Pinto is the Chief Data Scientist of MLSec Project. The goal of the project is to provide a platform for hypothesis testing for people interested in the development of machine learning algorithms to support the information security monitoring practice. He has over 14 years dedicated to information security solutions architecture, strategic advisory and monitoring. He has experience with a great range of security products, and has managed SOCs and SIEM implementations for way too long. Alex currently currently holds the CISSP-ISSAP, CISA, CISM and PMP certifications, not that anyone cares. He was also a PCI QSA for almost 7 years, but is almost fully recovered. Twitter: @alexcpsec

This protocol is developed to conduct a scoping review on the role of machine learning and preeclampsia integrated estimate of risk (PIERS) models in predicting maternal and perinatal birth outcomes or complications among women with hypertensive disorders of pregnancy. It is planned to include studies conducted from January 2000 to September 2025 around the globe.

This protocol is developed to conduct a scoping review on the role of machine learning and preeclampsia integrated estimate of risk (PIERS) models in predicting maternal and perinatal birth outcomes or complications among women with hypertensive disorders of pregnancy. It is planned to include studies conducted from January 2000 to September 2025 around the globe.

What is a GPU and how is it different than a GPU? GPUs and CPUs are both silicone based microprocessors but they differ in what they specialize in. GPUs specialize in parallel computations, and CPUs specialize in serial computations. While GPUs are known for a key component of getting the great graphics you want in your gamming, they are also known in the Machine Learning and AI Community for helping crunch millions of parameters needed to train a machine. Learn more in this episode of GLITCH. UPDATES: I've developed a Product Management Course for AI & Data Science for those interested in the industry or wanting to get into Product Management. Here's the link! https://www.udemy.com/course/the-product-management-for-data-science-ai-course/?referralCode=DE25D5190902F792E9A1 JOIN The GLITCH Email List: https://glitch.technology/subscribe SAY HELLO https://twitter.com/daniellethe https://instagram.com/daniellethe https://medium.com/daniellethe

What does the MLOps pipeline at London Based FinTech startup TrueLayer look like?   London Based Fintech start-up TrueLayer decided to use Machine Learning instead of a rule-based system in mid-2019 and in our 7th meetup we spoke to their lead data scientist Alex Spanos about everything that entailed.   During the meetup, we dove into how TrueLayer architected their MLOps pipeline for their Open Banking API: more specifically which tools they use and why, what prompted them to use machine learning, and how Alex sees the role of a Machine Learning Engineer. Alex has led the hiring process of Machine Learning Engineers and shared learnings on candidates and businesses alike.    Alex is the Lead Data Scientist at TrueLayer, focussing on building Open Banking API products powered by data. Prior to TrueLayer, he built predictive models in Financial Services, used social data to predict the \"next-big-thing\" in Fast Moving Consumer Goods and introduced Machine Learning techniques in subsurface imaging.   His academic background is in Applied Mathematics & Statistics.   Check out his blog entries for more info: https://blog.truelayer.com/improving-the-classification-of-your-transaction-data-with-machine-learning-c36d811e4257 https://alexiospanos.com/hiring-machine-learning-engineers-part-1/ https://alexiospanos.com/hiring-machine-learning-engineers-part-2/ Connect with Demetrios on LinkedIn:   https://www.linkedin.com/in/dpbrinkm/   Connect with Alex on Linkedin:   https://www.linkedin.com/in/alexspanos/ Join us on slack: https://join.slack.com/t/mlops-community/shared_invite/zt-391hcpnl-aSwNf_X5RyYSh40MiRe9Lw

This video was translated from English to Spanish. As described in https://developers.googleblog.com/2022/12/improving-video-voice-dubbing-through-deep-learning.html , we used the techniques of cross-lingual voice imitation and lip reanimation, which makes the voice sound like the original speaker and adjusts the lip motion to make it appear more natural. Original English series: https://www.youtube.com/playlist?list=PLOU2XLYxmsII9mzQ-Xxug4l2o04JBrkLV -------------------------------------------------------------- Este vídeo se tradujo del inglés al español. Como se describe en https://developers.googleblog.com/2022/12/improving-video-voice-dubbing-through-deep-learning.html , hemos utilizado las técnicas de imitación de voz multilingüe y reanimación labial, que hace que la voz suene como la del hablante original y ajusta el movimiento de los labios para que parezca más natural. Serie original en inglés: https://www.youtube.com/playlist?list=PLOU2XLYxmsII9mzQ-Xxug4l2o04JBrkLV

Opportunities and limitations of explaining quantum machine learning by Jonas Naujoks @QTMLConference

Think Machine Learning will replace humans or that ML models are always accurate? Think again! In this entertaining video, we debunk five of the most common myths about Machine Learning with humorous animations and engaging skits. Perfect for anyone exploring Data Science, this video separates fact from fiction to give you a clearer understanding of ML. Want to learn more? Check out our detailed fact-check article on our website!

Coffee Sessions #28 with Charlie You of Workday, Lessons learned from hosting the Machine Learning Engineered podcast //Bio Charlie You is a Machine Learning Engineer at Workday and the host of ML Engineered, a long-form interview podcast aiming to help listeners bring AI out of the lab and into products that people love. He holds a B.S. in Computer Science from Rensselaer Polytechnic Institute and previously worked for AWS AI. Charlie is currently working as a Machine Learning Engineer at Workday. He hosts the ML Engineered podcast, learning from the best practitioners in the world.   Check Charlie's podcast and website here: mlengineered.com https://cyou.ai/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Charlie on LinkedIn: https://linkedin.com/in/charlieyou/ Timestamps: [00:00] Introduction to Charlie You [01:50] Charlie's background on Machine Learning and inspiration to create a podcast [06:20] What's your experience been so far as the machine learning engineer and trying to put models into production and trying to get things out that has business value? [07:08] "I started the podcast because as I started working, I had the tingling that machine learning engineering is harder than most people thought, and like way harder than I personally thought." [08:20] What's an example of that where you target someone in your podcast, you keep that learning and you want an extra meeting the next day and say "Hey, actually I'm starting one of the world's experts on this topics and this is what they said"?    [10:06] In a world of tons of traditional software engineering assets and the process you put in place, how have they adopted what they're doing to the machine learning realm?    [19:00] About your podcast, what are some 2-3 most consistent trends that you've been seeing? [21:08] Instead of splintering so much as machine learning monitoring infrastructure specialist, are you going to departmentalize it in the future? [27:22] Is there such a thing as an MLOps engineer right now? [28:50] "We haven't seen a very vocal, very opinionated project manager in machine learning yet." - Todd Underwood [30:18] "Similarly with tooling, we haven't seen the emergence of the tools that encode those best practices." Charlie [31:42] "The day that you don't have to be a subject matter expert in machine learning to feel confident and deploy machine learning products, is the day that you will see the real product leadership in machine learning." Vishnu [34:12] I'd love to hear your take on some more trends that you've been seeing (Security and Ethics) [34:41] "Data Privacy and Security is always at the top of any consideration for infrastructure." Charlie [35:44] That's driven by legal requirements? How do you solve this problem? [37:27] How do we make sure that if that blows up, you're not left with nothing?   [42:28] In your conversations, have you seen people who goes with cloud provider? [43:25] Enterprises have much different incentives than startups do. [45:48] What are some used cases where companies are needing to service their entire needs? [45:48] What are some used cases where companies are needing to service their entire needs? [49:18] What are some takeaways that you had in terms of how you think about your career, what experiences you want to build as this MLOps based engineering is moving so fast?   [56:08] "Your edge is never in the algorithm"

trAIlique's facilities management software offers a powerful solution to streamline operations, enhance efficiency and reduce costs. Our computer-aided facilities management (CAFM) software simplifies tasks, from maintenance tracking to asset management, helping businesses optimize their facilities with ease. https://www.trailique.ai/solutions/facilities-management-software/

MLOps community meetup #56! Last Wednesday we talked to  Daniel Stahl, Head of Data and Analytic Platforms, Regions Bank. // Abstract: The Data Science practice has evolved significantly at Regions, with a corresponding need to scale and operationalize machine learning models. Additionally, highly regulated industries such as finance require a heightened focus on reproducibility, documentation, and model controls.  In this session with Daniel Stahl, we will discuss how the Regions team designed and scaled their data science platform using DevOps and MLOps practices.  This has allowed Regions to meet the increased demand for machine learning while embedding controls throughout the model lifecycle.  In the 2 years since the data science platform has been onboarded, 100% of data products have been successfully operationalized. // Bio: Daniel Stahl leads the ML platform team at Regions Bank and is responsible for tooling, data engineering, and process development to make operationalizing models easy, safe, and compliant for Data Scientists.   Daniel has spent his career in financial services and has developed novel methods for computing tail risk in both credit risk and operational risk, resulting in peer-reviewed publications in the Journal of Credit Risk and the Journal of Operational Risk. Daniel has a Masters in Mathematical Finance from the University of North Carolina Charlotte.      Daniel lives in Birmingham, Alabama with his wife and two daughters. ----------- Connect With Us ✌️-------------    Join our Slack community:  https://go.mlops.community/slack Follow us on Twitter:  @mlopscommunity Sign up for the next meetup:  https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Dan on LinkedIn: https://www.linkedin.com/in/daniel-stahl-6685a52a/ Timestamps: [00:00] Introduction to Ben Wilson [00:11] Ben's background in tech [01:17] "How do you do what I have always done pretty well which is being as lazy as possible in order to automate things that I hate doing. So I learned about Regression Problems." [03:40] Human aspect of Machine Learning in MLOps [05:51] MLOps is an organizational problem [09:27] Fragile Models [12:36] Fraud Cases [15:21] Data Monitoring [18:37] Importance of knowing what to monitor for [22:00] Monitoring for outliers [24:16] Staying out of Alert Hell [29:40] Ground Truth [31:25] Model vs Data Drift on Ground Truth Unavailability [34:25] Benefit to monitor system or business level metrics [38:20] Experiment in the beginning, not at the end [40:30] Adaptive windowing [42:22] Bridge the gap [46:42] What scarred you really bad?

A modeling paradigm is developed to augment predictive models of turbulence by effectively utilizing limited data generated from physical experiments. The key components of our approach involve inverse modeling to infer the spatial distribution of model discrepancies, and, machine learning to reconstruct discrepancy information from a large number of inverse problems into corrective model forms. We apply the methodology to turbulent flows over airfoils involving flow separation. Model augmentations are developed for the Spalart Allmaras (SA) model using adjoint-based full field inference on experimentally measured lift coefficient data. When these model forms are reconstructed using neural networks (NN) and embedded within a standard solver, we show that much improved predictions in lift can be obtained for geometries and flow conditions that were not used to train the model. The NN-augmented SA model also predicts surface pressures extremely well. Portability of this approach is demonstrated by confirming that predictive improvements are preserved when the augmentation is embedded in a different commercial finite-element solver. The broader vision is that by incorporating data that can reveal the form of the innate model discrepancy, the applicability of data-driven turbulence models can be extended to more general flows.

This study investigates the relationship between weather and physical activity, focusing on the incremental contribution of weather conditions to the prediction of mobility data beyond recent movement history, data on the built environment, time variables and basic descriptive individual variables. To assess this, the model is computed twice, once excluding and once including weather data, to quantify the added predictive value of weather-related factors. Built environment variables represent structural and spatial characteristics of participants’ residential surroundings, such as access to services, public transport, green space, and walkable infrastructure. These human-designed environmental features provide the physical context in which daily mobility occurs and are known to influence walking behavior, particularly among older adults (Barnett et al., 2017; Sallis et al., 2012; Stearns et al., 2023). In this study, built environment characteristics are included as person-static predictors to account for between-person differences in local mobility opportunities. The meta-analysis conducted by Turrisi et al. (2021) synthesizes findings from 77 studies investigating the relationship between weather and physical activity. Each of the weather indices temperature, precipitation, photoperiod, wind speed, and humidity were examined in at least 20% of the 77 studies, underscoring their relevance in understanding meteorological influences on physical activity. The meta-analysis highlights the significance of weather as a key factor influencing movement-related behavior. Building on this, the present study seeks to determine the extent of incremental variance in physical activity that can be explained by all locally measured weather indices available, including temperature, precipitation, solar radiation, wind speed, humidity and atmospheric pressure. Physical activity was defined in the meta-analysis by Turrisi et al. (2021) either as physical activity volume or as intensity-specific duration. Physical activity volume, primarily assessed using accelerometers, quantifies energy expenditure and is commonly measured in step count or total walking time. In contrast, intensity-specific duration captures the time allocated to different exertion levels, distinguishing specifically between moderate-to-vigorous physical activity (MVPA) and light-intensity physical activity (LPA) (Turrisi et al., 2021). However, these represent only a subset of the available Digital Mobility Outcomes (DMOs). A meta-analysis conducted by Polhemus et al. (2021) reviewed studies examining the relationship between various granular DMOs and four distinct medical conditions. Among the DMOs analysed, step count, gait speed, cadence, and step length demonstrated consistent evidence of validity and responsiveness across different conditions. Building on these findings concerning the relevance of DMOs, this study adopts a broader definition of physical activity by incorporating multiple DMOs as measures of physical activity. Specifically, this study examines four distinct mobility dimensions: volume as total walking time, pace as mean gait speed and rhythm as mean cadence (ground contacts per minute). Especially as these DMOs are increasingly utilized to assess gait quality across various diseases (Cavanaugh et al., 2007; Polhemus et al., 2021), it is crucial to identify influencing factors to ensure that future studies can account for all relevant variables. Given the impact of weather on physical activity, traditionally defined by volume or intensity-specific duration, it is particularly relevant to explore its impact on these more elaborate DMOs. In the existing literature, physical activity and weather are typically examined at an hourly or even daily aggregation level (Aspvik et al., 2018; Klimek et al., 2022), mostly using statistical models that emphasize interpretability and therefore impose distributional assumptions, or require explicit modeling of relationships (Albrecht et al., 2020; Klenk et al., 2012). In contrast, this study enhances both temporal and spatial resolution by analyzing data at 10-minute intervals and at postcode-level spatial granularity, thereby providing a more detailed and dynamic understanding of weather-driven changes in physical activity. Furthermore, this study employs a machine learning-based Random Forest approach, allowing for maximum flexibility in pattern detection. Unlike traditional statistical models, machine learning algorithms learn patterns directly from data rather than relying on predefined distributional assumptions. This enables them to capture complex, non-linear relationships within the dataset (Sekeroglu et al., 2022). More specifically, this study employs a machine learning-based Random Forest approach. Random Forest utilizes an ensemble of regression trees, generating multiple decision trees during training and optimizing predictions by averaging their individual regression outputs (Sekeroglu et al., 2022) It is particularly effective in capturing highly non-linear relationships while demonstrating resilience to overfitting and robustness against outliers. (Xie et al., 2021) However, despite these advantages, Random Forest is often regarded as a black-box algorithm due to the complexity introduced by the aggregation of a large number of trees (Couronné et al., 2018). While this complexity limits the interpretability of explicit decision rules, the model’s ability to assess the importance of predictor variables through a Variable Importance Measure provides valuable insights into feature relevance. It will be assessed using Permutation Feature Importance, a robust method that quantifies the effect of each predictor by measuring the increase in error after randomly permuting its values. Additionally, Accumulated Local Effects (ALEs) will be applied to visualize how changes in key weather variables affect physical activity, capturing potential nonlinear effects and threshold-dependent relationships. Random Forest is not inherently designed for time-series data, as it does not account for temporal dependencies in its structure. To address this limitation, recent movement history is incorporated through time delay embedding, where lagged variables serve as additional predictors (Beck & Jackson, 2022; Oertzen & Boker, 2010). In addition to lag variables for mapping short-term dependencies, additional temporal predictors model longer-term temporal dynamics per person. This approach allows the model to leverage temporal patterns while maintaining the flexibility of a machine learning framework. This study is characterized by its high temporal and spatial resolution, the focused examination of specific Digital Mobility Outcomes, and the application of a highly flexible, assumption-free analytical approach using Random Forest. This allows for an unbiased assessment of the extent to which weather phenomena contribute to the prediction of physical activity.

Accurately estimating evaporation is necessary for calculating and scheduling irrigation water requirements. Current literature points to the use of individual machine learning models for better estimation of evaporation. However, such methods have not been used in the Indian framework. Moreover, given the diversity of climate, it is necessary to develop an ensemble technique incorporating a significant number of machine learning algorithms to have a better estimation of weekly evaporation. The purpose of this paper is to develop an ensemble technique that makes the machine learning models that have a better estimation of weekly evaporation. The results showed that the Bagging Random Forest model has a much better performance in estimating weekly evaporation compared to other fitted ensemble models. R. S. Parmar | G. B. Chaudhari | S. H. Bhojani "Estimating Evaporation using Machine Learning Based Ensemble Technique" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-4, August 2023, URL: https://www.ijtsrd.com/papers/ijtsrd59847.pdf Paper Url:https://www.ijtsrd.com/engineering/agricultural-engineering/59847/estimating-evaporation-using-machine-learning-based-ensemble-technique/r-s-parmar

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (07 - Multiple Linear Regression)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (20 - Naive Bayes)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (28 -  Part 5 Association Rule Learning)

Sentiment prediction of contemporary music can have a wide-range of applications in modern society, for instance, selecting music for public institutions such as hospitals or restaurants to potentially improve the emotional well-being of personnel, patients, and customers, respectively. In this project, music recommendation system built upon on a naive Bayes classifier, trained to predict the sentiment of songs based on song lyrics alone. The experimental results show that music corresponding to a happy mood can be detected with high precision based on text features obtained from song lyrics.

“Read It” on #Android uses machine learning to pick out the core content of web pages and translates it to one of the 40+ languages of your choosing. Hear more from machine intelligence product lead David Kadouch. https://t.co/wBpl2ni0Lw Source: https://twitter.com/Android/status/1217572901597143041 Uploader: Android

When driving long distances, drivers who do not take frequent rests are more likely to get sleepy, a condition that experts say they often fail to identify early enough. Based on eye condition, this research proposes a system for detecting driver sleepiness in real time. A camera is often used to take a sequence of images by the system. In our system, these capture images may be saved as individual frames. The resulting frame is sent into facial recognition software as an input. The image's needed feature (eye) is then extracted. The method creates a condition for each eye and suggests a certain number of frames with the same condition that can be registered.

"Neutrosophic Computing and Machine Learning" (NCML) es una revista académica que ha sido creada para publicaciones de estudios avanzados en neutrosofía, conjunto neutrosófico, lógica neutrosófica, probabilidad neutrosófica, estadística neutrosófica, enfoques neutrosóficos para el aprendizaje automático, etc. y sus aplicaciones en cualquier campo.  

"Neutrosophic Computing and Machine Learning" (NCML) es una revista académica que ha sido creada para publicaciones de estudios avanzados en neutrosofía, conjunto neutrosófico, lógica neutrosófica, probabilidad neutrosófica, estadística neutrosófica, enfoques neutrosóficos para el aprendizaje automático, etc. y sus aplicaciones en cualquier campo.  

机器学习笔记 To restore the repository download the bundle wget https://archive.org/download/github.com-lcatro-Machine-Learning-Note_-_2017-11-20_02-07-46/lcatro-Machine-Learning-Note_-_2017-11-20_02-07-46.bundle and run: git clone lcatro-Machine-Learning-Note_-_2017-11-20_02-07-46.bundle Source: https://github.com/lcatro/Machine-Learning-Note Uploader: lcatro Upload date: 2017-11-20

This paper introduces the novel concept of single valued heptapartitioned neutrosophic sets (SVHNSs) which is the generalized version of the neutrosophic sets. This set consists of seven membership functions which are more sensitive to real-world problems. Membership functions are defined as an absolute true, relative true, absolute false, relative false, contradiction, unknown (undefined) and ignorance respectively. This scenario of indeterminacy provides a better accuracy. Moreover, several properties of this set are also addressed. This study focuses on the romantic sensations experienced by young boys and girls in a variety of contexts. The data set supporting this study comprises individuals aged 18-25, with data collected from the Psychology Department at Peshawar University, Pakistan. This data was critically analyzed using the Single-Valued Heptapartitioned Neutrosophic Set (SVHNS). For a real-world application involving the romantic feelings of young individuals across various dimensions, machine learning and graphical algorithms such as Encrypted K-Means Clustering, Encrypted K-Means Clustering Heat Map, Encrypted Elbow Method, Decrypted K-Means Clustering, Encrypted Correlation Matrix, and Decrypted Correlation Matrix were applied and visualized. These algorithms assist in examining and developing relationships among various factors that influence the romantic feelings of young men and women. The proposed techniques offer new dimensions not only for psychological studies in general but also specifically for understanding emotional disorders and breakups in romantic relationships among university students. 2020 Mathematics Subject Classifications: 68T05, 03E72, 62H30, 91F20

None Welcome to our Introduction to Machine Learning Course Repo! You can find more information about our Introduction to Machine Learning Course by visiting Course Website. To enroll our courses, you can find the next course that fit your schedule by visiting Upcoming Courses. Syllabus Lesson 1 Probabilty Review Linear Algebra Review Lesson 2 Data Preparation Linear Regression Lesson 3 Logistic Regression Regularization Lesson 4 Decision Trees Lesson 5 Unsupervised Learning Certification Example To restore the repository download the bundle wget https://archive.org/download/github.com-globalaihub-introduction-to-machine-learning_-_2021-03-25_19-24-11/globalaihub-introduction-to-machine-learning_-_2021-03-25_19-24-11.bundle and run: git clone globalaihub-introduction-to-machine-learning_-_2021-03-25_19-24-11.bundle Source: https://github.com/globalaihub/introduction-to-machine-learning Uploader: globalaihub Upload date: 2021-03-25

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (05 - Part 2 Regression)

A framework is introduced for solving a sequence of slowly changing optimization problems, including those arising in regression and classification applications, using optimization algorithms such as stochastic gradient descent (SGD). The optimization problems change slowly in the sense that the minimizers change at either a fixed or bounded rate. A method based on estimates of the change in the minimizers and properties of the optimization algorithm is introduced for adaptively selecting the number of samples needed from the distributions underlying each problem in order to ensure that the excess risk, i.e., the expected gap between the loss achieved by the approximate minimizer produced by the optimization algorithm and the exact minimizer, does not exceed a target level. Experiments with synthetic and real data are used to confirm that this approach performs well.

A framework is introduced for solving a sequence of slowly changing optimization problems, including those arising in regression and classification applications, using optimization algorithms such as stochastic gradient descent (SGD). The optimization problems change slowly in the sense that the minimizers change at either a fixed or bounded rate. A method based on estimates of the change in the minimizers and properties of the optimization algorithm is introduced for adaptively selecting the number of samples needed from the distributions underlying each problem in order to ensure that the excess risk, i.e., the expected gap between the loss achieved by the approximate minimizer produced by the optimization algorithm and the exact minimizer, does not exceed a target level. Experiments with synthetic and real data are used to confirm that this approach performs well.

A framework is introduced for solving a sequence of slowly changing optimization problems, including those arising in regression and classification applications, using optimization algorithms such as stochastic gradient descent (SGD). The optimization problems change slowly in the sense that the minimizers change at either a fixed or bounded rate. A method based on estimates of the change in the minimizers and properties of the optimization algorithm is introduced for adaptively selecting the number of samples needed from the distributions underlying each problem in order to ensure that the excess risk, i.e., the expected gap between the loss achieved by the approximate minimizer produced by the optimization algorithm and the exact minimizer, does not exceed a target level. Experiments with synthetic and real data are used to confirm that this approach performs well.

The delay at signalized intersections is a crucial parameter that determines the performance and level of service (LOS). The estimation models are commonly used to model delay; however, inaccurate predictions from these models can pose a significant limitation. Consequently, this study aimed to compare a wide array of machine learning algorithms, including Artificial Neural Networks (ANN), Random Forest (RF), decision tree, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), AdaBoost, Gradient Boost, XGBoost, and Partial Least Squares (PLS) regression. A comprehensive evaluation was undertaken across prediction accuracy, training-testing performance discrepancy, sensitivity to outliers, computational time cost, and model robustness. Additionally, the proposed methods were benchmarked against the Highway Capacity Manual (HCM), Webster, and Akçelik models. The results demonstrated that the RF model exhibited the most balanced performance across the specified criteria, with an average error below 4% and a rating of 35 out of 45 according to the proposed criteria. Moreover, the findings revealed that adopted analytical models should not be employed for vehicular delay estimation without calibration, as RMSE values were about 5 to 58 times higher than other models, varying by model.

Inflammatory Bowel Disease (IBD) is a chronic condition that requires continuous management and monitoring of symptoms to prevent flare-ups and maintain patient well-being. Machine learning (ML) technologies have shown significant promise in healthcare, particularly in providing personalized support for chronic disease management. This paper explores the development of an ML-powered chatbot designed specifically for IBD patient support and symptom tracking. The chatbot leverages Natural Language Processing (NLP) to understand patient queries, track symptoms, and provide real-time advice on disease management. It can predict symptom patterns and recommend personalized interventions by analyzing historical data, including symptom logs, lifestyle factors, and treatment history. A detailed technical overview of the chatbot development is provided, covering data collection, machine learning model design, integration with healthcare systems, and security protocols to ensure patient privacy. The paper also addresses the challenges in the development process, such as handling unstructured data, ensuring medical accuracy, and maintaining patient engagement. Key features of the chatbot include real-time symptom tracking, personalized advice based on machine learning models, and seamless integration with electronic health record (EHR) systems. The conclusion emphasizes the potential of ML-powered chatbots to transform chronic disease management by offering patients a more personalized, proactive, and accessible tool for managing IBD.

Heart illnesses are among the most significant contributors to mortality in the world in the modern era. Heart attacks are responsible for the death of one person every 33 seconds. disease of the cardiovascular system by disclosing the proportion of mortality all over the world that are caused by heart attacks. In order to forecast instances of heart disease, a supervised machine learning method is utilised. Because the incidence of heart strokes in younger people is growing at an alarming rate, we need to establish a method that can identify the warning signs of a heart attack at an early stage and stop the stroke before it occurs. Because it is impractical for the average person to often undertake expensive tests like the electrocardiogram (ECG), there is a need for a system that is convenient and, at the same time, accurate in forecasting the likelihood of developing heart disease. Therefore, our plan is to create a programme that, given basic symptoms such as age, sex, pulse rate, etc., can determine whether or not a person is at risk for developing a cardiac condition. The machine learning algorithm neural networks that are used in the suggested system are the most accurate and dependable.

This is the new era of technology development where all the things and work is done by the machines. The goal of Information Technology is to develop a device which is able to work like a human itself. For that Artificial Intelligence, Machine Learning and Deep Learning are going to be used. Machine Learning is a subpart of the Artificial Intelligent which helps a machine to learn by itself. To apply learning processes on machines it required deep knowledge of programming, mathematics and statistics. Now it is not a big problem, as the technology is changing day by day the new concept known as No Code ML and Auto Code Generation are introduced. This helps the users to create a model without doing any kind of coding. In this new technology everyone is able to create a model and use machine learning. There are several platforms which provide this kind of facilities. The models created on those platforms give good accuracy and desire outcomes as well. Hiteshkumar Babubhai Vora | Hardik Anilbhai Mirani | Vraj Bhatt "Traditional Machine Learning and No-Code Machine Learning with its Features and Application" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38287.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/38287/traditional-machine-learning-and-nocode-machine-learning-with-its-features-and-application/hiteshkumar-babubhai-vora