Machine learning is used widely on the web today. Apache Mahout provides scalable machine learning libraries for common tasks like recommendation, clustering, classification and pattern mining. It implements many algorithms like k-means clustering in a MapReduce framework allowing them to scale to large datasets. Mahout functionality includes collaborative filtering, document clustering, categorization and frequent pattern mining.
Machine Learning and Apache Mahout : An IntroductionVarad Meru
An Introductory presentation on Machine Learning and Apache Mahout. I presented it at the BigData Meetup - Pune Chapter's first meetup (http://www.meetup.com/Big-Data-Meetup-Pune-Chapter/).
SDEC2011 Mahout - the what, the how and the whyKorea Sdec
Mahout is an open source machine learning library from Apache. From its humble beginnings at Apache Lucene, the project has grown into a active community of developers, machine learning experts and enthusiasts. With v0.5 released recently, the project has been focussing full steam on developing stable APIs with an eye on our major milestone of v1.0. The speaker has been with Mahout from his days in college as a computer science student. The talk will focus on the major use cases of Mahout. The design decisions, things that worked, things that didn't, and things to expect in the future releases.
http://sdec.kr/
Machine Learning and Apache Mahout : An IntroductionVarad Meru
An Introductory presentation on Machine Learning and Apache Mahout. I presented it at the BigData Meetup - Pune Chapter's first meetup (http://www.meetup.com/Big-Data-Meetup-Pune-Chapter/).
SDEC2011 Mahout - the what, the how and the whyKorea Sdec
Mahout is an open source machine learning library from Apache. From its humble beginnings at Apache Lucene, the project has grown into a active community of developers, machine learning experts and enthusiasts. With v0.5 released recently, the project has been focussing full steam on developing stable APIs with an eye on our major milestone of v1.0. The speaker has been with Mahout from his days in college as a computer science student. The talk will focus on the major use cases of Mahout. The design decisions, things that worked, things that didn't, and things to expect in the future releases.
http://sdec.kr/
This presentation lets you know about Apache Mahout.
The Apache Mahout is a machine learning library and the main goal is to build scalable machine learning libraries.
Cassandra Summit 2014: Apache Spark - The SDK for All Big Data PlatformsDataStax Academy
Apache Spark has grown to be one of the largest open source communities in big data, with over 190 developers and dozens of companies contributing. The latest 1.0 release alone includes contributions from 117 people. A clean API, interactive shell, distributed in-memory computation, stream processing, interactive SQL, and libraries delivering everything from machine learning to graph processing make it an excellent unified platform to solve a number of problems. Apache Spark works very well with a growing number of big data solutions, including Cassandra and Hadoop. Come learn about Apache Spark and see how easy it is for you to get started using Spark to build your own high performance big data applications today.
An introduction to Apache Mahout presented at Apache BarCamp DC, May 19, 2012
A brief introduction to the examples and links to more resources for further exploration.
Mahout is an open source machine learning java library from Apache Software Foundation, and therefore platform independent, that provides a fertile framework and collection of patterns and ready-made component for testing and deploying new large-scale algorithms.
With these slides we aims at providing a deeper understanding of its architecture.
This presentation lets you know about Apache Mahout.
The Apache Mahout is a machine learning library and the main goal is to build scalable machine learning libraries.
Cassandra Summit 2014: Apache Spark - The SDK for All Big Data PlatformsDataStax Academy
Apache Spark has grown to be one of the largest open source communities in big data, with over 190 developers and dozens of companies contributing. The latest 1.0 release alone includes contributions from 117 people. A clean API, interactive shell, distributed in-memory computation, stream processing, interactive SQL, and libraries delivering everything from machine learning to graph processing make it an excellent unified platform to solve a number of problems. Apache Spark works very well with a growing number of big data solutions, including Cassandra and Hadoop. Come learn about Apache Spark and see how easy it is for you to get started using Spark to build your own high performance big data applications today.
An introduction to Apache Mahout presented at Apache BarCamp DC, May 19, 2012
A brief introduction to the examples and links to more resources for further exploration.
Mahout is an open source machine learning java library from Apache Software Foundation, and therefore platform independent, that provides a fertile framework and collection of patterns and ready-made component for testing and deploying new large-scale algorithms.
With these slides we aims at providing a deeper understanding of its architecture.
Final Presentation for Pattern RecognitiondavidglenEE
Summary of a 4-fold cross validation study performed on classifiers used in OCR. Presented for a class in pattern recognition. OCstar Inc. is a made up company name for the purpose of the presentation, per the requirements of the project.
This article got published in the Software Developer's Journal's February Edition.
It describes the use of MapReduce paradigm to design Clustering algorithms and explain three algorithms using MapReduce.
- K-Means Clustering
- Canopy Clustering
- MinHash Clustering
This slides explains how Convolution Neural Networks can be coded using Google TensorFlow.
Video available at : https://www.youtube.com/watch?v=EoysuTMmmMc
This contains the agenda of the Spark Meetup I organised in Bangalore on Friday, the 23rd of Jan 2014. It carries the slides for the talk I gave on distributed deep learning over Spark
Video: https://www.youtube.com/watch?v=Rt2oHibJT4k
Technologies such as Hadoop have addressed the "Volume" problem of Big Data, and technologies such as Spark have recently addressed the "Velocity" problem – but the "Variety" problem is largely unaddressed – there is a lot of manual "data wrangling" to mange data models.
These manual processes do not scale well. Not only is the variety of data increasing, also the rate of change in the data definitions is increasing. We can’t keep up. NoSQL data repositories can handle storage, but we need effective models of the data to fully utilize it.
This talk will present tools and a methodology to manage Big Data Models in a rapidly changing world. This talk covers:
Creating Semantic Metadata Models of Big Data Resources
Graphical UI Tools for Big Data Models
Tools to synchronize Big Data Models and Application Code
Using NoSQL Databases, such as Amazon DynamoDB, with Big Data Models
Using Big Data Models with Hadoop, Storm, Spark, Giraph, and Inference
Using Big Data Models with Machine Learning to generate Predictive Models
Developer Collaborative/Coordination processes using Big Data Models and Git
Managing change – Big Data Models with rapidly changing Data Resources
Solr is a great tool to have in the data scientist toolbox. In this talk, I walk through several demos of using Solr to data science activities as well as explore various use cases for Solr and data science
In just a few short years, search has quickly evolved from being a small text box in the nether regions of a website to being front and center in our lives. Increasingly, however, search engine technology is also being used for practical, real time recommendations, events processing, complex spatial functionality and time series analysis capable of not only matching user's queries in text, but also driving real time decision making and analytics. In fact, open source Apache Lucene/Solr can do all of this and more by taking advantage of new data structures and algorithms that complement more traditional IR approaches. In this demo-driven talk, Lucene committer Grant Ingersoll will take a look at some of the new and exciting ways users are leveraging Lucene/Solr and related technology to drive deeper insight into information needs that go beyond keywords in a text box.
A 1 hour intro to search, Apache Lucene and Solr, and LucidWorks Search. Contains a quick start with LucidWorks Search and a demo using financial data (See Github prj: http://bit.ly/lws-financial) as well as some basic vocab and search explanations
http://sigir2013.ie/industry_track.html#GrantIngersoll
Abstract: Apache Lucene and Solr are the most widely deployed search technology on the planet, powering sites like Twitter, Wikipedia, Zappos and countless applications across a large array of domains. They are also free, open source, extensible and extremely scalable. Lucene and Solr also contain a large number of features for solving common information retrieval problems ranging from pluggable posting list compression and scoring algorithms to faceting and spell checking. Increasingly, Lucene and Solr also are being (ab)used to power applications going way beyond the search box. In this talk, we'll explore the features and capabilities of Lucene and Solr 4.x, as well as look at how to (ab)use your search engine technology for fun and profit.
Presentation from March 18th, 2013 Triangle Java User Group on Taming Text. Presentation covers search, question answering, clustering, classification, named entity recognition, etc. See http://www.manning.com/ingersoll for more.
Intro talk for UNC School of Information and Library Science. Covers basics of Lucene and Solr as well as info on Lucene/Solr jobs, opportunities, etc.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...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 the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Key Trends Shaping the Future of Infrastructure.pdf
Intro to Mahout -- DC Hadoop
1. Intro to Apache Mahout Grant Ingersoll Lucid Imagination http://www.lucidimagination.com
2. Anyone Here Use Machine Learning? Any users of: Google? Search? Priority Inbox? Facebook? Twitter? LinkedIn?
3. Topics Background and Use Cases What can you do in Mahout? Where’s the community at? Resources K-Means in Hadoop (time permitting)
4. Definition “Machine Learning is programming computers to optimize a performance criterion using example data or past experience” Intro. To Machine Learning by E. Alpaydin Subset of Artificial Intelligence Lots of related fields: Information Retrieval Stats Biology Linear algebra Many more
5. Common Use Cases Recommend friends/dates/products Classify content into predefined groups Find similar content Find associations/patterns in actions/behaviors Identify key topics/summarize text Documents and Corpora Detect anomalies/fraud Ranking search results Others?
6. Apache Mahout An Apache Software Foundation project to create scalable machine learning libraries under the Apache Software License http://mahout.apache.org Why Mahout? Many Open Source ML libraries either: Lack Community Lack Documentation and Examples Lack Scalability Lack the Apache License Or are research-oriented Definition:http://dictionary.reference.com/browse/mahout
7. What does scalable mean to us? Goal: Be as fast and efficient as possible given the intrinsic design of the algorithm Some algorithms won’t scale to massive machine clusters Others fit logically on a Map Reduce framework like Apache Hadoop Still others will need different distributed programming models Others are already fast (SGD) Be pragmatic
8. Sampling of Who uses Mahout? https://cwiki.apache.org/confluence/display/MAHOUT/Powered+By+Mahout
9. What Can I do with Mahout Right Now?3C + FPM + O = Mahout
10. Collaborative Filtering Extensive framework for collaborative filtering (recommenders) Recommenders User based Item based Online and Offline support Offline can utilize Hadoop Many different Similarity measures Cosine, LLR, Tanimoto, Pearson, others
11. Clustering Document level Group documents based on a notion of similarity K-Means, Fuzzy K-Means, Dirichlet, Canopy, Mean-Shift, EigenCuts (Spectral) All Map/Reduce Distance Measures Manhattan, Euclidean, other Topic Modeling Cluster words across documents to identify topics Latent Dirichlet Allocation (M/R)
12.
13.
14. Other Primitive Collections! Collocations (M/R) Math library Vectors, Matrices, etc. Noise Reduction via Singular Value Decomp (M/R)
15. Prepare Data from Raw content Data Sources: Lucene integration bin/mahout lucene.vector… Document Vectorizer bin/mahout seqdirectory … bin/mahout seq2sparse … Programmatically See the Utils module in Mahout and the Iterator<Vector> classes Database File system
16. How to: Command Line Most algorithms have a Driver program $MAHOUT_HOME/bin/mahout.shhelps with most tasks Prepare the Data Different algorithms require different setup Run the algorithm Single Node Hadoop Print out the results or incorporate into application Several helper classes: LDAPrintTopics, ClusterDumper, etc.
17. What’s Happening Now? Unified Framework for Clustering and Classification 0.5 release on the horizon (May?) Working towards 1.0 release by focusing on: Tests, examples, documentation API cleanup and consistency Gearing up for Google Summer of Code New M/R work for Hidden Markov Models
18. Summary Machine learning is all over the web today Mahout is about scalable machine learning Mahout has functionality for many of today’s common machine learning tasks Many Mahout implementations use Hadoop
20. Resources “Mahout in Action” Owen, Anil, Dunning and Friedman http://awe.sm/5FyNe “Introducing Apache Mahout” http://www.ibm.com/developerworks/java/library/j-mahout/ “Taming Text” by Ingersoll, Morton, Farris “Programming Collective Intelligence” by Toby Segaran “Data Mining - Practical Machine Learning Tools and Techniques” by Ian H. Witten and Eibe Frank “Data-Intensive Text Processing with MapReduce” by Jimmy Lin and Chris Dyer
22. K-Means in Map-Reduce Input: Mahout Vectors representing the original content Either: A predefined set of initial centroids (Can be from Canopy) --k – The number of clusters to produce Iterate Do the centroid calculation (more in a moment) Clustering Step (optional) Output Centroids (as Mahout Vectors) Points for each Centroid (if Clustering Step was taken)
23. Map-Reduce Iteration Each Iteration calculates the Centroids using: KMeansMapper KMeansCombiner KMeansReducer Clustering Step Calculate the points for each Centroid using: KMeansClusterMapper
24. KMeansMapper During Setup: Load the initial Centroids (or the Centroids from the last iteration) Map Phase For each input Calculate it’s distance from each Centroid and output the closest one Distance Measures are pluggable Manhattan, Euclidean, Squared Euclidean, Cosine, others
25. KMeansReducer Setup: Load up clusters Convergence information Partial sums from KMeansCombiner (more in a moment) Reduce Phase Sum all the vectors in the cluster to produce a new Centroid Check for Convergence Output cluster
26. KMeansCombiner Just like KMeansReducer, but only produces partial sum of the cluster based on the data local to the Mapper
27. KMeansClusterMapper Some applications only care about what the Centroids are, so this step is optional Setup: Load up the clusters and the DistanceMeasure used Map Phase Calculate which Cluster the point belongs to Output <ClusterId, Vector>
Editor's Notes
3C: The three C’s: clustering, classification and collaborative filteringFPM: Frequent patternset miningO: Other (math, collections, etc.)
Convergence just checks to see how far the centroid has moved from the previous centroid