The document outlines minimum and recommended requirements for automated deployments. Minimum requirements include consistent deployments with no manual intervention, idempotence, high availability, pre/post condition checks with automated rollbacks, critical state alerting, and transparent logging. Recommended practices include checkpointing, blackout periods, synthetic traffic/processing for validation, and real-time visibility.
The Brain and the Modern AI: Drastic Differences and Curious SimilaritiesIlya Kuzovkin
In this presentation we user Marr's three levels of analysis to approach the question of how similar or different are the brain and the machines. By approaching this discussion in a more structured manner we find that the answer is different depending on the level of abstraction we are operating at.
The document discusses software distribution and the challenges involved. It covers topics like artifacts, dependencies, packaging, repositories, and integration with configuration management tools like Puppet and Chef. Artifacts need to have consistency checks, file lists, resolved dependencies, versioning, and be reproducible. Dependencies require testing, distribution from upstream repositories, and limiting their number. Packaging helps with signing, repositories, and configuration management integration.
商業簡報網 X matter lab x 501 Free Picture Layouts-5/5明文 韓
The document is a presentation that contains over 70 slides. Each slide displays a picture layout with variations of the text "The quick brown fox jumps over the lazy dog." and slide numbers. The layouts change across slides, with some including titles or dividing sections. The overall content appears to be demonstrating different formatting and layout options for presentation slides.
Building the Ideal Stack for Machine LearningSingleStore
This document discusses building an ideal machine learning stack for real-time applications. It provides an overview of machine learning and real-time use cases. It also demonstrates an example machine learning pipeline for fraud detection using labeled historical data to train a model and score new data in real-time. Finally, it discusses industry examples of using machine learning models for advertising optimization, anomaly detection, and image recognition and provides a demo of predicting images from the MNIST data set.
This document discusses the history and future of CAPTCHAs and their relationship with artificial intelligence. It describes how early programs like ELIZA and PARRY helped lay the groundwork for CAPTCHAs. It then reviews the development of CAPTCHAs from 1997 to the present, highlighting key events and technological advances. Finally, it proposes that future CAPTCHAs could use generative adversarial networks (GANs) to generate tests in a way that makes them difficult for AI systems to pass automatically.
The Brain and the Modern AI: Drastic Differences and Curious SimilaritiesIlya Kuzovkin
In this presentation we user Marr's three levels of analysis to approach the question of how similar or different are the brain and the machines. By approaching this discussion in a more structured manner we find that the answer is different depending on the level of abstraction we are operating at.
The document discusses software distribution and the challenges involved. It covers topics like artifacts, dependencies, packaging, repositories, and integration with configuration management tools like Puppet and Chef. Artifacts need to have consistency checks, file lists, resolved dependencies, versioning, and be reproducible. Dependencies require testing, distribution from upstream repositories, and limiting their number. Packaging helps with signing, repositories, and configuration management integration.
商業簡報網 X matter lab x 501 Free Picture Layouts-5/5明文 韓
The document is a presentation that contains over 70 slides. Each slide displays a picture layout with variations of the text "The quick brown fox jumps over the lazy dog." and slide numbers. The layouts change across slides, with some including titles or dividing sections. The overall content appears to be demonstrating different formatting and layout options for presentation slides.
Building the Ideal Stack for Machine LearningSingleStore
This document discusses building an ideal machine learning stack for real-time applications. It provides an overview of machine learning and real-time use cases. It also demonstrates an example machine learning pipeline for fraud detection using labeled historical data to train a model and score new data in real-time. Finally, it discusses industry examples of using machine learning models for advertising optimization, anomaly detection, and image recognition and provides a demo of predicting images from the MNIST data set.
This document discusses the history and future of CAPTCHAs and their relationship with artificial intelligence. It describes how early programs like ELIZA and PARRY helped lay the groundwork for CAPTCHAs. It then reviews the development of CAPTCHAs from 1997 to the present, highlighting key events and technological advances. Finally, it proposes that future CAPTCHAs could use generative adversarial networks (GANs) to generate tests in a way that makes them difficult for AI systems to pass automatically.
Artificial intelligence and machine learning document summarized in 3 sentences:
The document discusses the history and concepts of artificial intelligence including machine learning, deep learning, and neural networks. It provides brief overviews of supervised and unsupervised learning as well as recent milestones and risks of AI. The future of AI is explored including automation, assistants, climate change solutions, and cyborg technology.
Multipying the power of your agile team with DesignPhil Barrett
The presentation covers
Why software teams need design (with a nice little case study)
How good designers help your team work better (some things good designers do)
How to navigate the change (a few ways to think about changing your team's culture and process to make design successful and value-adding)
Intuition & Use-Cases of Embeddings in NLP & beyondC4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2LZgiKO.
Jay Alammar talks about the concept of word embeddings, how they're created, and looks at examples of how these concepts can be carried over to solve problems like content discovery and search ranking in marketplaces and media-consumption services (e.g. movie/music recommendations). Filmed at qconlondon.com.
Jay Alammar is VC and ML Explainer at STVcapital. He has helped tens of thousands of people wrap their heads around complex ML topics. He harnesses a visual, highly-intuitive presentation style to communicate concepts ranging from the most basic intros to data analysis, interactive intros to neural networks, to dissections of state-of-the-art models in Natural Language Processing.
What makes software development complex isn't the code, it's the humans. The most effective way to improve our capabilities in software development is to better understand ourselves.
In this talk, I'll introduce a conceptual model for human interaction, identity, culture, communication, relationships, and learning based on the foundational model of Idea Flow. If you were to write a simulator to describe the interaction of humans, this talk would describe the architecture.
Learn how to understand the humans on your team and fix the bugs in communication, by thinking about your teammates like code!
Edit
Archive
Delete
I'm not a scientist or a psychologist. These ideas are based on a combination of personal experience, reading lots of cognitive science books, and a couple years of running experiments on developers. As I struggled through the challenges of getting a software concept from my head to another developer's head (interpersonal Idea Flow), I learned a whole lot about human interaction.
As software developers, we have to work together, think together, and solve problems together to do our jobs. Code? We get it. Humans? WTF?!
Fortunately, humans are predictably irrational, predictably emotional, and predictably judgmental creatures. Of course those pesky humans will always do a few unexpected things, but once we know the algorithm for peace and harmony among humans, we can start debugging the communication problems on our team.
This document discusses using the ELK stack with Beats to provide logging and metrics capabilities. It introduces Beats as lightweight data shippers designed by Elasticsearch to collect and export data from applications and systems. The core Beats covered are Filebeat, Metricbeat, Packetbeat, and Winlogbeat. It highlights how Beats can be used to collect logs and metrics from applications, systems, containers and networks in a lightweight way. Examples are provided of using Metricbeat dashboards and community Beats.
DOXLON November 2016 - ELK Stack and Beats Outlyer
Jon Hammant, Head of Cloud & DevOps for UK & EU for Epam Systems, presented an overview of using the ELK stack together with the Beats Plugin data shippers to provide detailed system metrics, network traffic, file analysis, and more. In addition, he provided an overview of how to monitor multiple Docker containers in a cloud native environment, with logs sent back to a central host.
On Inherent Complexity of Computation, by Attila SzegediZeroTurnaround
The system you just recently deployed is likely an application processing some data, likely relying on some configuration, maybe using some plugins, certainly relying on some libraries, using services of an operating system running on some physical hardware. The previous sentence names 7 categories into which we compartmentalise various parts of a computation process that’s in the end going on in a physical world. Where do you draw the line of functionality between categories? From what vantage points do these distinctions become blurry? Finally, how does it all interact with the actual physical world in which the computation takes place? (What is the necessary physical minimum required to perform a computation, anyway?) Let’s make a journey from your AOP-assembled, plugin-injected, YAML-configured, JIT compiled, Hotspot-executed, Linux-on-x86 hosted Java application server talking JSON-over-HTTP-over-TCP-over-IP-over-Ethernet all the way down to electrons. And then back. Recorded at GeekOut 2013.
Reactive Streams and the Wide World of GroovySteve Pember
The concept of Reactive Streams (aka Reactive Extensions, Reactive Functional Programming, or simply Rx) has become increasingly popular recently, and with good reason. The Reactive Streams specification provides a universal abstraction for asynchronously processing data received across multiple sources (e.g. database, user input, third-party services), and includes mechanisms for controlling the rate at which data is received. This makes it a powerful tool within a Microservice platform. And did we mention that the Groovy lang community is quite involved?
In this talk we’ll explore the various features and concepts of Reactive Streams. We’ll talk about some typical use cases for Rx and more importantly, how to implement them. We’ll focus primarily on RxGroovy and Ratpack, then provide example implementations that show you how to get started with this powerful technique.
Our hope is that defenders and reverse engineers can make use of the project updates to validate their preparedness and techniques against highly targeted malware. As discussed in our presentation, detection of malicious code in runtime interpreted languages is error prone and difficult. Shortly after our initial presentation at INFILTRATE, Kaspersky created an AV signature that flagged as malicious many of the most popular GO language applications such as Docker, a Bitcoin wallet and the actual Golang installer in an attempt to flag EBOWLA binaries – oops.
We’ve updated the project to include a new loader for PowerShell. This ubiquitous Windows scripting language is widely used in offensive testing and by defenders for incident response. Now the incident responder will need to be proficient in PowerShell debugging to begin the task of decrypting targeted malware that could also end up being more PowerShell! Post-Ekoparty, the team is working on a traditional loader using C++ compiled code, so stay tuned and visit our EBOWLA GitHub page for future updates.
Our hope is that defenders and reverse engineers can make use of the project updates to validate their preparedness and techniques against highly targeted malware. As discussed in our presentation, detection of malicious code in runtime interpreted languages is error prone and difficult. Shortly after our initial presentation at INFILTRATE, Kaspersky created an AV signature that flagged as malicious many of the most popular GO language applications such as Docker, a Bitcoin wallet and the actual Golang installer in an attempt to flag EBOWLA binaries – oops.
We’ve updated the project to include a new loader for PowerShell. This ubiquitous Windows scripting language is widely used in offensive testing and by defenders for incident response. Now the incident responder will need to be proficient in PowerShell debugging to begin the task of decrypting targeted malware that could also end up being more PowerShell! Post-Ekoparty, the team is working on a traditional loader using C++ compiled code, so stay tuned and visit our EBOWLA GitHub page for future updates.
How SADI & SHARE help restore the Scientific Method to in silico scienceMark Wilkinson
This document discusses the transition from BioMoby to SADI as a framework for semantic web services. It provides statistics on BioMoby usage and describes demonstrations of how SADI allows complex queries to be answered by discovering and executing relevant web services without a centralized database. The author's vision is for SADI to support the scientific method by enabling personal ontologies and hypotheses to be explicitly expressed and evaluated dynamically.
"The Data Janitor 101", Daniel Molnar, Senior Data Scientist at Microsoft Dataconomy Media
"The Data Janitor 101", Daniel Molnar, Senior Data Scientist at Microsoft
Watch videos from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
https://www.youtube.com/c/DataNatives
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
I'm a generalist in a tight-knit data team enabling data-driven company culture and operations as a data janitor, data analyst and occasional data scientist. Doing ETL and Data Quality, defining company-wide KPIs and metrics with management, producing BI, exploring user behavior to trigger actionable changes in marketing and product, A/B testing, feature engineering for ML, building webapp for secret sauce internal data tool. Long live Bayes and Occam! Tools are mostly bash, Python, Redshift, Tableau, SQL, Python, Flask, Mustache, Wizard, Optimizely.
Chasing Elephants - Alberto Brandolini - Codemotion Rome 2017Codemotion
This document discusses common problems that arise when companies experience accelerated growth in developing software. It describes four phases of growth: inception, ramp up, outsourcing, and aftermath. Key issues discussed include a loss of purpose and energy as companies prioritize features and hiring over working with real users and experts, resulting in purposeless code of declining quality over time. The document advocates for creating an environment where developers can learn, experiment, and focus on users' needs rather than production metrics.
Faster! Faster! Accelerate your business with blazing prototypesOSCON Byrum
Bring your ideas to life! Convince your boss to that open source development is faster and cheaper than the "safe" COTS solution they probably hate anyway. Let's investigate ways to get real-life, functional prototypes up with blazing speed. We'll look at and compare tools for truly rapid development including Python, Django, Flask, PHP, Amazon EC2 and Heroku.
Overcome the 6 Antipatterns of Agile AdoptionAgile Velocity
Presented at Global Scrum Gathering Orlando 2016
Because of benefits like predictability, better quality of products, and faster delivery, many companies have adopted or in the process of adopting Agile. However, there are challenges.
David Hawks, CST and Agile Evangelist, explains the common antipatterns of Agile adoption.
The document discusses the transition from BioMoby to SADI as a framework for semantic web services. It provides statistics on BioMoby usage and describes demonstrations of complex queries being answered through SADI and SHARE without a centralized database. The demonstrations include finding pathways for a protein and lab results for transplant patients. It advocates for SADI to support the scientific method and personal hypotheses through distributed ontologies rather than centralized ones.
This document discusses emerging technologies and their potential impacts. It covers topics like artificial intelligence, quantum computing, robotics, cyborgs, smart materials, fusion power, artificial life, malware, biobots, network bots, and more. The document notes that many of these technologies are still in early experimental stages and face challenges before being ready for widespread use. It also discusses debates around AI safety and the relationship between humans and increasingly intelligent machines.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
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Artificial intelligence and machine learning document summarized in 3 sentences:
The document discusses the history and concepts of artificial intelligence including machine learning, deep learning, and neural networks. It provides brief overviews of supervised and unsupervised learning as well as recent milestones and risks of AI. The future of AI is explored including automation, assistants, climate change solutions, and cyborg technology.
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The presentation covers
Why software teams need design (with a nice little case study)
How good designers help your team work better (some things good designers do)
How to navigate the change (a few ways to think about changing your team's culture and process to make design successful and value-adding)
Intuition & Use-Cases of Embeddings in NLP & beyondC4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2LZgiKO.
Jay Alammar talks about the concept of word embeddings, how they're created, and looks at examples of how these concepts can be carried over to solve problems like content discovery and search ranking in marketplaces and media-consumption services (e.g. movie/music recommendations). Filmed at qconlondon.com.
Jay Alammar is VC and ML Explainer at STVcapital. He has helped tens of thousands of people wrap their heads around complex ML topics. He harnesses a visual, highly-intuitive presentation style to communicate concepts ranging from the most basic intros to data analysis, interactive intros to neural networks, to dissections of state-of-the-art models in Natural Language Processing.
What makes software development complex isn't the code, it's the humans. The most effective way to improve our capabilities in software development is to better understand ourselves.
In this talk, I'll introduce a conceptual model for human interaction, identity, culture, communication, relationships, and learning based on the foundational model of Idea Flow. If you were to write a simulator to describe the interaction of humans, this talk would describe the architecture.
Learn how to understand the humans on your team and fix the bugs in communication, by thinking about your teammates like code!
Edit
Archive
Delete
I'm not a scientist or a psychologist. These ideas are based on a combination of personal experience, reading lots of cognitive science books, and a couple years of running experiments on developers. As I struggled through the challenges of getting a software concept from my head to another developer's head (interpersonal Idea Flow), I learned a whole lot about human interaction.
As software developers, we have to work together, think together, and solve problems together to do our jobs. Code? We get it. Humans? WTF?!
Fortunately, humans are predictably irrational, predictably emotional, and predictably judgmental creatures. Of course those pesky humans will always do a few unexpected things, but once we know the algorithm for peace and harmony among humans, we can start debugging the communication problems on our team.
This document discusses using the ELK stack with Beats to provide logging and metrics capabilities. It introduces Beats as lightweight data shippers designed by Elasticsearch to collect and export data from applications and systems. The core Beats covered are Filebeat, Metricbeat, Packetbeat, and Winlogbeat. It highlights how Beats can be used to collect logs and metrics from applications, systems, containers and networks in a lightweight way. Examples are provided of using Metricbeat dashboards and community Beats.
DOXLON November 2016 - ELK Stack and Beats Outlyer
Jon Hammant, Head of Cloud & DevOps for UK & EU for Epam Systems, presented an overview of using the ELK stack together with the Beats Plugin data shippers to provide detailed system metrics, network traffic, file analysis, and more. In addition, he provided an overview of how to monitor multiple Docker containers in a cloud native environment, with logs sent back to a central host.
On Inherent Complexity of Computation, by Attila SzegediZeroTurnaround
The system you just recently deployed is likely an application processing some data, likely relying on some configuration, maybe using some plugins, certainly relying on some libraries, using services of an operating system running on some physical hardware. The previous sentence names 7 categories into which we compartmentalise various parts of a computation process that’s in the end going on in a physical world. Where do you draw the line of functionality between categories? From what vantage points do these distinctions become blurry? Finally, how does it all interact with the actual physical world in which the computation takes place? (What is the necessary physical minimum required to perform a computation, anyway?) Let’s make a journey from your AOP-assembled, plugin-injected, YAML-configured, JIT compiled, Hotspot-executed, Linux-on-x86 hosted Java application server talking JSON-over-HTTP-over-TCP-over-IP-over-Ethernet all the way down to electrons. And then back. Recorded at GeekOut 2013.
Reactive Streams and the Wide World of GroovySteve Pember
The concept of Reactive Streams (aka Reactive Extensions, Reactive Functional Programming, or simply Rx) has become increasingly popular recently, and with good reason. The Reactive Streams specification provides a universal abstraction for asynchronously processing data received across multiple sources (e.g. database, user input, third-party services), and includes mechanisms for controlling the rate at which data is received. This makes it a powerful tool within a Microservice platform. And did we mention that the Groovy lang community is quite involved?
In this talk we’ll explore the various features and concepts of Reactive Streams. We’ll talk about some typical use cases for Rx and more importantly, how to implement them. We’ll focus primarily on RxGroovy and Ratpack, then provide example implementations that show you how to get started with this powerful technique.
Our hope is that defenders and reverse engineers can make use of the project updates to validate their preparedness and techniques against highly targeted malware. As discussed in our presentation, detection of malicious code in runtime interpreted languages is error prone and difficult. Shortly after our initial presentation at INFILTRATE, Kaspersky created an AV signature that flagged as malicious many of the most popular GO language applications such as Docker, a Bitcoin wallet and the actual Golang installer in an attempt to flag EBOWLA binaries – oops.
We’ve updated the project to include a new loader for PowerShell. This ubiquitous Windows scripting language is widely used in offensive testing and by defenders for incident response. Now the incident responder will need to be proficient in PowerShell debugging to begin the task of decrypting targeted malware that could also end up being more PowerShell! Post-Ekoparty, the team is working on a traditional loader using C++ compiled code, so stay tuned and visit our EBOWLA GitHub page for future updates.
Our hope is that defenders and reverse engineers can make use of the project updates to validate their preparedness and techniques against highly targeted malware. As discussed in our presentation, detection of malicious code in runtime interpreted languages is error prone and difficult. Shortly after our initial presentation at INFILTRATE, Kaspersky created an AV signature that flagged as malicious many of the most popular GO language applications such as Docker, a Bitcoin wallet and the actual Golang installer in an attempt to flag EBOWLA binaries – oops.
We’ve updated the project to include a new loader for PowerShell. This ubiquitous Windows scripting language is widely used in offensive testing and by defenders for incident response. Now the incident responder will need to be proficient in PowerShell debugging to begin the task of decrypting targeted malware that could also end up being more PowerShell! Post-Ekoparty, the team is working on a traditional loader using C++ compiled code, so stay tuned and visit our EBOWLA GitHub page for future updates.
How SADI & SHARE help restore the Scientific Method to in silico scienceMark Wilkinson
This document discusses the transition from BioMoby to SADI as a framework for semantic web services. It provides statistics on BioMoby usage and describes demonstrations of how SADI allows complex queries to be answered by discovering and executing relevant web services without a centralized database. The author's vision is for SADI to support the scientific method by enabling personal ontologies and hypotheses to be explicitly expressed and evaluated dynamically.
"The Data Janitor 101", Daniel Molnar, Senior Data Scientist at Microsoft Dataconomy Media
"The Data Janitor 101", Daniel Molnar, Senior Data Scientist at Microsoft
Watch videos from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
https://www.youtube.com/c/DataNatives
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
I'm a generalist in a tight-knit data team enabling data-driven company culture and operations as a data janitor, data analyst and occasional data scientist. Doing ETL and Data Quality, defining company-wide KPIs and metrics with management, producing BI, exploring user behavior to trigger actionable changes in marketing and product, A/B testing, feature engineering for ML, building webapp for secret sauce internal data tool. Long live Bayes and Occam! Tools are mostly bash, Python, Redshift, Tableau, SQL, Python, Flask, Mustache, Wizard, Optimizely.
Chasing Elephants - Alberto Brandolini - Codemotion Rome 2017Codemotion
This document discusses common problems that arise when companies experience accelerated growth in developing software. It describes four phases of growth: inception, ramp up, outsourcing, and aftermath. Key issues discussed include a loss of purpose and energy as companies prioritize features and hiring over working with real users and experts, resulting in purposeless code of declining quality over time. The document advocates for creating an environment where developers can learn, experiment, and focus on users' needs rather than production metrics.
Faster! Faster! Accelerate your business with blazing prototypesOSCON Byrum
Bring your ideas to life! Convince your boss to that open source development is faster and cheaper than the "safe" COTS solution they probably hate anyway. Let's investigate ways to get real-life, functional prototypes up with blazing speed. We'll look at and compare tools for truly rapid development including Python, Django, Flask, PHP, Amazon EC2 and Heroku.
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Presented at Global Scrum Gathering Orlando 2016
Because of benefits like predictability, better quality of products, and faster delivery, many companies have adopted or in the process of adopting Agile. However, there are challenges.
David Hawks, CST and Agile Evangelist, explains the common antipatterns of Agile adoption.
The document discusses the transition from BioMoby to SADI as a framework for semantic web services. It provides statistics on BioMoby usage and describes demonstrations of complex queries being answered through SADI and SHARE without a centralized database. The demonstrations include finding pathways for a protein and lab results for transplant patients. It advocates for SADI to support the scientific method and personal hypotheses through distributed ontologies rather than centralized ones.
This document discusses emerging technologies and their potential impacts. It covers topics like artificial intelligence, quantum computing, robotics, cyborgs, smart materials, fusion power, artificial life, malware, biobots, network bots, and more. The document notes that many of these technologies are still in early experimental stages and face challenges before being ready for widespread use. It also discusses debates around AI safety and the relationship between humans and increasingly intelligent machines.
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Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
10. snomed
428071000124103 Current Heavy tobacco smoker Current Heavy tobacco smoker Smoking Status
428061000124105 Current Light tobacco smoker Current Light tobacco smoker Smoking Status
428041000124106 Current some day smoker Current some day smoker Smoking Status
8517006 Ex-smoker (finding) Former smoker Smoking Status
266919005 Never smoked tobacco (finding) Never smoker Smoking Status
77176002 Smoker (finding)
Smoker, current status
unknown
Smoking Status
449868002 Smokes tobacco daily (finding) Current every day smoker Smoking Status
266927001
Tobacco smoking consumption
unknown (finding)
Unknown if ever smoked Smoking Status
11. snomed
428071000124103 Current Heavy tobacco smoker Current Heavy tobacco smoker Smoking Status
428061000124105 Current Light tobacco smoker Current Light tobacco smoker Smoking Status
428041000124106 Current some day smoker Current some day smoker Smoking Status
8517006 Ex-smoker (finding) Former smoker Smoking Status
266919005 Never smoked tobacco (finding) Never smoker Smoking Status
77176002 Smoker (finding)
Smoker, current status
unknown
Smoking Status
449868002 Smokes tobacco daily (finding) Current every day smoker Smoking Status
266927001
Tobacco smoking consumption
unknown (finding)
Unknown if ever smoked Smoking Status
70. The project
• There’s a Ruby script that…
1. Calls a REST API.
2. Creates a text file of the results.
• There’s a person that…
1. Calls the Ruby script, passing in a string.
2. Checks the text file into git.
3. Merges to master.
• There’s a person that...
1. Creates a release.
2. Deploys the release with Chef.
• There’s Java code that...
1. Finds the text file and zips it up.
2. Makes the zip file available for download via REST.
txt
ruby REST
chef
git
oozie
java
zipREST
80. The project
• There’s a Ruby script that…
1. Calls a REST API.
2. Creates a text file of the results.
• There’s a person that…
1. Calls the Ruby script, passing in a string.
2. Checks the text file into git.
3. Merges to master.
• There’s a person that...
1. Creates a release.
2. Deploys the release with Chef.
• There’s Java code that...
1. Finds the text file and zips it up.
2. Makes the zip file available for download via REST.
txt
ruby REST
chef
git
oozie
java
zipREST
81. The project
• There’s Java code that...
1. Calls a REST API.
2. Creates the text file and zips it up.
3. Makes the zip file available for download via REST.
REST
oozie
java
zipREST
82. How did we
get here?
ANSWER:
______________
______________
______________
______________
83. Stages of
Competence
Right Intuition
Right Analysis
Wrong Analysis
Wrong Intuition
unconscious
incompetence
conscious
incompetence
conscious
competence
unconscious
competence
85. Stages of
Competence
Right Intuition
Right Analysis
Wrong Analysis
Wrong Intuition
unconscious
incompetence
conscious
incompetence
conscious
competence
unconscious
competence
86. The project
• There’s a Ruby script that…
1. Calls a REST API.
2. Creates a text file of the results.
• There’s a person that…
1. Calls the Ruby script, passing in a string.
2. Checks the text file into git.
3. Merges to master.
• There’s a person that...
1. Creates a release.
2. Deploys the release with Chef.
• There’s Java code that...
1. Finds the text file and zips it up.
2. Makes the zip file available for download via REST.
txt
ruby REST
chef
git
oozie
java
zipREST
87. Stages of
Competence
Right Intuition
Right Analysis
Wrong Analysis
Wrong Intuition
unconscious
incompetence
conscious
incompetence
conscious
competence
unconscious
competence
90. Stages of
Competence
Right Intuition
Right Analysis
Wrong Analysis
Wrong Intuition
unconscious
incompetence
conscious
incompetence
conscious
competence
unconscious
competence
91. The project
• There’s Java code that...
1. Calls a REST API.
2. Creates the text file and zips it up.
3. Makes the zip file available for download via REST.
REST
oozie
java
zipREST
92. Stages of
Competence
Right Intuition
Right Analysis
Wrong Analysis
Wrong Intuition
unconscious
incompetence
conscious
incompetence
conscious
competence
unconscious
competence
93. Where you
need to be
to teach
peopleRight Intuition
Right Analysis
Wrong Analysis
Wrong Intuition
unconscious
incompetence
conscious
incompetence
conscious
competence
unconscious
competence
96. Automated Deployments
An automated deployment is defined as a deployment process that occurs seamlessly without manual
intervention. The triggering of this process is outside of the scope of the deployment itself.
Minimum Requirements
Below are the minimum requirements outlined to have a successful automated deployment process. An
automated deployment MUST have each of the attributes listed below.
Consistent Deployments
It is assumed that automated deployments MUST be consistently deployed across domains.
No Intermediary Actions
With an automated deployment, there MUST be no intermediary actions that take place between the start of
the execution and the end state (regardless of that being a successful deployment or not). If any manual
intervention is made during or as a result of the deployment, it is not an automated deployment.
Idempotence
Automated deployments MUST be idempotent. If the deployment were to run multiple times, the end
state MUST be the same.
No Downtimes
Automated deployments MUST support and maintain high availability.
Pre/Post Condition Checks
Automated deployments MUST have automatic precondition and postcondition checks.
• A deployment can be initiated if and only if the precondition checks pass.
• A deployment is considered successful if and only if the postcondition checks pass.
Automated Rollbacks
Automated deployments MUST be able to automatically rollback to previously stable state based on the
result of the post condition checks.
Critical State Alerting
Automated deployments MUST have the ability to alert if the system reaches an invalid state.
Example:
1. A deployment happens
2. The post condition check fails
3. An automated rollback happens
4. The post condition for that rollback also fails
5. An alert fires to immediately have system owners investigate the state of the system
97. Logging Transparency
Automated deployments MUST have transparent logging to be able to investigate, troubleshoot, and
facilitate additional learning about the deployment process in action.
Benchmarking
Automated deployments MUST support benchmarking to account for consistency, success and failure rates,
and other metric based information.
Recommended Practices
Below are the recommended practices outlined to have a successful automated deployment process. An
automated deployment SHOULD have each of the attributes listed below.
Checkpointing
Automated deployments SHOULD support checkpointing. Checkpointing is defined as supporting the
deployment of viable partial states (if part of the deployment fails, only the part of it that failed SHOULD be
automatically rolled back). This assumes that the successful parts of the deployment are isolated and not
dependent on the failed portions.
Blackout Periods
Automated deployments SHOULD support periods of calendar time where the automated triggering of
deployments is suspended. Examples of such times would be incidents, critical events, off hours, etc.
Synthetic Traffic/Processing
Automated deployments SHOULD use synthetic traffic/processing for validation purposes. Synthetic
traffic/processing ensures greater reliability of the successfulness of a deployment.
Real-time Visibility
Automated deployments SHOULD have real-time visibility with which to see a visual representation of
deployments as they are occurring. This visibility ensures more accurate comprehension of the high level
view of the overall system.
2 Pages of documented Expectations
98. Logging Transparency
Automated deployments MUST have transparent logging to be able to investigate, troubleshoot, and
facilitate additional learning about the deployment process in action.
Benchmarking
Automated deployments MUST support benchmarking to account for consistency, success and failure rates,
and other metric based information.
Recommended Practices
Below are the recommended practices outlined to have a successful automated deployment process. An
automated deployment SHOULD have each of the attributes listed below.
Checkpointing
Automated deployments SHOULD support checkpointing. Checkpointing is defined as supporting the
deployment of viable partial states (if part of the deployment fails, only the part of it that failed SHOULD be
automatically rolled back). This assumes that the successful parts of the deployment are isolated and not
dependent on the failed portions.
Blackout Periods
Automated deployments SHOULD support periods of calendar time where the automated triggering of
deployments is suspended. Examples of such times would be incidents, critical events, off hours, etc.
Synthetic Traffic/Processing
Automated deployments SHOULD use synthetic traffic/processing for validation purposes. Synthetic
traffic/processing ensures greater reliability of the successfulness of a deployment.
Real-time Visibility
Automated deployments SHOULD have real-time visibility with which to see a visual representation of
deployments as they are occurring. This visibility ensures more accurate comprehension of the high level
view of the overall system.
2 Pages of software requirements
99. • documented expectations
• repeatable tests
• source control
• PEER Review
• User feedback
• monitoring
Automation is software
development.