Going beyond “Sorry, I didn’t get that”: building AI assistants that scale us...Justina Petraitytė
This document summarizes a workshop on building machine learning-powered conversational assistants. It covers natural language understanding using intent classification and entity extraction, dialogue management using reinforcement learning, and closing the feedback loop to retrain models with new data. The workshop teaches setting up Rasa, an open-source toolkit, and coding examples for natural language understanding and dialogue handling.
Deprecating the state machine: building conversational AI with the Rasa stackJustina Petraitytė
Rasa NLU & Rasa Core are the leading open source libraries for building machine learning-based chatbots and voice assistants. In this live-coding workshop, you will learn the fundamentals of conversational AI and how to build your own using the Rasa Stack.
Deprecating the state machine: building conversational AI with the Rasa stack...PyData
Rasa NLU & Rasa Core are the leading open source libraries for building machine learning-based chatbots and voice assistants. In this live-coding workshop you will learn the fundamentals of conversational AI and how to build your own using the Rasa Stack.
London atlassian meetup 31 jan 2016 jira metrics-extract slidesRudiger Wolf
Slides for talk given to London Atlassian User Group Jan 2017. How to get started with Python to extract data from Jira and produce charts for your Agile team.
Software Analytics with Jupyter, Pandas, jQAssistant, and Neo4j [Neo4j Online...Markus Harrer
Let’s tackle problems in software development in an automated, data-driven and reproducible way!
As developers, we often feel that there might be something wrong with the way we develop software. Unfortunately, a gut feeling alone isn’t sufficient for the complex, interconnected problems in software systems.
We need solid, understandable arguments to gain budgets for improvement projects or to defend us against political decisions. Though, we can help ourselves: Every step in the development or use of software leaves valuable, digital traces. With clever analysis, these data can show us root causes of problems in our software and deliver new insights – understandable for everybody.
If concrete problems and their impact are known, developers and managers can create solutions and take sustainable actions aligned to existing business goals.
In this meetup, I talk about the analysis of software data by using a digital notebook approach. This allows you to express your gut feelings explicitly with the help of hypotheses, explorations and visualizations step by step.
I show the collaboration of open source analysis tools (Jupyter, Pandas, jQAssistant and, of course, Neo4j) to inspect problems in Java applications and their environment. We have a look at performance hotspots, knowledge loss and worthless code parts – completely automated from raw data up to visualizations for management.
Participants learn how they can translate their unsafe gut feelings into solid evidence for obtaining budgets for dedicated improvement projects with the help of data analysis.
Advanced Virtual Assistant Based on Speech Processing Oriented Technology on ...ijtsrd
With the advancement of technology, the need for a virtual assistant is increasing tremendously. The development of virtual assistants is booming on all platforms. Cortana, Siri are some of the best examples for virtual assistants. We focus on improving the efficiency of virtual assistant by reducing the response time for a particular action. The primary development criterion of any virtual assistant is by developing a simple U.I. for assistant in all platforms and core functioning in the backend so that it could perform well in multi plat formed or cross plat formed manner by applying the backend code for all the platforms. We try a different research approach in this paper. That is, we give computation and processing power to edge devices itself. So that it could perform well by doing actions in a short period, think about the normal working of a typical virtual assistant. That is taking command from the user, transfer that command to the backend server, analyze it on the server, transfer back the action or result to the end user and finally get a response if we could do all this thing in a single machine itself, the response time will get reduced to a considerable amount. In this paper, we will develop a new algorithm by keeping a local database for speech recognition and creating various helpful functions to do particular action on the end device. Akhilesh L "Advanced Virtual Assistant Based on Speech Processing Oriented Technology on Edge Concept (S.P.O.T)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33289.pdf Paper Url: https://www.ijtsrd.com/computer-science/realtime-computing/33289/advanced-virtual-assistant-based-on-speech-processing-oriented-technology-on-edge-concept-spot/akhilesh-l
Going beyond “Sorry, I didn’t get that”: building AI assistants that scale us...Justina Petraitytė
This document summarizes a workshop on building machine learning-powered conversational assistants. It covers natural language understanding using intent classification and entity extraction, dialogue management using reinforcement learning, and closing the feedback loop to retrain models with new data. The workshop teaches setting up Rasa, an open-source toolkit, and coding examples for natural language understanding and dialogue handling.
Deprecating the state machine: building conversational AI with the Rasa stackJustina Petraitytė
Rasa NLU & Rasa Core are the leading open source libraries for building machine learning-based chatbots and voice assistants. In this live-coding workshop, you will learn the fundamentals of conversational AI and how to build your own using the Rasa Stack.
Deprecating the state machine: building conversational AI with the Rasa stack...PyData
Rasa NLU & Rasa Core are the leading open source libraries for building machine learning-based chatbots and voice assistants. In this live-coding workshop you will learn the fundamentals of conversational AI and how to build your own using the Rasa Stack.
London atlassian meetup 31 jan 2016 jira metrics-extract slidesRudiger Wolf
Slides for talk given to London Atlassian User Group Jan 2017. How to get started with Python to extract data from Jira and produce charts for your Agile team.
Software Analytics with Jupyter, Pandas, jQAssistant, and Neo4j [Neo4j Online...Markus Harrer
Let’s tackle problems in software development in an automated, data-driven and reproducible way!
As developers, we often feel that there might be something wrong with the way we develop software. Unfortunately, a gut feeling alone isn’t sufficient for the complex, interconnected problems in software systems.
We need solid, understandable arguments to gain budgets for improvement projects or to defend us against political decisions. Though, we can help ourselves: Every step in the development or use of software leaves valuable, digital traces. With clever analysis, these data can show us root causes of problems in our software and deliver new insights – understandable for everybody.
If concrete problems and their impact are known, developers and managers can create solutions and take sustainable actions aligned to existing business goals.
In this meetup, I talk about the analysis of software data by using a digital notebook approach. This allows you to express your gut feelings explicitly with the help of hypotheses, explorations and visualizations step by step.
I show the collaboration of open source analysis tools (Jupyter, Pandas, jQAssistant and, of course, Neo4j) to inspect problems in Java applications and their environment. We have a look at performance hotspots, knowledge loss and worthless code parts – completely automated from raw data up to visualizations for management.
Participants learn how they can translate their unsafe gut feelings into solid evidence for obtaining budgets for dedicated improvement projects with the help of data analysis.
Advanced Virtual Assistant Based on Speech Processing Oriented Technology on ...ijtsrd
With the advancement of technology, the need for a virtual assistant is increasing tremendously. The development of virtual assistants is booming on all platforms. Cortana, Siri are some of the best examples for virtual assistants. We focus on improving the efficiency of virtual assistant by reducing the response time for a particular action. The primary development criterion of any virtual assistant is by developing a simple U.I. for assistant in all platforms and core functioning in the backend so that it could perform well in multi plat formed or cross plat formed manner by applying the backend code for all the platforms. We try a different research approach in this paper. That is, we give computation and processing power to edge devices itself. So that it could perform well by doing actions in a short period, think about the normal working of a typical virtual assistant. That is taking command from the user, transfer that command to the backend server, analyze it on the server, transfer back the action or result to the end user and finally get a response if we could do all this thing in a single machine itself, the response time will get reduced to a considerable amount. In this paper, we will develop a new algorithm by keeping a local database for speech recognition and creating various helpful functions to do particular action on the end device. Akhilesh L "Advanced Virtual Assistant Based on Speech Processing Oriented Technology on Edge Concept (S.P.O.T)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33289.pdf Paper Url: https://www.ijtsrd.com/computer-science/realtime-computing/33289/advanced-virtual-assistant-based-on-speech-processing-oriented-technology-on-edge-concept-spot/akhilesh-l
Shruthi Ramesh Nayak is a graduate student seeking a job as a Java developer. She has a Master's in Computer Science from UT Dallas and a Bachelor's in Computer Engineering from PESIT, Bangalore. Her experience includes internships at Sabre Corporation and Cleartrip as a Java developer and mobile software engineer, respectively. She has strong skills in Java, Python, Android and data structures and algorithms. She has published research and completed academic projects involving search engines, social networks and visual cryptography. Her hobbies include developing Android apps for contact sharing, events and remote mouse control.
Lambda architecture for real time big dataTrieu Nguyen
- The document discusses the Lambda Architecture, a system designed by Nathan Marz for building real-time big data applications. It is based on three principles: human fault-tolerance, data immutability, and recomputation.
- The document provides two case studies of applying Lambda Architecture - at Greengar Studios for API monitoring and statistics, and at eClick for real-time data analytics on streaming user event data.
- Key lessons discussed are keeping solutions simple, asking the right questions to enable deep analytics and profit, using reactive and functional approaches, and turning data into useful insights.
business model, business model canvas, mission model, mission model canvas, customer development, lean launchpad, lean startup, stanford, startup, steve blank, entrepreneurship, I-Corps, Stanford
The document discusses conversational AI and how deep machine learning techniques can help advance natural language understanding and generation for chatbots. It describes an open-source framework for building conversational agents and its architecture, including natural language understanding, dialogue handling, and natural language generation components. The framework uses recurrent neural networks and memory networks for these tasks. It also highlights some open challenges in the field and recent papers on conversational AI research.
Qamar Ali is a software developer currently working at Livastar.com in Hyderabad, India. He has over 3 years of experience in data science and software development. Some of his responsibilities have included automating report generation using Python scripts and Twitter data collection and analysis using Tweepy. He received his B.Tech in Computer Science from IIIT-Hyderabad in 2014 with a CGPA of 7.23. His skills include Python, Java, C, MySQL, Django and other web technologies.
This document is a mini project report submitted by Saloni Jaiswal for their MBA program. It discusses the development of a chatbot for the Yioop web search portal. The report includes an introduction to chatbots, different types of chatbots, and the objectives and methodology for the project. It is divided into several sections covering the company profile, literature review, research methodology, data analysis, conclusions, and limitations. The project aims to add chatbot functionality and an API to Yioop to allow developers to create chatbots that can interact with users on group discussion pages.
What's AGI? How is it different from an Agent or an AI Assistant? If you're looking to understand how AI Agents/AGI can help your company, check this out.
GSoC 2017 Proposal - Chatbot for DBpedia Ram G Athreya
The document is a project application for building a conversational chatbot for DBpedia. It proposes developing a chatbot that can understand natural language queries, fetch relevant information from DBpedia, and tailor responses based on different platforms. It outlines a tentative architecture with 6 steps: 1) receiving requests, 2) classifying requests, 3) handling requests, 4) obtaining answers from question answering services or DBpedia, 5) generating responses, and 6) sending responses customized for each platform. Key aspects include using existing services for question answering and request classification, storing user information in databases, and presenting additional contextual information from DBpedia based on entity types.
LangChain Intro, Keymate.AI Search Plugin for ChatGPT, How to use langchain library? How to implement similar functionality in programming language of your choice? Example LangChain applications.
The presentation revolves around the concept of "langChain", This innovative framework is designed to "chain" together different components to create more advanced use cases around Large Language Models (LLMs). The idea is to leverage the power of LLMs to tackle complex problems and generate solutions that are more than the sum of their parts.
One of the key features of the presentation is the application of the "Keymate.AI Search" plugin in conjunction with the Reasoning and Acting Chain of Thought (ReAct) framework. The presenter encourages the audience to utilize these tools to generate reasoning traces and actions. The ReAct framework, learned from an initial search, is then applied to these traces and actions, demonstrating the potential of LLMs to learn and apply complex frameworks.
The presentation also delves into the impact of climate change on biodiversity. The presenter prompts the audience to look up the latest research on this topic and summarize the key findings. This exercise not only highlights the importance of climate change but also demonstrates the capabilities of LLMs in researching and summarizing complex topics.
The presentation concludes with several key takeaways. The presenter emphasizes that specialized custom solutions work best and suggests a bottom-up approach to expert systems. However, they caution that over-abstraction can lead to leakages, causing time and money limits to hit early and tasks to fail or require many iterations. The presenter also notes that while prompt engineering is important, it's not necessary to over-optimize if the LLM is clever. The presentation ends on a hopeful note, expressing a need for more clever LLMs and acknowledging that good applications are rare but achievable.
Overall, the presentation provides a comprehensive overview of the LanGCHAIN framework, its applications, and the potential of LLMs in solving complex problems. It serves as a call to action for the audience to explore these tools and frameworks.
Langchain Framework is an innovative approach to linguistic data processing, combining the principles of language sciences, blockchain technology, and artificial intelligence. This deck introduces the groundbreaking elements of the framework, detailing how it enhances security, transparency, and decentralization in language data management. It discusses its applications in various fields, including machine learning, translation services, content creation, and more. The deck also highlights its key features, such as immutability, peer-to-peer networks, and linguistic asset ownership, that could revolutionize how we handle linguistic data in the digital age.
Stitch Fix aspires to help you find the style that you will love. Data, the backbone of the business, is used to help with styling recommendations, demand modeling, user acquisition, and merchandise planning and also to influence business decisions throughout the organization. These decisions are backed by algorithms and data collected and interpreted based on client preferences. Neelesh Srinivas Salian offers an overview of the compute infrastructure used by the data science team at Stitch Fix, covering the architecture, tools within the larger ecosystem, and the challenges that the team overcame along the way.
Apache Spark plays an important role in Stitch Fix’s data platform, and the company’s data scientists use Spark for their ETL and Presto for their ad hoc queries. The goal for the team running the compute infrastructure is to understand and make the data scientists’ lives easier, particularly in terms of usability of Spark, by building tools that expedite the process of getting started with Spark and transitioning from an ad hoc to a production workflow. The compute infrastructure is a part of the data platform that is responsible for all the needs of data scientists as Stitch Fix.
Neelesh shares Stitch Fix’s journey, exploring its ad hoc and production infrastructure and detailing its in-house tools and how they work in synergy with open source frameworks in a cloud environment. Neelesh also discusses the additional improvements to the infrastructure that help persist information for future use and optimization and explains how the implementation of Amazon’s EMR FS has helped make it easier to read from the S3 source.
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Hadoop Cluster- Single and multi node, Hadoop 2.0, Flume, Sqoop, Map-Reduce, PIG, Hive, Hbase, Zookeeper, Oozie etc. will be covered in the course.
Atlassian User Group NYC April 27 2017 PresentationsMarlon Palha
This document provides an overview of an event being held by Adaptavist in various cities in North America to demonstrate their ScriptRunner product. The agenda includes introductory presentations on ScriptRunner and how it can be used for support delivery and within Confluence, JIRA, and Bitbucket. There will also be a session demonstrating how to extend the user interface in JIRA. Participants can sign up to receive the presentation slides. The evening agenda includes a further overview of ScriptRunner and its new editor, followed by a question and answer session.
Nicholas Schiller presented on using APIs to customize library services. He demonstrated how to build a web application using the WorldCat Search API that automatically adds Boolean search terms to a user's query and formats the results. The application was built with PHP for server-side scripting, HTML5 for interface design, and jQuery Mobile to optimize for different devices. The presentation provided examples of APIs, guidelines for API projects, and resources for further learning about APIs and programming.
Deep learning networks can be successfully applied to big data for knowledge discovery, knowledge application, and knowledge-based prediction. In other words, deep learning can be a powerful engine for producing actionable results.
Six Months Industrial Training In MohaliAmmy Patrix
Abhyaas is Mohali based Industrial Training Institute for MCA, B.Tech Candidates. Abhyaas is sponsored by ART World Web Solutions, Gloably based Web Development Company
The document provides information about Abhyaas, an industrial training program for IT professionals. It discusses:
- Abhyaas provides hands-on training through both classroom and live project work under the guidance of project managers to increase employability.
- Trainees learn the latest technologies while working on real-life projects rather than outdated techniques used by other institutes.
- In addition to technical training, Abhyaas also offers personality development, interview preparation assistance, and placement support to help students find jobs.
Achieving Technical Excellence in Your Software Teams - from Devternity Peter Gfader
Our industry has a problem: We are not lacking software methodologies, programming languages, tools or frameworks but we need great software engineers.
Great software engineer teams build quality-in and deliver great software on a regular basis. The technical excellence of those engineers will help you escape the "Waterfall sandwich" and make your organization a little more agile, from the inception of an idea till they go live.
I will talk about my experiences from the last 15 years, including small software delivery teams until big financial institutions.
Why would a company like to be "agile"?
How can a company achieve that?
How can you achieve Technical Excellence in your software teams?
What developer skills are more important than languages, methods or frameworks?
This will be an interactive session with a Q&A at the end.
Odoo releases a new update every year. The latest version, Odoo 17, came out in October 2023. It brought many improvements to the user interface and user experience, along with new features in modules like accounting, marketing, manufacturing, websites, and more.
The Odoo 17 update has been a hot topic among startups, mid-sized businesses, large enterprises, and Odoo developers aiming to grow their businesses. Since it is now already the first quarter of 2024, you must have a clear idea of what Odoo 17 entails and what it can offer your business if you are still not aware of it.
This blog covers the features and functionalities. Explore the entire blog and get in touch with expert Odoo ERP consultants to leverage Odoo 17 and its features for your business too.
An Overview of Odoo ERP
Odoo ERP was first released as OpenERP software in February 2005. It is a suite of business applications used for ERP, CRM, eCommerce, websites, and project management. Ten years ago, the Odoo Enterprise edition was launched to help fund the Odoo Community version.
When you compare Odoo Community and Enterprise, the Enterprise edition offers exclusive features like mobile app access, Odoo Studio customisation, Odoo hosting, and unlimited functional support.
Today, Odoo is a well-known name used by companies of all sizes across various industries, including manufacturing, retail, accounting, marketing, healthcare, IT consulting, and R&D.
The latest version, Odoo 17, has been available since October 2023. Key highlights of this update include:
Enhanced user experience with improvements to the command bar, faster backend page loading, and multiple dashboard views.
Instant report generation, credit limit alerts for sales and invoices, separate OCR settings for invoice creation, and an auto-complete feature for forms in the accounting module.
Improved image handling and global attribute changes for mailing lists in email marketing.
A default auto-signature option and a refuse-to-sign option in HR modules.
Options to divide and merge manufacturing orders, track the status of manufacturing orders, and more in the MRP module.
Dark mode in Odoo 17.
Now that the Odoo 17 announcement is official, let’s look at what’s new in Odoo 17!
What is Odoo ERP 17?
Odoo 17 is the latest version of one of the world’s leading open-source enterprise ERPs. This version has come up with significant improvements explained here in this blog. Also, this new version aims to introduce features that enhance time-saving, efficiency, and productivity for users across various organisations.
Odoo 17, released at the Odoo Experience 2023, brought notable improvements to the user interface and added new functionalities with enhancements in performance, accessibility, data analysis, and management, further expanding its reach in the market.
Top Benefits of Using Salesforce Healthcare CRM for Patient Management.pdfVALiNTRY360
Salesforce Healthcare CRM, implemented by VALiNTRY360, revolutionizes patient management by enhancing patient engagement, streamlining administrative processes, and improving care coordination. Its advanced analytics, robust security, and seamless integration with telehealth services ensure that healthcare providers can deliver personalized, efficient, and secure patient care. By automating routine tasks and providing actionable insights, Salesforce Healthcare CRM enables healthcare providers to focus on delivering high-quality care, leading to better patient outcomes and higher satisfaction. VALiNTRY360's expertise ensures a tailored solution that meets the unique needs of any healthcare practice, from small clinics to large hospital systems.
For more info visit us https://valintry360.com/solutions/health-life-sciences
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Shruthi Ramesh Nayak is a graduate student seeking a job as a Java developer. She has a Master's in Computer Science from UT Dallas and a Bachelor's in Computer Engineering from PESIT, Bangalore. Her experience includes internships at Sabre Corporation and Cleartrip as a Java developer and mobile software engineer, respectively. She has strong skills in Java, Python, Android and data structures and algorithms. She has published research and completed academic projects involving search engines, social networks and visual cryptography. Her hobbies include developing Android apps for contact sharing, events and remote mouse control.
Lambda architecture for real time big dataTrieu Nguyen
- The document discusses the Lambda Architecture, a system designed by Nathan Marz for building real-time big data applications. It is based on three principles: human fault-tolerance, data immutability, and recomputation.
- The document provides two case studies of applying Lambda Architecture - at Greengar Studios for API monitoring and statistics, and at eClick for real-time data analytics on streaming user event data.
- Key lessons discussed are keeping solutions simple, asking the right questions to enable deep analytics and profit, using reactive and functional approaches, and turning data into useful insights.
business model, business model canvas, mission model, mission model canvas, customer development, lean launchpad, lean startup, stanford, startup, steve blank, entrepreneurship, I-Corps, Stanford
The document discusses conversational AI and how deep machine learning techniques can help advance natural language understanding and generation for chatbots. It describes an open-source framework for building conversational agents and its architecture, including natural language understanding, dialogue handling, and natural language generation components. The framework uses recurrent neural networks and memory networks for these tasks. It also highlights some open challenges in the field and recent papers on conversational AI research.
Qamar Ali is a software developer currently working at Livastar.com in Hyderabad, India. He has over 3 years of experience in data science and software development. Some of his responsibilities have included automating report generation using Python scripts and Twitter data collection and analysis using Tweepy. He received his B.Tech in Computer Science from IIIT-Hyderabad in 2014 with a CGPA of 7.23. His skills include Python, Java, C, MySQL, Django and other web technologies.
This document is a mini project report submitted by Saloni Jaiswal for their MBA program. It discusses the development of a chatbot for the Yioop web search portal. The report includes an introduction to chatbots, different types of chatbots, and the objectives and methodology for the project. It is divided into several sections covering the company profile, literature review, research methodology, data analysis, conclusions, and limitations. The project aims to add chatbot functionality and an API to Yioop to allow developers to create chatbots that can interact with users on group discussion pages.
What's AGI? How is it different from an Agent or an AI Assistant? If you're looking to understand how AI Agents/AGI can help your company, check this out.
GSoC 2017 Proposal - Chatbot for DBpedia Ram G Athreya
The document is a project application for building a conversational chatbot for DBpedia. It proposes developing a chatbot that can understand natural language queries, fetch relevant information from DBpedia, and tailor responses based on different platforms. It outlines a tentative architecture with 6 steps: 1) receiving requests, 2) classifying requests, 3) handling requests, 4) obtaining answers from question answering services or DBpedia, 5) generating responses, and 6) sending responses customized for each platform. Key aspects include using existing services for question answering and request classification, storing user information in databases, and presenting additional contextual information from DBpedia based on entity types.
LangChain Intro, Keymate.AI Search Plugin for ChatGPT, How to use langchain library? How to implement similar functionality in programming language of your choice? Example LangChain applications.
The presentation revolves around the concept of "langChain", This innovative framework is designed to "chain" together different components to create more advanced use cases around Large Language Models (LLMs). The idea is to leverage the power of LLMs to tackle complex problems and generate solutions that are more than the sum of their parts.
One of the key features of the presentation is the application of the "Keymate.AI Search" plugin in conjunction with the Reasoning and Acting Chain of Thought (ReAct) framework. The presenter encourages the audience to utilize these tools to generate reasoning traces and actions. The ReAct framework, learned from an initial search, is then applied to these traces and actions, demonstrating the potential of LLMs to learn and apply complex frameworks.
The presentation also delves into the impact of climate change on biodiversity. The presenter prompts the audience to look up the latest research on this topic and summarize the key findings. This exercise not only highlights the importance of climate change but also demonstrates the capabilities of LLMs in researching and summarizing complex topics.
The presentation concludes with several key takeaways. The presenter emphasizes that specialized custom solutions work best and suggests a bottom-up approach to expert systems. However, they caution that over-abstraction can lead to leakages, causing time and money limits to hit early and tasks to fail or require many iterations. The presenter also notes that while prompt engineering is important, it's not necessary to over-optimize if the LLM is clever. The presentation ends on a hopeful note, expressing a need for more clever LLMs and acknowledging that good applications are rare but achievable.
Overall, the presentation provides a comprehensive overview of the LanGCHAIN framework, its applications, and the potential of LLMs in solving complex problems. It serves as a call to action for the audience to explore these tools and frameworks.
Langchain Framework is an innovative approach to linguistic data processing, combining the principles of language sciences, blockchain technology, and artificial intelligence. This deck introduces the groundbreaking elements of the framework, detailing how it enhances security, transparency, and decentralization in language data management. It discusses its applications in various fields, including machine learning, translation services, content creation, and more. The deck also highlights its key features, such as immutability, peer-to-peer networks, and linguistic asset ownership, that could revolutionize how we handle linguistic data in the digital age.
Stitch Fix aspires to help you find the style that you will love. Data, the backbone of the business, is used to help with styling recommendations, demand modeling, user acquisition, and merchandise planning and also to influence business decisions throughout the organization. These decisions are backed by algorithms and data collected and interpreted based on client preferences. Neelesh Srinivas Salian offers an overview of the compute infrastructure used by the data science team at Stitch Fix, covering the architecture, tools within the larger ecosystem, and the challenges that the team overcame along the way.
Apache Spark plays an important role in Stitch Fix’s data platform, and the company’s data scientists use Spark for their ETL and Presto for their ad hoc queries. The goal for the team running the compute infrastructure is to understand and make the data scientists’ lives easier, particularly in terms of usability of Spark, by building tools that expedite the process of getting started with Spark and transitioning from an ad hoc to a production workflow. The compute infrastructure is a part of the data platform that is responsible for all the needs of data scientists as Stitch Fix.
Neelesh shares Stitch Fix’s journey, exploring its ad hoc and production infrastructure and detailing its in-house tools and how they work in synergy with open source frameworks in a cloud environment. Neelesh also discusses the additional improvements to the infrastructure that help persist information for future use and optimization and explains how the implementation of Amazon’s EMR FS has helped make it easier to read from the S3 source.
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Hadoop Cluster- Single and multi node, Hadoop 2.0, Flume, Sqoop, Map-Reduce, PIG, Hive, Hbase, Zookeeper, Oozie etc. will be covered in the course.
Atlassian User Group NYC April 27 2017 PresentationsMarlon Palha
This document provides an overview of an event being held by Adaptavist in various cities in North America to demonstrate their ScriptRunner product. The agenda includes introductory presentations on ScriptRunner and how it can be used for support delivery and within Confluence, JIRA, and Bitbucket. There will also be a session demonstrating how to extend the user interface in JIRA. Participants can sign up to receive the presentation slides. The evening agenda includes a further overview of ScriptRunner and its new editor, followed by a question and answer session.
Nicholas Schiller presented on using APIs to customize library services. He demonstrated how to build a web application using the WorldCat Search API that automatically adds Boolean search terms to a user's query and formats the results. The application was built with PHP for server-side scripting, HTML5 for interface design, and jQuery Mobile to optimize for different devices. The presentation provided examples of APIs, guidelines for API projects, and resources for further learning about APIs and programming.
Deep learning networks can be successfully applied to big data for knowledge discovery, knowledge application, and knowledge-based prediction. In other words, deep learning can be a powerful engine for producing actionable results.
Six Months Industrial Training In MohaliAmmy Patrix
Abhyaas is Mohali based Industrial Training Institute for MCA, B.Tech Candidates. Abhyaas is sponsored by ART World Web Solutions, Gloably based Web Development Company
The document provides information about Abhyaas, an industrial training program for IT professionals. It discusses:
- Abhyaas provides hands-on training through both classroom and live project work under the guidance of project managers to increase employability.
- Trainees learn the latest technologies while working on real-life projects rather than outdated techniques used by other institutes.
- In addition to technical training, Abhyaas also offers personality development, interview preparation assistance, and placement support to help students find jobs.
Achieving Technical Excellence in Your Software Teams - from Devternity Peter Gfader
Our industry has a problem: We are not lacking software methodologies, programming languages, tools or frameworks but we need great software engineers.
Great software engineer teams build quality-in and deliver great software on a regular basis. The technical excellence of those engineers will help you escape the "Waterfall sandwich" and make your organization a little more agile, from the inception of an idea till they go live.
I will talk about my experiences from the last 15 years, including small software delivery teams until big financial institutions.
Why would a company like to be "agile"?
How can a company achieve that?
How can you achieve Technical Excellence in your software teams?
What developer skills are more important than languages, methods or frameworks?
This will be an interactive session with a Q&A at the end.
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Odoo releases a new update every year. The latest version, Odoo 17, came out in October 2023. It brought many improvements to the user interface and user experience, along with new features in modules like accounting, marketing, manufacturing, websites, and more.
The Odoo 17 update has been a hot topic among startups, mid-sized businesses, large enterprises, and Odoo developers aiming to grow their businesses. Since it is now already the first quarter of 2024, you must have a clear idea of what Odoo 17 entails and what it can offer your business if you are still not aware of it.
This blog covers the features and functionalities. Explore the entire blog and get in touch with expert Odoo ERP consultants to leverage Odoo 17 and its features for your business too.
An Overview of Odoo ERP
Odoo ERP was first released as OpenERP software in February 2005. It is a suite of business applications used for ERP, CRM, eCommerce, websites, and project management. Ten years ago, the Odoo Enterprise edition was launched to help fund the Odoo Community version.
When you compare Odoo Community and Enterprise, the Enterprise edition offers exclusive features like mobile app access, Odoo Studio customisation, Odoo hosting, and unlimited functional support.
Today, Odoo is a well-known name used by companies of all sizes across various industries, including manufacturing, retail, accounting, marketing, healthcare, IT consulting, and R&D.
The latest version, Odoo 17, has been available since October 2023. Key highlights of this update include:
Enhanced user experience with improvements to the command bar, faster backend page loading, and multiple dashboard views.
Instant report generation, credit limit alerts for sales and invoices, separate OCR settings for invoice creation, and an auto-complete feature for forms in the accounting module.
Improved image handling and global attribute changes for mailing lists in email marketing.
A default auto-signature option and a refuse-to-sign option in HR modules.
Options to divide and merge manufacturing orders, track the status of manufacturing orders, and more in the MRP module.
Dark mode in Odoo 17.
Now that the Odoo 17 announcement is official, let’s look at what’s new in Odoo 17!
What is Odoo ERP 17?
Odoo 17 is the latest version of one of the world’s leading open-source enterprise ERPs. This version has come up with significant improvements explained here in this blog. Also, this new version aims to introduce features that enhance time-saving, efficiency, and productivity for users across various organisations.
Odoo 17, released at the Odoo Experience 2023, brought notable improvements to the user interface and added new functionalities with enhancements in performance, accessibility, data analysis, and management, further expanding its reach in the market.
Top Benefits of Using Salesforce Healthcare CRM for Patient Management.pdfVALiNTRY360
Salesforce Healthcare CRM, implemented by VALiNTRY360, revolutionizes patient management by enhancing patient engagement, streamlining administrative processes, and improving care coordination. Its advanced analytics, robust security, and seamless integration with telehealth services ensure that healthcare providers can deliver personalized, efficient, and secure patient care. By automating routine tasks and providing actionable insights, Salesforce Healthcare CRM enables healthcare providers to focus on delivering high-quality care, leading to better patient outcomes and higher satisfaction. VALiNTRY360's expertise ensures a tailored solution that meets the unique needs of any healthcare practice, from small clinics to large hospital systems.
For more info visit us https://valintry360.com/solutions/health-life-sciences
UI5con 2024 - Keynote: Latest News about UI5 and it’s EcosystemPeter Muessig
Learn about the latest innovations in and around OpenUI5/SAPUI5: UI5 Tooling, UI5 linter, UI5 Web Components, Web Components Integration, UI5 2.x, UI5 GenAI.
Recording:
https://www.youtube.com/live/MSdGLG2zLy8?si=INxBHTqkwHhxV5Ta&t=0
Unveiling the Advantages of Agile Software Development.pdfbrainerhub1
Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.
14 th Edition of International conference on computer visionShulagnaSarkar2
About the event
14th Edition of International conference on computer vision
Computer conferences organized by ScienceFather group. ScienceFather takes the privilege to invite speakers participants students delegates and exhibitors from across the globe to its International Conference on computer conferences to be held in the Various Beautiful cites of the world. computer conferences are a discussion of common Inventions-related issues and additionally trade information share proof thoughts and insight into advanced developments in the science inventions service system. New technology may create many materials and devices with a vast range of applications such as in Science medicine electronics biomaterials energy production and consumer products.
Nomination are Open!! Don't Miss it
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Award Nomination: https://x-i.me/ishnom
Conference Submission: https://x-i.me/anicon
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Using Query Store in Azure PostgreSQL to Understand Query PerformanceGrant Fritchey
Microsoft has added an excellent new extension in PostgreSQL on their Azure Platform. This session, presented at Posette 2024, covers what Query Store is and the types of information you can get out of it.
What to do when you have a perfect model for your software but you are constrained by an imperfect business model?
This talk explores the challenges of bringing modelling rigour to the business and strategy levels, and talking to your non-technical counterparts in the process.
Preparing Non - Technical Founders for Engaging a Tech AgencyISH Technologies
Preparing non-technical founders before engaging a tech agency is crucial for the success of their projects. It starts with clearly defining their vision and goals, conducting thorough market research, and gaining a basic understanding of relevant technologies. Setting realistic expectations and preparing a detailed project brief are essential steps. Founders should select a tech agency with a proven track record and establish clear communication channels. Additionally, addressing legal and contractual considerations and planning for post-launch support are vital to ensure a smooth and successful collaboration. This preparation empowers non-technical founders to effectively communicate their needs and work seamlessly with their chosen tech agency.Visit our site to get more details about this. Contact us today www.ishtechnologies.com.au
8 Best Automated Android App Testing Tool and Framework in 2024.pdfkalichargn70th171
Regarding mobile operating systems, two major players dominate our thoughts: Android and iPhone. With Android leading the market, software development companies are focused on delivering apps compatible with this OS. Ensuring an app's functionality across various Android devices, OS versions, and hardware specifications is critical, making Android app testing essential.
Microservice Teams - How the cloud changes the way we workSven Peters
A lot of technical challenges and complexity come with building a cloud-native and distributed architecture. The way we develop backend software has fundamentally changed in the last ten years. Managing a microservices architecture demands a lot of us to ensure observability and operational resiliency. But did you also change the way you run your development teams?
Sven will talk about Atlassian’s journey from a monolith to a multi-tenanted architecture and how it affected the way the engineering teams work. You will learn how we shifted to service ownership, moved to more autonomous teams (and its challenges), and established platform and enablement teams.
Enhanced Screen Flows UI/UX using SLDS with Tom KittPeter Caitens
Join us for an engaging session led by Flow Champion, Tom Kitt. This session will dive into a technique of enhancing the user interfaces and user experiences within Screen Flows using the Salesforce Lightning Design System (SLDS). This technique uses Native functionality, with No Apex Code, No Custom Components and No Managed Packages required.
Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
Mobile App Development Company In Noida | Drona InfotechDrona Infotech
Drona Infotech is a premier mobile app development company in Noida, providing cutting-edge solutions for businesses.
Visit Us For : https://www.dronainfotech.com/mobile-application-development/
Mobile App Development Company In Noida | Drona Infotech
Going beyond “Sorry, I didn’t get that”: building AI assistants that scale using machine learning (PyData DC 2018)
1. Justina Petraityte, Developer Advocate @ Rasa
Going beyond ‘Sorry, I didn’t get that’: building
AI assistants that scale using machine learning
2. What are we focusing on during this workshop
Introduction
Goal:
Build a machine learning - powered assistant
Roadmap:
1. Natural Language Understanding
i. Introduction and theory
ii. Coding
2. Dialogue Handling
i. Introduction and theory
ii. Coding
3. Closing the feedback loop
4. Questions
3. Setup
Introduction
1. Jupyter notebook in python 3.6
2. Download:
Repository: https://github.com/RasaHQ/rasa-workshop-pydata-dc
Alternative:
1. Google Colab: https://pydata.org/dc2018/proposals/43/
4. Conversational AI will
dramatically change how
your customers interact
with you.
Example of a live Skill:
A customer can change her
address via Facebook Messenger
6. Rasa the OSS to build conversational software with ML
Introduction
Backend,
database,
API, etc.
Dialogue
Management
“Brain”
Input Modules
“Ears”
NLU, GUI elements,
context, personal
info
Output
Modules
“Mouth”
NLG, GUI elements
Connector
Modules
Connector to any
conversational
platforms
“What’s the weather
like tomorrow?”
(User Request via
text or voice)
“It will be sunny and
20°C.”
(AI response via
text or voice)
https://github.com/RasaHQ/rasa-workshop-pydata-dc
7. Why Rasa?
Introduction
Runs Locally
● No Network
Overhead
● Control QoS
● Deploy anywhere
Hackable
● Tune models for your
use case
Own Your Data
● Don’t hand data over
to big tech co’s
● Avoid vendor lock-in
7https://github.com/RasaHQ/rasa-workshop-pydata-dc
9. Rasa NLU: Natural Language Understanding
Under the Hood
Goal: create structured data
I have a new address, it’s
709 King St, San Francisco
https://github.com/RasaHQ/rasa-workshop-pydata-dc
10. Natural Language Understanding
Natural Language
Understanding
What’s the
weather like
tomorrow?
Example Intent Classification Pipeline
”What’s the weather like tomorrow?” { “intent”: “request_weather” }
Vectorization Intent Classification
Under The Hood
https://github.com/RasaHQ/rasa-workshop-pydata-dc
11. Rasa NLU: Natural Language Understanding
Under the Hood
Bags are your friend
Bag
of
words
SVM
greet
goodbye
thank_you
request_weather
confirm
What’s the
weather like
tomorrow?
https://github.com/RasaHQ/rasa-workshop-pydata-dc
12. Rasa NLU: Natural Language Understanding
Under the Hood
Natural Language
Understanding
What’s the
weather like
tomorrow?
Example Entity Extraction Pipeline
”What’s the weather like tomorrow?” { “date”: “tomorrow” }
Tokenizer
Part of Speech
Tagger
Chunker
Named Entity
Recognition
Entity Extraction
Example Intent Classification Pipeline
”What’s the weather like tomorrow?” { “intent”: “request_weather” }
Vectorization Intent Classification
https://github.com/RasaHQ/rasa-workshop-pydata-dc
13. Rasa NLU: Entity Extraction
Under the Hood
Where can I get a burrito in the 2nd arrondissement ?
cuisine location
1. Binary classifier is_entity & then entity_classifier
2. Direct structured prediction
averaged perceptron
https://github.com/RasaHQ/rasa-workshop-pydata-dc
15. Supervised Word Vectors (the new way)
Text Classification beyond word2vec
References:
WSABIE (Weston, Bengio, Usunier)
StarSpace (Wu et al)
https://github.com/RasaHQ/rasa-workshop-pydata-dc
18. Why Dialogue Handling with Rasa Core?
Under The Hood
● No more state machines!
● Reinforcement Learning: too much
data, reward functions...
● Need a simple solution for everyone
20. Rasa Core: Dialogue Handling
Under The Hood
“What’s the weather
like tomorrow?”
Intent
Entities
next
ActionState
previous
Action
“Thanks.”
after next
Action
updated
State
“It will be sunny
and 20°C.”
SVM
Recurrent NN
...
21. Rasa Core: Dialogue Handling
Under The Hood
“What’s the weather
like tomorrow?”
“It will be sunny and
20°C.”
Entity
Input
Action Mask
Renormal-
ization
Sample
action
Action
type?
Response
API Call
Recurrent
NN
API Call
Entity
Output
Intent
Classification
Entity
Extraction
Similar to LSTM-dialogue prediction paper: https://arxiv.org/abs/1606.01269
21
23. Rasa Core: Dialogue Training
Under The Hood
Issue: How to get started? Interactive Learning→
What’s the weather
like tomorrow?
How did you like it?
Correct wrong
behaviour
Retrain model
It will be sunny and
20°C.
26. Closing The Loop
Final Thoughts
“What’s the weather
like tomorrow?”
(User Request via
text or voice)
New Training
Data
Retrain ML
Models
Correct Training
Data
Relabel
Collected Data
Use Improved
Model
27. Open challenges
Final Thoughts
● Handling OOV words
● Multi language entity recognition
● Combination of dialogue models
● Negation
We’re constantly working on improving our models!
For those that are curious:
28. ● Techniques to handle small data sets are key to get started with
conversational AI
● Deep ML techniques help advance state of the art NLU and
conversational AI
● Combine ML with traditional Programming and Rules where
appropriate
● Abandon flow charts
Summary
Final Thoughts
4 take home thoughts: