Demo on using Amazon SageMaker to try out Mask R-CNN, a deep learning image segmentation network.
Presented at Cambridge AWS Meetup Main Meeting #18: New Amazon AI Services
Metadata extraction using Amazon Rekognition and Amazon SageMakerMatt McDonnell
Metail's mission is to digitize every garment for every body. In this talk we discuss how AI can be used in our image processing pipeline to provide our customers with garment metadata.
This talk was presented at presented at the Cambridge AWS Meetup group Main Meeting #23 on 6th November 2018.
Apple makes it really easy to get started with Machine Learning as a developer. See how you can easily use Create ML and Turi Create to train Machine Learning models and use them in your iOS apps.
Had a great time discussing Azure ML with the Denver R User Group meeting this evening. We covered ways to use R in Azure ML to clean, transform and process data and to build machine learning models. This is a great meetup if you have a chance to attend a future meeting!
In this Project, mini computer have various types of performing software included Calculator, Tiktaktoe and Last Creating Notepad.
It has a knowledge to understand the code of creation with the output respectively.
Alexandra johnson reducing operational barriers to model trainingMLconf
This document discusses reducing operational barriers to machine learning model training through building machine learning infrastructure. It presents challenges faced by both machine learning experts and infrastructure engineers. It then describes SigOpt's solution of building SigOpt Orchestrate to address these challenges through containerization, Kubernetes for parallel training, and a command line interface for viewing progress and debugging. The final slides invite connecting with SigOpt and note they are hiring.
DataSciencePT #27 - Fifty Shades of Automated Machine LearningRui Quintino
Is "the sexiest job of 21st century", the Data Scientist, about to be automated? How & when can AutoML tools help on a typical machine learning lifecycle? What AutoML challenges are still open & what ML work will remain in the foreseeable future? Most importantly… will robots get all the fun & sex appeal? :) Some questions we'll try to tackle on this session.
*-Robots are not allowed in this session
The student learned how to use various photography hardware like a DSLR camera, flash triggers, strobe flashes, and soft boxes to take professional photos. They also learned software like Photoshop to edit photos, InDesign to layout pages professionally, and WordPress to create and maintain a blog. Through this process, the student gained experience with different photography and design techniques.
TimeToPic is a visualization tool that displays data in a timeline format. It uses events, state machines, and values to visualize various aspects of program behavior. This allows developers to quickly understand problems by seeing the "big picture" of what is happening across their application or system. TimeToPic works across different platforms and frameworks and can speed up debugging and analysis by making issues easier to identify and locate from visualizing log and measurement data.
Metadata extraction using Amazon Rekognition and Amazon SageMakerMatt McDonnell
Metail's mission is to digitize every garment for every body. In this talk we discuss how AI can be used in our image processing pipeline to provide our customers with garment metadata.
This talk was presented at presented at the Cambridge AWS Meetup group Main Meeting #23 on 6th November 2018.
Apple makes it really easy to get started with Machine Learning as a developer. See how you can easily use Create ML and Turi Create to train Machine Learning models and use them in your iOS apps.
Had a great time discussing Azure ML with the Denver R User Group meeting this evening. We covered ways to use R in Azure ML to clean, transform and process data and to build machine learning models. This is a great meetup if you have a chance to attend a future meeting!
In this Project, mini computer have various types of performing software included Calculator, Tiktaktoe and Last Creating Notepad.
It has a knowledge to understand the code of creation with the output respectively.
Alexandra johnson reducing operational barriers to model trainingMLconf
This document discusses reducing operational barriers to machine learning model training through building machine learning infrastructure. It presents challenges faced by both machine learning experts and infrastructure engineers. It then describes SigOpt's solution of building SigOpt Orchestrate to address these challenges through containerization, Kubernetes for parallel training, and a command line interface for viewing progress and debugging. The final slides invite connecting with SigOpt and note they are hiring.
DataSciencePT #27 - Fifty Shades of Automated Machine LearningRui Quintino
Is "the sexiest job of 21st century", the Data Scientist, about to be automated? How & when can AutoML tools help on a typical machine learning lifecycle? What AutoML challenges are still open & what ML work will remain in the foreseeable future? Most importantly… will robots get all the fun & sex appeal? :) Some questions we'll try to tackle on this session.
*-Robots are not allowed in this session
The student learned how to use various photography hardware like a DSLR camera, flash triggers, strobe flashes, and soft boxes to take professional photos. They also learned software like Photoshop to edit photos, InDesign to layout pages professionally, and WordPress to create and maintain a blog. Through this process, the student gained experience with different photography and design techniques.
TimeToPic is a visualization tool that displays data in a timeline format. It uses events, state machines, and values to visualize various aspects of program behavior. This allows developers to quickly understand problems by seeing the "big picture" of what is happening across their application or system. TimeToPic works across different platforms and frameworks and can speed up debugging and analysis by making issues easier to identify and locate from visualizing log and measurement data.
AWS DeepLens Workshop: Building Computer Vision Applications - BDA201 - Atlan...Amazon Web Services
In this workshop, learn how to build and deploy computer vision models using the AWS DeepLens deep learning-enabled video camera. Learn to build a machine learning model from scratch using Amazon SageMaker, and get hands-on experience with AWS DeepLens by extending that model to build an end-to-end AI application using Amazon Rekognition. Attendees also learn about use cases built by the community which integrate other AWS services and extend the functionality of AWS DeepLens. Please note, you must have an AWS account to participate in this workshop. If setting up a new account, please do this at least 24 hours in advance of the workshop.
This document summarizes a presentation about deploying custom machine learning models using Amazon SageMaker. It discusses:
1. An overview of machine learning, AWS SageMaker, and how SageMaker works to build, train, test, tune and deploy models.
2. The process for deploying a fully custom ML model with SageMaker, including building the model, defining inference code, creating a SageMaker container, and deploying the model as an endpoint.
3. A demo of this process showing how to create a model, endpoint configuration, and endpoint to deploy a custom model and invoke it via an API.
Sagemaker is a fully managed AWS service that facilitates machine learning development including data preparation, model training, evaluation and deployment. It provides various tools like pretrained models, algorithms, notebooks and frameworks to simplify and accelerate the ML workflow. Some key Sagemaker components are notebooks, Studio, algorithms like linear learner and frameworks like TensorFlow that can be used to build and deploy machine learning models.
История одного успешного ".NET" проекта, Александр СугакSigma Software
The document describes an IoT project built using an unconventional technology stack. It discusses the current status quo of typical .NET projects using standard Microsoft technologies. It then presents a case study of an IoT project built using non-standard technologies like F#, Suave, ReactJS, and running on Linux. It outlines the principles used in selecting the technology stack, including preferring less magic frameworks, ensuring predictable and reproducible builds, shortening feedback loops, and focusing on reusable skills and knowledge.
- The document is a programming tutorial that introduces Python and MATLAB for programming in medical imaging.
- It discusses what a computer program is, explains programming languages and code, and why learning to program is useful.
- The tutorial compares Python and MATLAB, noting that both can be used for the course but examples will focus on MATLAB. It outlines differences like MATLAB requiring a license while Python is free.
Strata CA 2019: From Jupyter to Production Manu MukerjiManu Mukerji
Proposed title
From Jupyter to production
Description of the presentation
Jupyter is very popular for data science, data exploration and visualization, this talk is about how to use it in for AI/ML in a production environment.
General Flow of talk:
How things can go wrong with QA, Production releases when using a notebook
Common Jupyter ML examples
Standard ML flow
Training in production
Model creation
Testing in production
Papermill and Jupyter
Production workflows with Sagemaker
Speaker
Manu Mukerji is senior director of data, machine learning, and analytics at 8×8. Manu’s background lies in cloud computing and big data, working on systems handling billions of transactions per day in real time. He enjoys building and architecting scalable, highly available data solutions and has extensive experience working in online advertising and social media.
Make Cross-platform Mobile Apps Quickly - SIGGRAPH 2014Gil Irizarry
This document provides a summary of a presentation about making cross-platform mobile apps quickly using open source tools. It discusses using PhoneGap to create apps using HTML, CSS, and JavaScript that are cross-platform. It provides examples of building simple apps demonstrating concepts like accessing device data, using maps, touch events, and animation. The examples are meant to illustrate how to create mobile apps that work across Android and iOS without using their native languages.
Postmortem of a uwp xaml application developmentDavid Catuhe
This document provides tips and tricks for developing Universal Windows Platform (UWP) applications using C# and XAML, including techniques for adapting the user interface for different devices, persisting navigation state, improving performance, debugging issues, and deploying apps to the Microsoft Store. Specific strategies covered include using a reflow technique to rearrange UI elements, serializing navigation history with a stack of objects, avoiding reliance on NavigationCache, using debug stopwatches and profiling tools, adding exception handling, and integrating AppInsights for analytics and crash reporting.
Application Development Using Java - DIYComputerScience Courseparag
This document describes a project-based course to build a Minesweeper game from scratch using Java. The 13-section course breaks the project into incremental parts, teaching programming concepts at each stage like object-oriented design, testing, persistence, web development, and more. Learners will build both desktop and web versions, while practicing techniques such as refactoring, internationalization, logging, and database integration. The goal is for students to learn Java by completing the projects and reading provided code at their own pace.
AWS DeepLens Workshop: Building Computer Vision Applications - BDA201 - Anahe...Amazon Web Services
In this workshop, learn how to build and deploy computer vision models using the AWS DeepLens deep learning-enabled video camera. Learn to build a machine learning model from scratch using Amazon SageMaker, and get hands-on experience with AWS DeepLens by extending that model to build an end-to-end AI application using Amazon Rekognition. Attendees also learn about use cases built by the community which integrate other AWS services and extend the functionality of AWS DeepLens. Please note, you must have an AWS account to participate in this workshop. If setting up a new account, please do so at least 24 hours in advance of the workshop.
MLOps and Reproducible ML on AWS with Kubeflow and SageMakerProvectus
Looking to implement MLOps using AWS services and Kubeflow? Come and learn about machine learning from the experts of Provectus and Amazon Web Services (AWS)!
Businesses recognize that machine learning projects are important but go beyond just building and deploying models, which is mostly done by organizations. Successful ML projects entail a complete lifecycle involving ML, DevOps, and data engineering and are built on top of ML infrastructure.
AWS and Amazon SageMaker provide a foundation for building infrastructure for machine learning while Kubeflow is a great open source project, which is not given enough credit in the AWS community. In this webinar, we show how to design and build an end-to-end ML infrastructure on AWS.
Agenda
- Introductions
- Case Study: GoCheck Kids
- Overview of AWS Infrastructure for Machine Learning
- Provectus ML Infrastructure on AWS
- Experimentation
- MLOps
- Feature Store
Intended Audience
Technology executives & decision makers, manager-level tech roles, data engineers & data scientists, ML practitioners & ML engineers, and developers
Presenters
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Qingwei Li, ML Specialist Solutions Architect, AWS
Feel free to share this presentation with your colleagues and don't hesitate to reach out to us at info@provectus.com if you have any questions!
REQUEST WEBINAR: https://provectus.com/webinar-mlops-and-reproducible-ml-on-aws-with-kubeflow-and-sagemaker-aug-2020/
The Good, the Bad and the Ugly things to do with androidStanojko Markovik
The document discusses good practices, bad habits, and ugly issues that can arise when developing Android applications.
The good section covers clean code practices, using libraries, XML resources, and lifecycle methods. The bad section notes lazy practices like ignoring lifecycles and leaving cursors open. The ugly section describes ANRs, memory issues like bitmaps and strings, and overuse of logs and notifications. Developers are advised to follow proper patterns, manage resources carefully, and leverage tools like TraceView and MAT to debug problems.
Building Instruqt, a scalable learning platformInstruqt
On February 15th I gave a talk on how we built Instruqt. We use Kubernetes, Terraform and Google Cloud, and in my talk I explain the benefits of using these tools and services correctly.
Version Control in Machine Learning + AI (Stanford)Anand Sampat
Starting with outlining the history of conventional version control before diving into explaining QoDs (Quantitative Oriented Developers) and the unique problems their ML systems pose from an operations perspective (MLOps). With the only status quo solutions being proprietary in-house pipelines (exclusive to Uber, Google, Facebook) and manual tracking/fragile "glue" code for everyone else.
Datmo works to solve this issue by empowering QoDs in two ways: making MLOps manageable and simple (rather than completely abstracted away) as well as reducing the amount of glue code so to ensure more robust end-to-end pipelines.
This goes through a simple example of using Datmo with an Iris classification dataset. Later workshops will expand to show how Datmo can work with other data pipelining tools.
Why you should consider a microframework for your next web projectJoaquín Muñoz M.
1) Microframeworks are lightweight frameworks that only handle core tasks like routing and sessions, giving developers freedom over components, patterns, and conventions.
2) In contrast, full-stack frameworks like Rails are more rigid since they enforce certain development philosophies and replacing core components requires more work.
3) For small projects, microframeworks keep code small since only necessary components are included, whereas full-stack frameworks start at a larger size and grow from there.
This document discusses the prototype design pattern, which specifies the kind of objects to create using a prototypical instance and creates new objects by copying its prototype. It allows specifying new objects at runtime without creating many classes or inheritance structures. The prototype pattern is useful when object initialization is expensive and there will be few variations, as it avoids expensive creation from scratch by cloning pre-initialized prototypes instead. Some consequences are that object classes can be added or removed dynamically by cloning prototypes as needed.
This document contains tips for working with the SharePoint Framework (SPFx). It begins with an introduction of the speaker and is followed by 15 tips for adopting SPFx including managing web part properties, dependencies, development environments, and automating deployment. The tips provide guidance on issues that may be encountered when developing SPFx solutions such as only bootstrapping Angular once, using dummy data, locking dependencies, and using version control for the SPFx Yeoman generator.
Introduction to Machine Learning in Python using Scikit-LearnAmol Agrawal
This document outlines a proposed workshop on machine learning in Python using the Scikit-Learn module. The workshop will introduce machine learning concepts and how to use Scikit-Learn to implement supervised and unsupervised machine learning algorithms for classification, regression, dimensionality reduction, and clustering. It will provide example code notebooks and exercises for participants to get hands-on experience applying machine learning to real-world examples and incorporating machine learning into their own work.
AWS DeepLens Workshop: Building Computer Vision Applications - BDA201 - Atlan...Amazon Web Services
In this workshop, learn how to build and deploy computer vision models using the AWS DeepLens deep learning-enabled video camera. Learn to build a machine learning model from scratch using Amazon SageMaker, and get hands-on experience with AWS DeepLens by extending that model to build an end-to-end AI application using Amazon Rekognition. Attendees also learn about use cases built by the community which integrate other AWS services and extend the functionality of AWS DeepLens. Please note, you must have an AWS account to participate in this workshop. If setting up a new account, please do this at least 24 hours in advance of the workshop.
This document summarizes a presentation about deploying custom machine learning models using Amazon SageMaker. It discusses:
1. An overview of machine learning, AWS SageMaker, and how SageMaker works to build, train, test, tune and deploy models.
2. The process for deploying a fully custom ML model with SageMaker, including building the model, defining inference code, creating a SageMaker container, and deploying the model as an endpoint.
3. A demo of this process showing how to create a model, endpoint configuration, and endpoint to deploy a custom model and invoke it via an API.
Sagemaker is a fully managed AWS service that facilitates machine learning development including data preparation, model training, evaluation and deployment. It provides various tools like pretrained models, algorithms, notebooks and frameworks to simplify and accelerate the ML workflow. Some key Sagemaker components are notebooks, Studio, algorithms like linear learner and frameworks like TensorFlow that can be used to build and deploy machine learning models.
История одного успешного ".NET" проекта, Александр СугакSigma Software
The document describes an IoT project built using an unconventional technology stack. It discusses the current status quo of typical .NET projects using standard Microsoft technologies. It then presents a case study of an IoT project built using non-standard technologies like F#, Suave, ReactJS, and running on Linux. It outlines the principles used in selecting the technology stack, including preferring less magic frameworks, ensuring predictable and reproducible builds, shortening feedback loops, and focusing on reusable skills and knowledge.
- The document is a programming tutorial that introduces Python and MATLAB for programming in medical imaging.
- It discusses what a computer program is, explains programming languages and code, and why learning to program is useful.
- The tutorial compares Python and MATLAB, noting that both can be used for the course but examples will focus on MATLAB. It outlines differences like MATLAB requiring a license while Python is free.
Strata CA 2019: From Jupyter to Production Manu MukerjiManu Mukerji
Proposed title
From Jupyter to production
Description of the presentation
Jupyter is very popular for data science, data exploration and visualization, this talk is about how to use it in for AI/ML in a production environment.
General Flow of talk:
How things can go wrong with QA, Production releases when using a notebook
Common Jupyter ML examples
Standard ML flow
Training in production
Model creation
Testing in production
Papermill and Jupyter
Production workflows with Sagemaker
Speaker
Manu Mukerji is senior director of data, machine learning, and analytics at 8×8. Manu’s background lies in cloud computing and big data, working on systems handling billions of transactions per day in real time. He enjoys building and architecting scalable, highly available data solutions and has extensive experience working in online advertising and social media.
Make Cross-platform Mobile Apps Quickly - SIGGRAPH 2014Gil Irizarry
This document provides a summary of a presentation about making cross-platform mobile apps quickly using open source tools. It discusses using PhoneGap to create apps using HTML, CSS, and JavaScript that are cross-platform. It provides examples of building simple apps demonstrating concepts like accessing device data, using maps, touch events, and animation. The examples are meant to illustrate how to create mobile apps that work across Android and iOS without using their native languages.
Postmortem of a uwp xaml application developmentDavid Catuhe
This document provides tips and tricks for developing Universal Windows Platform (UWP) applications using C# and XAML, including techniques for adapting the user interface for different devices, persisting navigation state, improving performance, debugging issues, and deploying apps to the Microsoft Store. Specific strategies covered include using a reflow technique to rearrange UI elements, serializing navigation history with a stack of objects, avoiding reliance on NavigationCache, using debug stopwatches and profiling tools, adding exception handling, and integrating AppInsights for analytics and crash reporting.
Application Development Using Java - DIYComputerScience Courseparag
This document describes a project-based course to build a Minesweeper game from scratch using Java. The 13-section course breaks the project into incremental parts, teaching programming concepts at each stage like object-oriented design, testing, persistence, web development, and more. Learners will build both desktop and web versions, while practicing techniques such as refactoring, internationalization, logging, and database integration. The goal is for students to learn Java by completing the projects and reading provided code at their own pace.
AWS DeepLens Workshop: Building Computer Vision Applications - BDA201 - Anahe...Amazon Web Services
In this workshop, learn how to build and deploy computer vision models using the AWS DeepLens deep learning-enabled video camera. Learn to build a machine learning model from scratch using Amazon SageMaker, and get hands-on experience with AWS DeepLens by extending that model to build an end-to-end AI application using Amazon Rekognition. Attendees also learn about use cases built by the community which integrate other AWS services and extend the functionality of AWS DeepLens. Please note, you must have an AWS account to participate in this workshop. If setting up a new account, please do so at least 24 hours in advance of the workshop.
MLOps and Reproducible ML on AWS with Kubeflow and SageMakerProvectus
Looking to implement MLOps using AWS services and Kubeflow? Come and learn about machine learning from the experts of Provectus and Amazon Web Services (AWS)!
Businesses recognize that machine learning projects are important but go beyond just building and deploying models, which is mostly done by organizations. Successful ML projects entail a complete lifecycle involving ML, DevOps, and data engineering and are built on top of ML infrastructure.
AWS and Amazon SageMaker provide a foundation for building infrastructure for machine learning while Kubeflow is a great open source project, which is not given enough credit in the AWS community. In this webinar, we show how to design and build an end-to-end ML infrastructure on AWS.
Agenda
- Introductions
- Case Study: GoCheck Kids
- Overview of AWS Infrastructure for Machine Learning
- Provectus ML Infrastructure on AWS
- Experimentation
- MLOps
- Feature Store
Intended Audience
Technology executives & decision makers, manager-level tech roles, data engineers & data scientists, ML practitioners & ML engineers, and developers
Presenters
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Qingwei Li, ML Specialist Solutions Architect, AWS
Feel free to share this presentation with your colleagues and don't hesitate to reach out to us at info@provectus.com if you have any questions!
REQUEST WEBINAR: https://provectus.com/webinar-mlops-and-reproducible-ml-on-aws-with-kubeflow-and-sagemaker-aug-2020/
The Good, the Bad and the Ugly things to do with androidStanojko Markovik
The document discusses good practices, bad habits, and ugly issues that can arise when developing Android applications.
The good section covers clean code practices, using libraries, XML resources, and lifecycle methods. The bad section notes lazy practices like ignoring lifecycles and leaving cursors open. The ugly section describes ANRs, memory issues like bitmaps and strings, and overuse of logs and notifications. Developers are advised to follow proper patterns, manage resources carefully, and leverage tools like TraceView and MAT to debug problems.
Building Instruqt, a scalable learning platformInstruqt
On February 15th I gave a talk on how we built Instruqt. We use Kubernetes, Terraform and Google Cloud, and in my talk I explain the benefits of using these tools and services correctly.
Version Control in Machine Learning + AI (Stanford)Anand Sampat
Starting with outlining the history of conventional version control before diving into explaining QoDs (Quantitative Oriented Developers) and the unique problems their ML systems pose from an operations perspective (MLOps). With the only status quo solutions being proprietary in-house pipelines (exclusive to Uber, Google, Facebook) and manual tracking/fragile "glue" code for everyone else.
Datmo works to solve this issue by empowering QoDs in two ways: making MLOps manageable and simple (rather than completely abstracted away) as well as reducing the amount of glue code so to ensure more robust end-to-end pipelines.
This goes through a simple example of using Datmo with an Iris classification dataset. Later workshops will expand to show how Datmo can work with other data pipelining tools.
Why you should consider a microframework for your next web projectJoaquín Muñoz M.
1) Microframeworks are lightweight frameworks that only handle core tasks like routing and sessions, giving developers freedom over components, patterns, and conventions.
2) In contrast, full-stack frameworks like Rails are more rigid since they enforce certain development philosophies and replacing core components requires more work.
3) For small projects, microframeworks keep code small since only necessary components are included, whereas full-stack frameworks start at a larger size and grow from there.
This document discusses the prototype design pattern, which specifies the kind of objects to create using a prototypical instance and creates new objects by copying its prototype. It allows specifying new objects at runtime without creating many classes or inheritance structures. The prototype pattern is useful when object initialization is expensive and there will be few variations, as it avoids expensive creation from scratch by cloning pre-initialized prototypes instead. Some consequences are that object classes can be added or removed dynamically by cloning prototypes as needed.
This document contains tips for working with the SharePoint Framework (SPFx). It begins with an introduction of the speaker and is followed by 15 tips for adopting SPFx including managing web part properties, dependencies, development environments, and automating deployment. The tips provide guidance on issues that may be encountered when developing SPFx solutions such as only bootstrapping Angular once, using dummy data, locking dependencies, and using version control for the SPFx Yeoman generator.
Introduction to Machine Learning in Python using Scikit-LearnAmol Agrawal
This document outlines a proposed workshop on machine learning in Python using the Scikit-Learn module. The workshop will introduce machine learning concepts and how to use Scikit-Learn to implement supervised and unsupervised machine learning algorithms for classification, regression, dimensionality reduction, and clustering. It will provide example code notebooks and exercises for participants to get hands-on experience applying machine learning to real-world examples and incorporating machine learning into their own work.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
1. 1
OCTOBER 2015
Detectron on Amazon SageMaker
Matt McDonnell, Data Scientist at Metail
Cambridge AWS Meetup 2018-02-06
2. 2
Using Amazon SageMaker to try out Deep
Learning applications
• Discovering something interesting:
Detectron
In particular Mask R-CNN could be
useful for image segmentation
• Starting an Amazon SageMaker
notebook
• Running Open Source demo code
– Cloning GitHub repos
– Running demo notebook
• Cleaning up
Overview
Examples of Metail Composed Photography
We’re not currently using Mask R-CNN for this
and it may not be a good use case - but worth
trying out