This document summarizes the 22nd ACM SIGKDD conference on knowledge discovery and data mining. It discusses the following topics in 3 sentences or less each:
- Overview of the conference with ~80 sessions and 2,700 participants
- Popular business applications of data mining like recommendation systems, predictive maintenance, and customer targeting
- The typical predictive modeling flow including data preparation, model training, evaluation, and deployment
1. Knowledge discovery in production requires automation due to the growth of information, devices, and knowledge workers.
2. A core dataflow model engine is needed to preprocess data and compose networked intelligence solutions for emerging applications.
3. Product solutions include hybrid SaaS factory subscriptions and applications via an open marketplace to deliver business value such as increased productivity and test time reduction for electronics manufacturing customers.
Data science involves collecting and analyzing large amounts of data to discover patterns and make predictions. It is an interdisciplinary field that uses techniques from mathematics, statistics, machine learning, and domain expertise. The key steps in a data science project are to explore the data through preprocessing, visualization, and modeling techniques; build a model using methods like machine learning algorithms, clustering, or decision trees; and apply the model to make predictions or other insights. Popular tools for data science include R, Python, and packages within them for statistical analysis, machine learning, and data visualization.
Machine and Deep Learning Application.
Applying big data learning techniques for a malware classification problem.
Code:
https://gist.github.com/indraneeld/7ffb182fd8eb87d6d463dedc001efad0
Acknowledgments:
Canadian Institute for Cybersecurity (CIC) project in collaboration with Canadian Centre for Cyber Security (CCCS).
This document proposes a predictive analytics solution to target hotel property recommendations to specific customer segments for an online travel agency. It involves using machine learning techniques to:
1. Identify customer segments and their preferences based on past booking and browsing data.
2. Quantitatively associate customer segments with property attributes like ratings, prices and brands.
3. Calculate performance scores for properties in different search result positions based on past booking data.
4. Integrate customer preference and property performance scores to determine the optimal placement of properties for different customer segments.
Machine learning algorithms aim to strike the right balance between accuracy on training data and the ability to generalize to new examples. Models that are too complex may memorize noise in the training set, while those that are too simple will not capture important patterns. Finding this balance is a core challenge in statistical learning theory.
This document provides an overview of machine learning and robotic vision. It discusses what machine learning is, how it is used in areas like security, business and medicine. It also discusses what learning means and different machine learning techniques. For robotic vision, it discusses what a robot and vision are, the advantages of robots, how robotic vision works and examples of processing images. It provides an example of machine learning and discusses the development and future of robots.
This document discusses machine learning approaches for threat detection in cybersecurity. It begins with an overview of machine learning applications in security like malware detection and classification. It then covers the machine learning toolkit, emphasizing that data is the most important factor. It describes supervised learning techniques like regression and support vector machines. It also discusses challenges like the curse of dimensionality and separating sparse signals from noise in the data. The key takeaways are that machine learning can provide scalable threat detection when done correctly by focusing on relevant predictive data and understanding its limitations and algorithms.
This document summarizes the 22nd ACM SIGKDD conference on knowledge discovery and data mining. It discusses the following topics in 3 sentences or less each:
- Overview of the conference with ~80 sessions and 2,700 participants
- Popular business applications of data mining like recommendation systems, predictive maintenance, and customer targeting
- The typical predictive modeling flow including data preparation, model training, evaluation, and deployment
1. Knowledge discovery in production requires automation due to the growth of information, devices, and knowledge workers.
2. A core dataflow model engine is needed to preprocess data and compose networked intelligence solutions for emerging applications.
3. Product solutions include hybrid SaaS factory subscriptions and applications via an open marketplace to deliver business value such as increased productivity and test time reduction for electronics manufacturing customers.
Data science involves collecting and analyzing large amounts of data to discover patterns and make predictions. It is an interdisciplinary field that uses techniques from mathematics, statistics, machine learning, and domain expertise. The key steps in a data science project are to explore the data through preprocessing, visualization, and modeling techniques; build a model using methods like machine learning algorithms, clustering, or decision trees; and apply the model to make predictions or other insights. Popular tools for data science include R, Python, and packages within them for statistical analysis, machine learning, and data visualization.
Machine and Deep Learning Application.
Applying big data learning techniques for a malware classification problem.
Code:
https://gist.github.com/indraneeld/7ffb182fd8eb87d6d463dedc001efad0
Acknowledgments:
Canadian Institute for Cybersecurity (CIC) project in collaboration with Canadian Centre for Cyber Security (CCCS).
This document proposes a predictive analytics solution to target hotel property recommendations to specific customer segments for an online travel agency. It involves using machine learning techniques to:
1. Identify customer segments and their preferences based on past booking and browsing data.
2. Quantitatively associate customer segments with property attributes like ratings, prices and brands.
3. Calculate performance scores for properties in different search result positions based on past booking data.
4. Integrate customer preference and property performance scores to determine the optimal placement of properties for different customer segments.
Machine learning algorithms aim to strike the right balance between accuracy on training data and the ability to generalize to new examples. Models that are too complex may memorize noise in the training set, while those that are too simple will not capture important patterns. Finding this balance is a core challenge in statistical learning theory.
This document provides an overview of machine learning and robotic vision. It discusses what machine learning is, how it is used in areas like security, business and medicine. It also discusses what learning means and different machine learning techniques. For robotic vision, it discusses what a robot and vision are, the advantages of robots, how robotic vision works and examples of processing images. It provides an example of machine learning and discusses the development and future of robots.
This document discusses machine learning approaches for threat detection in cybersecurity. It begins with an overview of machine learning applications in security like malware detection and classification. It then covers the machine learning toolkit, emphasizing that data is the most important factor. It describes supervised learning techniques like regression and support vector machines. It also discusses challenges like the curse of dimensionality and separating sparse signals from noise in the data. The key takeaways are that machine learning can provide scalable threat detection when done correctly by focusing on relevant predictive data and understanding its limitations and algorithms.
Big Data Open Source Tools and Trends: Enable Real-Time Business Intelligence...Perficient, Inc.
This document discusses big data tools and trends that enable real-time business intelligence from machine logs. It provides an overview of Perficient, a leading IT consulting firm, and introduces the speakers Eric Roch and Ben Hahn. It then covers topics like what constitutes big data, how machine data is a source of big data, and how tools like Hadoop, Storm, Elasticsearch can be used to extract insights from machine data in real-time through open source solutions and functional programming approaches like MapReduce. It also demonstrates a sample data analytics workflow using these tools.
The Challenges of Bringing Machine Learning to the MassesAlice Zheng
Why is it hard to build ML software, and why it is like designing a database. Jointly created with Sethu Raman (Dato/GraphLab). Talk at NIPS 2014 workshop on Software Engineering for Machine Learning (https://sites.google.com/site/software4ml/).
Wei Fang received an M.S. in Electrical and Computer Engineering from Carnegie Mellon University in 2017, with a focus on machine learning, deep learning, speech recognition, and cloud computing. He has internship experience at Microsoft Research Asia developing models to jointly train word and entity embeddings using Wikipedia and Freebase data. His projects include implementing a search engine, speech recognition system from scratch, and using recurrent neural networks with attention for machine reading. He also worked on accelerating speaker verification on GPUs and developing a Twitter analytics web service on AWS.
OpenPOWER Webinar on Machine Learning for Academic Research Ganesan Narayanasamy
The document discusses machine learning and deep learning techniques. It provides examples of different machine learning algorithms like decision trees, linear regression, neural networks and deep learning models. It also discusses applications of machine learning in areas like computer vision, natural language processing and bioinformatics. Finally, it talks about technologies that can help democratize machine learning like distributed computing frameworks and open source libraries.
This document provides an introduction to deep learning with Microsoft's Cognitive Toolkit (CNTK). It discusses key deep learning concepts and how they are implemented in CNTK, including neural networks, backpropagation, loss functions, and common network architectures like convolutional neural networks. It also outlines several of Microsoft's products that use deep learning like Cortana, Bing, and Skype Translator. Examples of training deep learning models with CNTK on datasets like MNIST using logistic regression, multi-layer perceptrons, and CNNs are also presented.
Traditional Machine Learning and Deep Learning on OpenPOWER/POWER systemsGanesan Narayanasamy
This presentation gave deep dive into various machine learning and deep learning algorithms followed by an overview of the hardware and software technologies for democratization of AI including OpenPOWER/POWER9 solutions.
One of the most popular buzz words nowadays in the technology world is “Machine Learning (ML).” Most economists and business experts foresee Machine Learning changing every aspect of our lives in the next 10 years through automating and optimizing processes. This is leading many organizations to seek experts who can implement Machine Learning into their businesses.
The paper will be written for statistical programmers who want to explore Machine Learning career, add Machine Learning skills to their experiences or enter a Machine Learning fields. The paper will discuss about personal journey to become to a Machine Learning Engineer from a statistical programmer. The paper will share my personal experience on what motivated me to start Machine Learning career, how I started it, and what I have learned and done to be a Machine Learning Engineer. In addition, the paper will also discuss the future of Machine Learning in Pharmaceutical Industry, especially in Biometric department.
cloud computing - concepts and technologies and mechanisms of tackling problems in cloud
you plz ignore who created it , plz focus on problem oriented points
This document discusses cloud computing concepts, technologies, and business implications. It provides an introduction to cloud models like IaaS, PaaS, and SaaS and demonstrates cloud capabilities through examples of Amazon AWS, Google App Engine, and Windows Azure. The document also discusses enabling technologies for cloud computing like virtualization and programming models for big data like MapReduce and Hadoop.
1. The document discusses architecting data science platforms for a dating product using an event-driven architecture that stores all data as a stream of events.
2. Key aspects of the architecture include an event history repository that stores real-time event streams, a Solr search index for querying events, and using the event stream for both online and offline machine learning.
3. The architecture aims to enable fast experimentation cycles by using the same code and data for production, development, and training machine learning models.
The Data Science Process - Do we need it and how to apply?Ivo Andreev
Machine learning is not black magic but a discipline that involves statistics, data science, analysis and hard work. From searching patterns and data preparation through applying and optimizing algorithms to obtaining usable predictions, one would need background and appropriate tools.
But do we need it, when there is already available AI as a service solution out there? Do we need to try hard with artificial neural networks? And if we decide to do so, what tools would be a safe bet?
In this session we will go through real world examples, mention key tools from Microsoft and open source world to do data science and machine learning and most importantly - we will provide a workflow and some best practices.
This document provides a summary of Piush Kapoor's experience as a Python developer with over 7 years of experience in web/application development using Python, Django, C++ and analytical programming. He has extensive experience developing web applications using Python, Django, PHP, JavaScript and databases like MySQL, Oracle and MongoDB. His experience also includes software development, agile methodologies, data analytics and front end development skills.
AI on Greenplum Using Apache MADlib and MADlib Flow - Greenplum Summit 2019VMware Tanzu
This document discusses machine learning and deep learning capabilities in Greenplum using Apache MADlib. It begins with an overview of MADlib, describing it as an open source machine learning library for PostgreSQL and Greenplum Database. It then discusses specific machine learning algorithms and techniques supported, such as linear regression, neural networks, graph algorithms, and more. It also covers scaling of algorithms like SVM and PageRank with increasing data and graph sizes. Later sections discuss deep learning integration with Greenplum, challenges of model management and operationalization, and introduces MADlib Flow as a tool to address those challenges through an end-to-end data science workflow in SQL.
This document is a resume for Dilnoza Bobokalonova that highlights her education and skills in electrical engineering, computer science, and data science. It summarizes her relevant coursework, projects, and work experience utilizing natural language processing, machine learning, and deep learning techniques. Her experience includes developing models to analyze patent data and predict future trends, as well as training students in programming languages and research. She has strong skills in languages like Java, Python, and C/C++ and technologies such as TensorFlow, Spark, and MongoDB.
The document provides an overview of cloud computing concepts, technologies, and business implications. It discusses cloud models including IaaS, PaaS, and SaaS. It demonstrates cloud capabilities through examples on Amazon AWS, Google App Engine, and Windows Azure. It also covers MapReduce and graph processing as cloud programming models and provides a case study on using cloud computing for a predictive quality project.
The document discusses cloud computing concepts and technologies. It provides an introduction to cloud models like IaaS, PaaS and SaaS and demonstrates cloud capabilities through examples on Amazon AWS, Google App Engine and Windows Azure. It also discusses the Hadoop distributed file system and MapReduce programming model for large scale data processing in the cloud.
The document discusses cloud computing concepts and technologies. It provides an introduction to cloud models like IaaS, PaaS and SaaS and demonstrates cloud capabilities through examples on Amazon AWS, Google App Engine and Windows Azure. It also discusses the Hadoop distributed file system and MapReduce programming model for large scale data processing in the cloud.
In this video from the ISC Big Data'14 Conference, Ted Willke from Intel presents: The Analytics Frontier of the Hadoop Eco-System.
"The Hadoop MapReduce framework grew out of an effort to make it easy to express and parallelize simple computations that were routinely performed at Google. It wasn’t long before libraries, like Apache Mahout, were developed to enable matrix factorization, clustering, regression, and other more complex analyses on Hadoop. Now, many of these libraries and their workloads are migrating to Apache Spark because it supports a wider class of applications than MapReduce and is more appropriate for iterative algorithms, interactive processing, and streaming applications. What’s next beyond Spark? Where is big data analytics processing headed? How will data scientists program these systems? In this talk, we will explore the current analytics frontier, the popular debates, and discuss some potentially clever additions. We will also share the emergent data science applications and collaborative university research that inform our thinking."
Learn more:
http://www.isc-events.com/bigdata14/schedule.html
and
http://www.intel.com/content/www/us/en/software/intel-graph-solutions.html
Watch the video presentation: https://www.youtube.com/watch?v=qlfx495Ekw0
OSCON 2014: Data Workflows for Machine LearningPaco Nathan
This document provides examples of different frameworks that can be used for machine learning data workflows, including KNIME, Python, Julia, Summingbird, Scalding, and Cascalog. It describes features of each framework such as KNIME's large number of integrations and visual workflow editing, Python's broad ecosystem, Julia's performance and parallelism support, Summingbird's ability to switch between Storm and Scalding backends, and Scalding's implementation of the Scala collections API over Cascading for compact workflow code. The document aims to familiarize readers with options for building machine learning data workflows.
Building a Scalable and reliable open source ML Platform with MLFlowGoDataDriven
This document discusses building a scalable and open source machine learning platform. It introduces MLOps and describes ING's ML batch platform use case. The machine learning lifecycle is presented, noting that operationalizing machine learning models is difficult due to infrastructure deployment challenges, lack of collaboration and standardization. An ideal MLOps approach is described with flexible, scalable, automated and standardized processes. Benefits of ING's MLOps approach include increased efficiency, speed, quality, security and auditability. Open source tools that could be leveraged are also presented.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Big Data Open Source Tools and Trends: Enable Real-Time Business Intelligence...Perficient, Inc.
This document discusses big data tools and trends that enable real-time business intelligence from machine logs. It provides an overview of Perficient, a leading IT consulting firm, and introduces the speakers Eric Roch and Ben Hahn. It then covers topics like what constitutes big data, how machine data is a source of big data, and how tools like Hadoop, Storm, Elasticsearch can be used to extract insights from machine data in real-time through open source solutions and functional programming approaches like MapReduce. It also demonstrates a sample data analytics workflow using these tools.
The Challenges of Bringing Machine Learning to the MassesAlice Zheng
Why is it hard to build ML software, and why it is like designing a database. Jointly created with Sethu Raman (Dato/GraphLab). Talk at NIPS 2014 workshop on Software Engineering for Machine Learning (https://sites.google.com/site/software4ml/).
Wei Fang received an M.S. in Electrical and Computer Engineering from Carnegie Mellon University in 2017, with a focus on machine learning, deep learning, speech recognition, and cloud computing. He has internship experience at Microsoft Research Asia developing models to jointly train word and entity embeddings using Wikipedia and Freebase data. His projects include implementing a search engine, speech recognition system from scratch, and using recurrent neural networks with attention for machine reading. He also worked on accelerating speaker verification on GPUs and developing a Twitter analytics web service on AWS.
OpenPOWER Webinar on Machine Learning for Academic Research Ganesan Narayanasamy
The document discusses machine learning and deep learning techniques. It provides examples of different machine learning algorithms like decision trees, linear regression, neural networks and deep learning models. It also discusses applications of machine learning in areas like computer vision, natural language processing and bioinformatics. Finally, it talks about technologies that can help democratize machine learning like distributed computing frameworks and open source libraries.
This document provides an introduction to deep learning with Microsoft's Cognitive Toolkit (CNTK). It discusses key deep learning concepts and how they are implemented in CNTK, including neural networks, backpropagation, loss functions, and common network architectures like convolutional neural networks. It also outlines several of Microsoft's products that use deep learning like Cortana, Bing, and Skype Translator. Examples of training deep learning models with CNTK on datasets like MNIST using logistic regression, multi-layer perceptrons, and CNNs are also presented.
Traditional Machine Learning and Deep Learning on OpenPOWER/POWER systemsGanesan Narayanasamy
This presentation gave deep dive into various machine learning and deep learning algorithms followed by an overview of the hardware and software technologies for democratization of AI including OpenPOWER/POWER9 solutions.
One of the most popular buzz words nowadays in the technology world is “Machine Learning (ML).” Most economists and business experts foresee Machine Learning changing every aspect of our lives in the next 10 years through automating and optimizing processes. This is leading many organizations to seek experts who can implement Machine Learning into their businesses.
The paper will be written for statistical programmers who want to explore Machine Learning career, add Machine Learning skills to their experiences or enter a Machine Learning fields. The paper will discuss about personal journey to become to a Machine Learning Engineer from a statistical programmer. The paper will share my personal experience on what motivated me to start Machine Learning career, how I started it, and what I have learned and done to be a Machine Learning Engineer. In addition, the paper will also discuss the future of Machine Learning in Pharmaceutical Industry, especially in Biometric department.
cloud computing - concepts and technologies and mechanisms of tackling problems in cloud
you plz ignore who created it , plz focus on problem oriented points
This document discusses cloud computing concepts, technologies, and business implications. It provides an introduction to cloud models like IaaS, PaaS, and SaaS and demonstrates cloud capabilities through examples of Amazon AWS, Google App Engine, and Windows Azure. The document also discusses enabling technologies for cloud computing like virtualization and programming models for big data like MapReduce and Hadoop.
1. The document discusses architecting data science platforms for a dating product using an event-driven architecture that stores all data as a stream of events.
2. Key aspects of the architecture include an event history repository that stores real-time event streams, a Solr search index for querying events, and using the event stream for both online and offline machine learning.
3. The architecture aims to enable fast experimentation cycles by using the same code and data for production, development, and training machine learning models.
The Data Science Process - Do we need it and how to apply?Ivo Andreev
Machine learning is not black magic but a discipline that involves statistics, data science, analysis and hard work. From searching patterns and data preparation through applying and optimizing algorithms to obtaining usable predictions, one would need background and appropriate tools.
But do we need it, when there is already available AI as a service solution out there? Do we need to try hard with artificial neural networks? And if we decide to do so, what tools would be a safe bet?
In this session we will go through real world examples, mention key tools from Microsoft and open source world to do data science and machine learning and most importantly - we will provide a workflow and some best practices.
This document provides a summary of Piush Kapoor's experience as a Python developer with over 7 years of experience in web/application development using Python, Django, C++ and analytical programming. He has extensive experience developing web applications using Python, Django, PHP, JavaScript and databases like MySQL, Oracle and MongoDB. His experience also includes software development, agile methodologies, data analytics and front end development skills.
AI on Greenplum Using Apache MADlib and MADlib Flow - Greenplum Summit 2019VMware Tanzu
This document discusses machine learning and deep learning capabilities in Greenplum using Apache MADlib. It begins with an overview of MADlib, describing it as an open source machine learning library for PostgreSQL and Greenplum Database. It then discusses specific machine learning algorithms and techniques supported, such as linear regression, neural networks, graph algorithms, and more. It also covers scaling of algorithms like SVM and PageRank with increasing data and graph sizes. Later sections discuss deep learning integration with Greenplum, challenges of model management and operationalization, and introduces MADlib Flow as a tool to address those challenges through an end-to-end data science workflow in SQL.
This document is a resume for Dilnoza Bobokalonova that highlights her education and skills in electrical engineering, computer science, and data science. It summarizes her relevant coursework, projects, and work experience utilizing natural language processing, machine learning, and deep learning techniques. Her experience includes developing models to analyze patent data and predict future trends, as well as training students in programming languages and research. She has strong skills in languages like Java, Python, and C/C++ and technologies such as TensorFlow, Spark, and MongoDB.
The document provides an overview of cloud computing concepts, technologies, and business implications. It discusses cloud models including IaaS, PaaS, and SaaS. It demonstrates cloud capabilities through examples on Amazon AWS, Google App Engine, and Windows Azure. It also covers MapReduce and graph processing as cloud programming models and provides a case study on using cloud computing for a predictive quality project.
The document discusses cloud computing concepts and technologies. It provides an introduction to cloud models like IaaS, PaaS and SaaS and demonstrates cloud capabilities through examples on Amazon AWS, Google App Engine and Windows Azure. It also discusses the Hadoop distributed file system and MapReduce programming model for large scale data processing in the cloud.
The document discusses cloud computing concepts and technologies. It provides an introduction to cloud models like IaaS, PaaS and SaaS and demonstrates cloud capabilities through examples on Amazon AWS, Google App Engine and Windows Azure. It also discusses the Hadoop distributed file system and MapReduce programming model for large scale data processing in the cloud.
In this video from the ISC Big Data'14 Conference, Ted Willke from Intel presents: The Analytics Frontier of the Hadoop Eco-System.
"The Hadoop MapReduce framework grew out of an effort to make it easy to express and parallelize simple computations that were routinely performed at Google. It wasn’t long before libraries, like Apache Mahout, were developed to enable matrix factorization, clustering, regression, and other more complex analyses on Hadoop. Now, many of these libraries and their workloads are migrating to Apache Spark because it supports a wider class of applications than MapReduce and is more appropriate for iterative algorithms, interactive processing, and streaming applications. What’s next beyond Spark? Where is big data analytics processing headed? How will data scientists program these systems? In this talk, we will explore the current analytics frontier, the popular debates, and discuss some potentially clever additions. We will also share the emergent data science applications and collaborative university research that inform our thinking."
Learn more:
http://www.isc-events.com/bigdata14/schedule.html
and
http://www.intel.com/content/www/us/en/software/intel-graph-solutions.html
Watch the video presentation: https://www.youtube.com/watch?v=qlfx495Ekw0
OSCON 2014: Data Workflows for Machine LearningPaco Nathan
This document provides examples of different frameworks that can be used for machine learning data workflows, including KNIME, Python, Julia, Summingbird, Scalding, and Cascalog. It describes features of each framework such as KNIME's large number of integrations and visual workflow editing, Python's broad ecosystem, Julia's performance and parallelism support, Summingbird's ability to switch between Storm and Scalding backends, and Scalding's implementation of the Scala collections API over Cascading for compact workflow code. The document aims to familiarize readers with options for building machine learning data workflows.
Building a Scalable and reliable open source ML Platform with MLFlowGoDataDriven
This document discusses building a scalable and open source machine learning platform. It introduces MLOps and describes ING's ML batch platform use case. The machine learning lifecycle is presented, noting that operationalizing machine learning models is difficult due to infrastructure deployment challenges, lack of collaboration and standardization. An ideal MLOps approach is described with flexible, scalable, automated and standardized processes. Benefits of ING's MLOps approach include increased efficiency, speed, quality, security and auditability. Open source tools that could be leveraged are also presented.
Similar to Distributed Machine Learning: 1. A New Era (20)
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...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 automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
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.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
2. Story Outline
• Use existing frameworks (2007~2010)
• Methods: Frequent itemset mining, Collaborative
filtering, Spectral clustering, Graph partitioning,
Restricted Boltzmann machine, Latent topic modeling
• Frameworks: MPI, MapReduce, Pregel, GBR
• Developing frameworks (2010~2014)
• MapReduce Lite (C++) for language models
• Peacock (Go) for latent topic modeling
3. Lessons
• Internet services relies on machine intelligence
• Intelligence comes from learning users’ behavior
• Value lies in long tails ▹
• It is more about big than fast
• Good system = good algorithm + good architecture
• More about engineering than math
• It is Industrial Revolution!
4. Pitfalls
• De-noise data ▹
• Parallelize models in papers and textbooks
• Use existing frameworks
• MPI
• Mix frameworks with cluster operating systems ▹
• Less talking about production
• Use standard measures
• Java or Python ▹
5. Environment
• Balance business with killing tech development
• Separated
• Combined
• Switching
• Standalone business
• ML software
• ML frameworks
• MLaaP or MLaaS
10. var resp *Response
select {
case b := <- rpc.Call("B"):
resp = extract(b)
case c := <- rpc.Call("C"):
resp = extract(c)
case e := <- rpc.Call("E"):
resp = extract(e)
case <- time.Timeout(1*second):
resp = nil
}
// use resp here.
11. var mutex = NewMutex();
var returns = 0;
var timer = setTimeout(timeout,1*second);
!
function rpcResp(resp) {
mutex.Lock();
if (returns == 0) {
clearTimeout(timer);
use(resp);
}
returns++;
mutex.Unlock();
}
function timeout() {
mutex.Lock();
returns++;
mutex.Unlock();
}
!
rpc.Call("B", rpcResp);
rpc.Call("C", rpcResp);
rpc.Call("D", rpcResp);
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