1) Cloud computing platforms like Amazon SageMaker provide tools for building, training, and deploying machine learning models at scale.
2) SageMaker allows users to launch ML training jobs using popular frameworks and then deploy trained models for inference through hosted endpoints.
3) The document outlines the full SageMaker workflow from problem definition, data processing, model training, deployment to multiple environments, and retraining as needed based on new data.
Cognitive Automation: What does success look like? IBM
We hear about cognitive automation. But what does success look like? Meet Cognitive Assist, our virtual agent. These virtual agents, powered by Watson, have ingested a vast corpus of knowledge about the applications IBM support, so they can provide the guidance an experienced coach could give – consistently and in real time. Read more about how Cognitive Assist can help you.
IT Operation Management Automation Roadmap post PandemicManasKumarLenka1
Captures a roadmap as to how Post Pandemic, Organizations Like CGI can in grow in ITOM automation space considering they have existing IP and experience IN RPA , BPM space
TensorFlow London 18: Dr Alastair Moore, Towards the use of Graphical Models ...Seldon
Abstract: Recent developments in understanding technology diffusion and business strategy lend themselves towards analysis as directed graphs. Alastair will briefly introduce a Wardley Map, a directed dependency graph situated in a metric space. I will highlight aspects of this representation that lend themselves to analysis using dynamic graphical models. I will discuss some preliminary thinking about modelling aspects of a business, specifically those that are dependent on machine learning systems.
Bio: Alastair is a UCL Computer Science PhD (Computer Vision) with broad experience of applied Machine Learning and Statistical Analysis in a variety of settings. His background includes stints with corporate research, internet startups and universities. He was on the founding team on spin-out Satalia.com (Data Science) and venture backed WeArePopUp.com (Real Estate contracting) and helped setup the IDEALondon innovation centre with UCL and Cisco Systems. His primary objective at Mishcon de Reya is to ensure the firm consistently uses data and machine learning techniques to support and improve its business process, and helps advise its clients on innovation and technology. Alastair continues to maintain an active teaching role in the UCL School of Management (Predictive systems) the Cambridge Judge Business School (Entrepreneurship) and Peking University, Beijing (Technology innovation). His research interests include technology strategy, smart contracting and computational law.
Olivier Blais: Want to adopt AI in your business: good luck!Lviv Startup Club
Olivier Blais: Want to adopt AI in your business: good luck!
Data Science Online Camp 2021
Website - https://dscamp.org/
Youtube - https://www.youtube.com/startuplviv
FB - https://www.facebook.com/Data-Science-Camp-103012708431833
IBM i & digital transformation - Presentation & basic demo
IBM Watson Studio, IBM DSX Local w/ Open Source (Spark) & IBM Technology (OpenPower, CAPI, NVLINK)
Cognitive Automation: What does success look like? IBM
We hear about cognitive automation. But what does success look like? Meet Cognitive Assist, our virtual agent. These virtual agents, powered by Watson, have ingested a vast corpus of knowledge about the applications IBM support, so they can provide the guidance an experienced coach could give – consistently and in real time. Read more about how Cognitive Assist can help you.
IT Operation Management Automation Roadmap post PandemicManasKumarLenka1
Captures a roadmap as to how Post Pandemic, Organizations Like CGI can in grow in ITOM automation space considering they have existing IP and experience IN RPA , BPM space
TensorFlow London 18: Dr Alastair Moore, Towards the use of Graphical Models ...Seldon
Abstract: Recent developments in understanding technology diffusion and business strategy lend themselves towards analysis as directed graphs. Alastair will briefly introduce a Wardley Map, a directed dependency graph situated in a metric space. I will highlight aspects of this representation that lend themselves to analysis using dynamic graphical models. I will discuss some preliminary thinking about modelling aspects of a business, specifically those that are dependent on machine learning systems.
Bio: Alastair is a UCL Computer Science PhD (Computer Vision) with broad experience of applied Machine Learning and Statistical Analysis in a variety of settings. His background includes stints with corporate research, internet startups and universities. He was on the founding team on spin-out Satalia.com (Data Science) and venture backed WeArePopUp.com (Real Estate contracting) and helped setup the IDEALondon innovation centre with UCL and Cisco Systems. His primary objective at Mishcon de Reya is to ensure the firm consistently uses data and machine learning techniques to support and improve its business process, and helps advise its clients on innovation and technology. Alastair continues to maintain an active teaching role in the UCL School of Management (Predictive systems) the Cambridge Judge Business School (Entrepreneurship) and Peking University, Beijing (Technology innovation). His research interests include technology strategy, smart contracting and computational law.
Olivier Blais: Want to adopt AI in your business: good luck!Lviv Startup Club
Olivier Blais: Want to adopt AI in your business: good luck!
Data Science Online Camp 2021
Website - https://dscamp.org/
Youtube - https://www.youtube.com/startuplviv
FB - https://www.facebook.com/Data-Science-Camp-103012708431833
IBM i & digital transformation - Presentation & basic demo
IBM Watson Studio, IBM DSX Local w/ Open Source (Spark) & IBM Technology (OpenPower, CAPI, NVLINK)
Mindtree leverages its performance engineering services to develop software products and applications that perform optimally in normal as well as extreme load conditions. This reduces the number of failures related to performance and availability. We offer performance engineering services across a wide range of verticals and applications based on client server, Web technologies, Web services and ERP.
This session was recorded in NYC on October 22nd, 2019.
Video recording of the session can be viewed here: https://youtu.be/Z0quYTZr6C0
Description: How businesses can recession proof themselves by using the power of the Ascend Analytical Sandbox; and how Experian is leveraging its vast data to make sure every borrower is presented in the best light in front of the lenders.
Bio: Ankit is the Product & Innovation Expert at Experian leading the overall roadmap for the Ascend Analytical Sandbox; a one-stop shop for insights, model development, and results measurement.
BigMLSchool: ML Platforms and AutoML in the EnterpriseBigML, Inc
An introductory session on the current situation of Machine Learning, the importance of ML platforms and AutoML, and why ML needs to be properly taught at schools and universities.
The lecturer is Ed Fernández, Board Director at BigML and Arowana International, a Private Equity firm, Faculty at Northeastern University (the Silicon Valley campus), lecturer at Headspring Corporate Learning (the Joint Venture of Financial Times and IE Business School), and mentor at Berkeley Sutardja Center for Entrepreneurship and Technology.
*Machine Learning School for Business Schools 2021: Virtual Conference.
Human-Centered AI: Scalable, Interactive Tools for Interpretation and Attribu...polochau
Artificial intelligence and machine learning models are growing increasingly available, but many models offer predictions that are difficult to understand, evaluate and ultimately act upon. We present how scalable interactive visualization can be used to amplify people’s ability to understand and interact with large-scale data and complex models. We sample from projects where interactive visualization has provided key leaps of insight, from increased model explorability with models trained on millions of instances (ActiVis deployed with Facebook), increased usability for non-experts about state-of-the-art AI (GAN Lab open-sourced with Google Brain; went viral!), and our latest work Summit, the first interactive system that scalably summarizes and visualizes what features a deep learning model has learned and how those features interact to make predictions. We conclude by highlighting the next visual analytics research frontiers in AI.
DevOps took us from SysAdmins to DeployAdmins to improve availability but came with a tidal wave of tools and environments, leaving detecting anomalies and finding root cause a task for the overcrowded war-room of siloed experts.
Developers need to understand infrastructure, and operations needs to understand the SDLC.
The good news is AIOps can help!
Let’s look at what AIOps can realistically do for us, identify criteria for where to automate and lay out the stepping stones for achieving AIOps.
Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017StampedeCon
This talk will walk through the important building blocks of Automated AI. Rajiv will highlight the current gaps in the analytics organizations, how to close those gaps using automated AI. Some of the issues discussed around automated AI are the accuracy of models, tradeoffs around control when using automation, interpretability of models, and integration with other tools. These issues will be highlighted with examples of automated analytics in different industries. The talk will end with some examples of how automated AI in the hands of data scientists and business analysts is transforming analytic teams and organizations.
How Applying an AI-Defined Infrastructure Can Boost Data Center OperationsCognizant
An artificial-intelligence-based infrastructure that uses the data available within the data center to optimize and automate infrastructure operations can enhance operational efficiencies and improve the quality of service offered to the business.
A well-publicised PwC report projects that by 2030, “smart automation” technologies could contribute up to 14% of global GDP (around 10% to UK GDP). It also believesthat the long-term net effect of automation on the economy will be positive. Thus, despite the above uncomplimentary words from a scientist whose work still drives our modern digital economy,the verdict is in – technologies like Robotic Process Automation (RPA) are here to stay.
AI in the Enterprise: Past, Present & Future - StampedeCon AI Summit 2017StampedeCon
Artificial Intelligence has entered a renaissance thanks to rapid progress in domains as diverse as self-driving cars, intelligent assistants, and game play. Underlying this progress is Deep Learning – driven by significant improvements in Graphic Processing Units and computational models inspired by the human brain that excel at capturing structures hidden in massive complex datasets. These techniques have been pioneered at research universities and digital giants but mainstream enterprises are starting to apply them as open source tools and improved hardware become available. Learn how AI is impacting analytics today and in the future.
Learn how AI is affecting the enterprise including applications like fraud detection, mobile personalization, predicting failures for IoT and text analysis to improve call center interactions. We look at how practical examples of assessing the opportunity for AI, phased adoption, and lessons going from research, to prototype, to scaled production deployment.
SigOpt founder and CEO, Scott Clark, PhD, explains the tradeoffs you'll want to consider when designing your modeling platform and integrating hyperparameter optimization to enhance data scientist productivity.
Borys Pratsiuk: How ML technology helps solving specific business problemsLviv Startup Club
Borys Pratsiuk: How ML technology helps solving specific business problems
AI & BigData Online Day 2021
Website - http://aiconf.com.ua
Youtube - https://www.youtube.com/startuplviv
FB - https://www.facebook.com/aiconf
Machine Learning for Logistics: Predicting Expedition Outcome - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
In Machine Learning in Credit Risk Modeling, we provide an explanation of the main Machine Learning models used in James so that Efficiency does not come at the expense of Explainability.
(Contact Yvan De Munck for more info or to receive other and future updates on the subject @yvandemunck or yvan@james.finance)
"Scaling ML from 0 to millions of users", Julien Simon, AWS Dev Day Kyiv 2019Provectus
AWS Dev Day Kyiv 2019
Track: Analytics & Machine Learning
Session: ""Scaling ML from 0 to millions of users""
Speaker: Julien Simon, Global AI & Machine Learning Evangelist at AWS
Level: 300
AWS Dev Day is a free, full-day technical event where new developers will learn about some of the hottest topics in cloud computing, and experienced developers can dive deep on newer AWS services.
Provectus has organized AWS Dev Day Kyiv in close collaboration with Amazon Web Services: 800+ participants, 18 sessions, 3 tracks, a really AWSome Day!
Now, together with Zeo Alliance, we're building and nurturing AWS User Group Ukraine — join us on Facebook to stay updated about cloud technologies and AWS services: https://www.facebook.com/groups/AWSUserGroupUkraine
Video: https://www.youtube.com/watch?v=N73u1mx9DqY
Mindtree leverages its performance engineering services to develop software products and applications that perform optimally in normal as well as extreme load conditions. This reduces the number of failures related to performance and availability. We offer performance engineering services across a wide range of verticals and applications based on client server, Web technologies, Web services and ERP.
This session was recorded in NYC on October 22nd, 2019.
Video recording of the session can be viewed here: https://youtu.be/Z0quYTZr6C0
Description: How businesses can recession proof themselves by using the power of the Ascend Analytical Sandbox; and how Experian is leveraging its vast data to make sure every borrower is presented in the best light in front of the lenders.
Bio: Ankit is the Product & Innovation Expert at Experian leading the overall roadmap for the Ascend Analytical Sandbox; a one-stop shop for insights, model development, and results measurement.
BigMLSchool: ML Platforms and AutoML in the EnterpriseBigML, Inc
An introductory session on the current situation of Machine Learning, the importance of ML platforms and AutoML, and why ML needs to be properly taught at schools and universities.
The lecturer is Ed Fernández, Board Director at BigML and Arowana International, a Private Equity firm, Faculty at Northeastern University (the Silicon Valley campus), lecturer at Headspring Corporate Learning (the Joint Venture of Financial Times and IE Business School), and mentor at Berkeley Sutardja Center for Entrepreneurship and Technology.
*Machine Learning School for Business Schools 2021: Virtual Conference.
Human-Centered AI: Scalable, Interactive Tools for Interpretation and Attribu...polochau
Artificial intelligence and machine learning models are growing increasingly available, but many models offer predictions that are difficult to understand, evaluate and ultimately act upon. We present how scalable interactive visualization can be used to amplify people’s ability to understand and interact with large-scale data and complex models. We sample from projects where interactive visualization has provided key leaps of insight, from increased model explorability with models trained on millions of instances (ActiVis deployed with Facebook), increased usability for non-experts about state-of-the-art AI (GAN Lab open-sourced with Google Brain; went viral!), and our latest work Summit, the first interactive system that scalably summarizes and visualizes what features a deep learning model has learned and how those features interact to make predictions. We conclude by highlighting the next visual analytics research frontiers in AI.
DevOps took us from SysAdmins to DeployAdmins to improve availability but came with a tidal wave of tools and environments, leaving detecting anomalies and finding root cause a task for the overcrowded war-room of siloed experts.
Developers need to understand infrastructure, and operations needs to understand the SDLC.
The good news is AIOps can help!
Let’s look at what AIOps can realistically do for us, identify criteria for where to automate and lay out the stepping stones for achieving AIOps.
Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017StampedeCon
This talk will walk through the important building blocks of Automated AI. Rajiv will highlight the current gaps in the analytics organizations, how to close those gaps using automated AI. Some of the issues discussed around automated AI are the accuracy of models, tradeoffs around control when using automation, interpretability of models, and integration with other tools. These issues will be highlighted with examples of automated analytics in different industries. The talk will end with some examples of how automated AI in the hands of data scientists and business analysts is transforming analytic teams and organizations.
How Applying an AI-Defined Infrastructure Can Boost Data Center OperationsCognizant
An artificial-intelligence-based infrastructure that uses the data available within the data center to optimize and automate infrastructure operations can enhance operational efficiencies and improve the quality of service offered to the business.
A well-publicised PwC report projects that by 2030, “smart automation” technologies could contribute up to 14% of global GDP (around 10% to UK GDP). It also believesthat the long-term net effect of automation on the economy will be positive. Thus, despite the above uncomplimentary words from a scientist whose work still drives our modern digital economy,the verdict is in – technologies like Robotic Process Automation (RPA) are here to stay.
AI in the Enterprise: Past, Present & Future - StampedeCon AI Summit 2017StampedeCon
Artificial Intelligence has entered a renaissance thanks to rapid progress in domains as diverse as self-driving cars, intelligent assistants, and game play. Underlying this progress is Deep Learning – driven by significant improvements in Graphic Processing Units and computational models inspired by the human brain that excel at capturing structures hidden in massive complex datasets. These techniques have been pioneered at research universities and digital giants but mainstream enterprises are starting to apply them as open source tools and improved hardware become available. Learn how AI is impacting analytics today and in the future.
Learn how AI is affecting the enterprise including applications like fraud detection, mobile personalization, predicting failures for IoT and text analysis to improve call center interactions. We look at how practical examples of assessing the opportunity for AI, phased adoption, and lessons going from research, to prototype, to scaled production deployment.
SigOpt founder and CEO, Scott Clark, PhD, explains the tradeoffs you'll want to consider when designing your modeling platform and integrating hyperparameter optimization to enhance data scientist productivity.
Borys Pratsiuk: How ML technology helps solving specific business problemsLviv Startup Club
Borys Pratsiuk: How ML technology helps solving specific business problems
AI & BigData Online Day 2021
Website - http://aiconf.com.ua
Youtube - https://www.youtube.com/startuplviv
FB - https://www.facebook.com/aiconf
Machine Learning for Logistics: Predicting Expedition Outcome - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
In Machine Learning in Credit Risk Modeling, we provide an explanation of the main Machine Learning models used in James so that Efficiency does not come at the expense of Explainability.
(Contact Yvan De Munck for more info or to receive other and future updates on the subject @yvandemunck or yvan@james.finance)
"Scaling ML from 0 to millions of users", Julien Simon, AWS Dev Day Kyiv 2019Provectus
AWS Dev Day Kyiv 2019
Track: Analytics & Machine Learning
Session: ""Scaling ML from 0 to millions of users""
Speaker: Julien Simon, Global AI & Machine Learning Evangelist at AWS
Level: 300
AWS Dev Day is a free, full-day technical event where new developers will learn about some of the hottest topics in cloud computing, and experienced developers can dive deep on newer AWS services.
Provectus has organized AWS Dev Day Kyiv in close collaboration with Amazon Web Services: 800+ participants, 18 sessions, 3 tracks, a really AWSome Day!
Now, together with Zeo Alliance, we're building and nurturing AWS User Group Ukraine — join us on Facebook to stay updated about cloud technologies and AWS services: https://www.facebook.com/groups/AWSUserGroupUkraine
Video: https://www.youtube.com/watch?v=N73u1mx9DqY
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.
Webinar GLUGNet - Machine Learning.Net and Windows Machine LearningBruno Capuano
Slides used during the webinar session on Machine Learning.Net and Windows Machine Learning on 2019 02 21 for the GLUGnet User Group for .NET, Web, Mobile, Database
Microsoft has released Automated ML technologies for developers through ML.NET, Azure ML Service, and Azure Databricks. This presenter is a data scientist and Microsoft architect, and will give a comprehensive overview of the utility and use case of this automated technology for production solutions. The presentation includes code you can try now.
Workshop slides for the introduction to Amazon SageMaker, and integration of Amazon SageMaker with other tools within your AWS environment. Visit https://aws.amazon.com/sagemaker for more information.
Building WhereML, an AI Powered Twitter Bot for Guessing Locations of Picture...Amazon Web Services
The WhereML Twitter bot is built on the LocationNet model which is trained with the Berkley Multimedia Commons public dataset of 33.9 million geotagged images from Flickr (and other sources). The model is based on a ResNet-101 architecture and adds a classification layer that splits the earth into ~15000 cells created with Google’s S2 spherical geometry library. This model is based on prior work completed at Berkley and Google.
In this session we’ll start by describing AI in general terms then diving into deep learning and the MXNet framework. We’ll describe the LocationNet model in detail and show how it is trained and created in Amazon SageMaker. Finally, we’ll talk about the Twitter Account Activity webhooks API and how to interact with it using an API Gateway and AWS Lambda function.
Attendees are encouraged to interact with the bot in real-time at whereml.bot or on twitter at @WhereML
All code used in this project is open source and was written live on twitch.tv/aws and attendees are encouraged to experiment with it.
NEW LAUNCH! Integrating Amazon SageMaker into your Enterprise - MCL345 - re:I...Amazon Web Services
Amazon SageMaker is a fully managed platform for data scientists and developers to build, train and deploy machine learning models in production applications. In this workshop, you will learn how to integrate Amazon SageMaker with other AWS services in order to meet enterprise requirements. Using Amazon S3, Amazon Glue, Amazon KMS, Amazon SageMaker, Amazon CodeStar, Amazon ECR, IAM; we will walkthrough the machine learning lifecycle in an integrated AWS environment and discuss best practices.Attendees must have some familiarities with AWS products as well as a good understanding of machine learning theory. The dataset for the workshop will be provided.
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/
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI.
This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
This presentation is the fourth of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
Train ML Models Using Amazon SageMaker with TensorFlow - SRV336 - Chicago AWS...Amazon Web Services
Amazon SageMaker is a fully managed platform that enables developers and data scientists to build, train, and deploy machine learning (ML) models in production applications easily and at scale. In this chalk talk, we dive deep into training an ML model based on the TensorFlow framework. We discuss the specifics of training a model through Amazon SageMaker by taking an algorithm and running it on a training cluster in an auto-scaling group. This session showcases the scalability of training that is possible with Amazon SageMaker, which reduces the time and cost of training runs.
Vamos explorar como podemos utilizar aprendizagem de máquina, de forma fácil, nas aplicações que desenvolvemos no dia a dia utilizando nossas habilidades em .NET através do ML.NET, um framework open source e cross-platform!
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
9. AI Frameworks
AWS Deep Learning AMI
• Easy-to-launch tutorials
• Hassle-free setup and configuration
• Pay only for what you use
• Accelerate your model training and deployment
• Support for popular deep learning frameworks
15. Amazon SageMaker is a fully managed service that enable developers
and data scientist to quickly and easilybuild, train anddeploymachine
learning models at any scale..
..from idea to production.
20. 4
ML Hosting Service
Different Versions of
the same inference
code in ECR. Prod is
the master container,
it will serve over 50%
of requests.
Amazon ECR
Model Artifacts
21. 4
ML Hosting Service
Amazon ECR
Model Artifacts
ModelName: prod
Create a Model
Different Versions of
the same inference
code in ECR. Prod is
the master container,
it will serve over 50%
of requests.
22. 4
ML Hosting Service
Amazon ECR
Model Artifacts
Model Versions
Create a versions of
a Model
Different Versions of
the same inference
code in ECR. Prod is
the master container,
it will serve over 50%
of requests.
23. 4
ML Hosting Service
InstanceType: c3.4xlarge
MinInstanceCount: 5
MaxInstanceCount: 20
ModelNmae: prod
VariantName: prodPrimary
VariantWeight: 50
ProductionVariant
Amazon ECR
Model Artifacts
30 50
10 10
Model Versions
Create weighted
ProductionVariants
Different Versions of
the same inference
code in ECR. Prod is
the master container,
it will serve over 50%
of requests.
24. 4
ML Hosting Service
InstanceType: c3.4xlarge
MinInstanceCount: 5
MaxInstanceCount: 20
ModelName: prod
VariantName: prodPrimary
VariantWeight: 50
ProductionVariant
Amazon ECR
Model Artifacts
Endpoint Configuration
30 50
10 10
Model Versions
Create an
EndpointConfiguration
from one or many
ProductionVariants
Different Versions of
the same inference
code in ECR. Prod is
the master container,
it will serve over 50%
of requests.
25. 4
ML Hosting Service
InstanceType: c3.4xlarge
MinInstanceCount: 5
MaxInstanceCount: 20
ModelNmae: prod
VariantName: prodPrimary
VariantWeight: 50
ProductionVariant
Amazon ECR
Model Artifacts
Endpoint Configuration
Inference Endpoint
30 50
10 10
Model Versions
Create an Endpoint
from one
EndpointConfiguration
Different Versions of
the same inference
code in ECR. Prod is
the master container,
it will serve over 50%
of requests.
26. • Training Algorithm / inference code is
packaged in Docker Image on ECR
• SageMaker pulls training algorithm image
from ECR into Model raining Service
• Amazon SM downloads or streams the
training data and runs Training
• After training Amazon SM uploads model
artifacts to Amazon S3
• For Inference, Amazon SM pulls the model
artifacts and the inference image from ECR
into Model Hosting Service
• Amazon SM exposes an inference
endpoint for client application to send
prediction requests
• Ground truth data collected from the client
could be sent into training bucket to retrain
and update the model