Detailed design for a robust counter as well as design for a completely on-line multi-armed bandit implementation that uses the new Bayesian Bandit algorithm.
This talk is one that I gave to the HPTS workshop in Asilomar in 2009. It describes the ideas behind micro-sharding and outlines how Katta can manage micro-shards.
Some builds and spacing are off because this was exported as power point from Keynote.
Distributed real time stream processing- why and howPetr Zapletal
In this talk you will discover various state-of-the-art open-source distributed streaming frameworks, their similarities and differences, implementation trade-offs, their intended use-cases, and how to choose between them. Petr will focus on the popular frameworks, including Spark Streaming, Storm, Samza and Flink. You will also explore theoretical introduction, common pitfalls, popular architectures, and much more.
The demand for stream processing is increasing. Immense amounts of data has to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications, include trading, social networks, the Internet of Things, and system monitoring, are becoming more and more important. A number of powerful, easy-to-use open source platforms have emerged to address this.
Petr's goal is to provide a comprehensive overview of modern streaming solutions and to help fellow developers with picking the best possible solution for their particular use-case. Join this talk if you are thinking about, implementing, or have already deployed a streaming solution.
Detailed design for a robust counter as well as design for a completely on-line multi-armed bandit implementation that uses the new Bayesian Bandit algorithm.
This talk is one that I gave to the HPTS workshop in Asilomar in 2009. It describes the ideas behind micro-sharding and outlines how Katta can manage micro-shards.
Some builds and spacing are off because this was exported as power point from Keynote.
Distributed real time stream processing- why and howPetr Zapletal
In this talk you will discover various state-of-the-art open-source distributed streaming frameworks, their similarities and differences, implementation trade-offs, their intended use-cases, and how to choose between them. Petr will focus on the popular frameworks, including Spark Streaming, Storm, Samza and Flink. You will also explore theoretical introduction, common pitfalls, popular architectures, and much more.
The demand for stream processing is increasing. Immense amounts of data has to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications, include trading, social networks, the Internet of Things, and system monitoring, are becoming more and more important. A number of powerful, easy-to-use open source platforms have emerged to address this.
Petr's goal is to provide a comprehensive overview of modern streaming solutions and to help fellow developers with picking the best possible solution for their particular use-case. Join this talk if you are thinking about, implementing, or have already deployed a streaming solution.
Some notes about spark streming positioning give the current players: Beam, Flink, Storm et al. Helpful if you have to choose an Streaming engine for your project.
What are algorithms? How can I build a machine learning model? In machine learning, training large models on a massive amount of data usually improves results. Our customers report, however, that training such models and deploying them is either operationally prohibitive or outright impossible for them. At Amazon, we created a collection of machine learning algorithms that scale to any amount of data, including k-means clustering for data segmentation, factorisation machines for recommendations, and time-series forecasting. This talk will discuss those algorithms, understand where and how they can be used, and our design choices.
Bobby Evans and Tom Graves, the engineering leads for Spark and Storm development at Yahoo will talk about how these technologies are used on Yahoo's grids and reasons why to use one or the other.
Bobby Evans is the low latency data processing architect at Yahoo. He is a PMC member on many Apache projects including Storm, Hadoop, Spark, and Tez. His team is responsible for delivering Storm as a service to all of Yahoo and maintaining Spark on Yarn for Yahoo (Although Tom really does most of that work).
Tom Graves a Senior Software Engineer on the Platform team at Yahoo. He is an Apache PMC member on Hadoop, Spark, and Tez. His team is responsible for delivering and maintaining Spark on Yarn for Yahoo.
Real time big data analytics with Storm by Ron Bodkin of Think Big AnalyticsData Con LA
This talk provides an overview of the open source Storm system for processing Big Data in realtime. The talk starts with an overview of the technology, including key components: Nimbus, Zookeeper, Topology, Tuple, Trident. It looks at integration with Hadoop through YARN and recent improvements. The presentation then dives into the complex Big Data architecture in which Storm can be integrated . The result is a compelling stack of technologies including integrated Hadoop clusters, MPP, and NoSQL databases.
After this, we look at example use cases for Storm: real-time advertising statistics, updating a Machine Learned model for content popularity predictions, and financial compliance monitoring.
PHP Backends for Real-Time User Interaction using Apache Storm.DECK36
Engaging users in real-time is the topic of our times. Whether it’s a game, a shop, or a content-network, the aim remains the same: providing a personalized experience. In this workshop we will look under the hood of Apache Storm and lay a firm foundation on how to use it with PHP. By that, you can leverage your existing codebase and PHP expertise for an entirely new world: real-time analytics and business logic working on message streams. During the course of the workshop, we will introduce Apache Storm and take a look at all of its components. We will then skyrocket the applicability of Storm by showing you how to implement their components with PHP. All exercises will be conducted using an example project, the infamous and most exhilarating lolcat kitten game ever conceived: Plan 9 From Outer Kitten. In order to follow the hands-on excercises, you will need a development VM prepared by us with all relevant system components and our project repositories. To make the workshop experience as smooth as possible for all participants, please bring a prepared computer to the workshop, as there will be no time to deal with installation and setup issues. Please download all prerequisites and install them as described: VM, Plan 9 webapp, Plan 9 storm backend, (Tutorial: https://github.com/DECK36/plan9_workshop_tutorial ).
Slides from talk given at the NYC Cassandra Meetup. Discussing how Storm works and how it integrates well with Apache Cassandra.
There is also a segway into a example project that uses Storm and Cassandra to implement a scalable reactive web crawler.
http://github.com/tjake/stormscraper
Streaming data presents new challenges for statistics and machine learning on extremely large data sets. Tools such as Apache Storm, a stream processing framework, can power range of data analytics but lack advanced statistical capabilities. These slides are from the Apache.con talk, which discussed developing streaming algorithms with the flexibility of both Storm and R, a statistical programming language.
At the talk I dicsussed issues of why and how to use Storm and R to develop streaming algorithms; in particular I focused on:
• Streaming algorithms
• Online machine learning algorithms
• Use cases showing how to process hundreds of millions of events a day in (near) real time
See: https://apacheconna2015.sched.org/event/09f5a1cc372860b008bce09e15a034c4#.VUf7wxOUd5o
Storm – Streaming Data Analytics at Scale - StampedeCon 2014StampedeCon
At StampedeCon 2014, Scott Shaw (Hortonworks) and Kit Menke (Enteprise Holdings) presented "Storm – Streaming Data Analytics at Scale"
Storm’s primary purpose is to provide real-time analytics against fast moving data before its stored. The use cases range from fraud detection, machine learning, to ETL.
Storm has been clocked at over 1 million tuples processed per second per node. It’s fast, scalable, and language agnostic. This session provides an architecture overview as well as a real-world discussion of its use and implementation at Enterprise Holdings.
Detailed design for a robust counter as well as design for a completely on-line multi-armed bandit implementation that uses the new Bayesian Bandit algorithm - by Ted Dunning
A talk given by Ted Dunning in the HPTS workshop in Asilomar in 2009. It describes the ideas behind micro-sharding and outlines how Katta can manage micro-shards.
Some builds and spacing are off because this was exported as power point from Keynote.
Some notes about spark streming positioning give the current players: Beam, Flink, Storm et al. Helpful if you have to choose an Streaming engine for your project.
What are algorithms? How can I build a machine learning model? In machine learning, training large models on a massive amount of data usually improves results. Our customers report, however, that training such models and deploying them is either operationally prohibitive or outright impossible for them. At Amazon, we created a collection of machine learning algorithms that scale to any amount of data, including k-means clustering for data segmentation, factorisation machines for recommendations, and time-series forecasting. This talk will discuss those algorithms, understand where and how they can be used, and our design choices.
Bobby Evans and Tom Graves, the engineering leads for Spark and Storm development at Yahoo will talk about how these technologies are used on Yahoo's grids and reasons why to use one or the other.
Bobby Evans is the low latency data processing architect at Yahoo. He is a PMC member on many Apache projects including Storm, Hadoop, Spark, and Tez. His team is responsible for delivering Storm as a service to all of Yahoo and maintaining Spark on Yarn for Yahoo (Although Tom really does most of that work).
Tom Graves a Senior Software Engineer on the Platform team at Yahoo. He is an Apache PMC member on Hadoop, Spark, and Tez. His team is responsible for delivering and maintaining Spark on Yarn for Yahoo.
Real time big data analytics with Storm by Ron Bodkin of Think Big AnalyticsData Con LA
This talk provides an overview of the open source Storm system for processing Big Data in realtime. The talk starts with an overview of the technology, including key components: Nimbus, Zookeeper, Topology, Tuple, Trident. It looks at integration with Hadoop through YARN and recent improvements. The presentation then dives into the complex Big Data architecture in which Storm can be integrated . The result is a compelling stack of technologies including integrated Hadoop clusters, MPP, and NoSQL databases.
After this, we look at example use cases for Storm: real-time advertising statistics, updating a Machine Learned model for content popularity predictions, and financial compliance monitoring.
PHP Backends for Real-Time User Interaction using Apache Storm.DECK36
Engaging users in real-time is the topic of our times. Whether it’s a game, a shop, or a content-network, the aim remains the same: providing a personalized experience. In this workshop we will look under the hood of Apache Storm and lay a firm foundation on how to use it with PHP. By that, you can leverage your existing codebase and PHP expertise for an entirely new world: real-time analytics and business logic working on message streams. During the course of the workshop, we will introduce Apache Storm and take a look at all of its components. We will then skyrocket the applicability of Storm by showing you how to implement their components with PHP. All exercises will be conducted using an example project, the infamous and most exhilarating lolcat kitten game ever conceived: Plan 9 From Outer Kitten. In order to follow the hands-on excercises, you will need a development VM prepared by us with all relevant system components and our project repositories. To make the workshop experience as smooth as possible for all participants, please bring a prepared computer to the workshop, as there will be no time to deal with installation and setup issues. Please download all prerequisites and install them as described: VM, Plan 9 webapp, Plan 9 storm backend, (Tutorial: https://github.com/DECK36/plan9_workshop_tutorial ).
Slides from talk given at the NYC Cassandra Meetup. Discussing how Storm works and how it integrates well with Apache Cassandra.
There is also a segway into a example project that uses Storm and Cassandra to implement a scalable reactive web crawler.
http://github.com/tjake/stormscraper
Streaming data presents new challenges for statistics and machine learning on extremely large data sets. Tools such as Apache Storm, a stream processing framework, can power range of data analytics but lack advanced statistical capabilities. These slides are from the Apache.con talk, which discussed developing streaming algorithms with the flexibility of both Storm and R, a statistical programming language.
At the talk I dicsussed issues of why and how to use Storm and R to develop streaming algorithms; in particular I focused on:
• Streaming algorithms
• Online machine learning algorithms
• Use cases showing how to process hundreds of millions of events a day in (near) real time
See: https://apacheconna2015.sched.org/event/09f5a1cc372860b008bce09e15a034c4#.VUf7wxOUd5o
Storm – Streaming Data Analytics at Scale - StampedeCon 2014StampedeCon
At StampedeCon 2014, Scott Shaw (Hortonworks) and Kit Menke (Enteprise Holdings) presented "Storm – Streaming Data Analytics at Scale"
Storm’s primary purpose is to provide real-time analytics against fast moving data before its stored. The use cases range from fraud detection, machine learning, to ETL.
Storm has been clocked at over 1 million tuples processed per second per node. It’s fast, scalable, and language agnostic. This session provides an architecture overview as well as a real-world discussion of its use and implementation at Enterprise Holdings.
Detailed design for a robust counter as well as design for a completely on-line multi-armed bandit implementation that uses the new Bayesian Bandit algorithm - by Ted Dunning
A talk given by Ted Dunning in the HPTS workshop in Asilomar in 2009. It describes the ideas behind micro-sharding and outlines how Katta can manage micro-shards.
Some builds and spacing are off because this was exported as power point from Keynote.
Challenges & Capabilites in Managing a MapR Cluster by David TuckerMapR Technologies
"If you're using Hadoop in production, how do you manage it? Does the distribution you're using provide any tools to make the job easier? What are the pitfalls? Are there parts of the system that are less robust or that have problems more often? Are you running Hadoop on bare metal, or in a cloud environment, and is one easier than the other?"
MapR Senior Solutions Architect David Tucker speaks about the challenges and capabilites in managing a cluster. This talk was given at the SF Bay Area Large Scale Production Engineering Meetup (Sept 19, 2013).
Nearest neighbor models are conceptually just about the simplest kind of model possible. The problem is that they generally aren’t feasible to apply. Or at least, they weren’t feasible until the advent of Big Data techniques. These slides will describe some of the techniques used in the knn project to reduce thousand-year computations to a few hours. The knn project uses the Mahout math library and Hadoop to speed up these enormous computations to the point that they can be usefully applied to real problems. These same techniques can also be used to do real-time model scoring.
Lecture 11 - KNN and Clustering, a lecture in subject module Statistical & Ma...Maninda Edirisooriya
Supervised ML technique, K-Nearest Neighbor and Unsupervised Clustering techniques are learnt in this lesson. This was one of the lectures of a full course I taught in University of Moratuwa, Sri Lanka on 2023 second half of the year.
Clustering is an unsupervised Machine Learning-based Algorithm that comprises a group of data points into clusters so that the objects belong to the same group. Clustering helps to splits data into several subsets. Each of these subsets contains data similar to each other, and these subsets are called clusters.
A brief introduction to clustering with Scikit learn. In this presentation, we provide an overview with real examples of how to make use and optimize within k-means clustering.
Slides from the presentation "Modern Cryptography" delivered at Deovxx UK 2013. See Parleys.com for the full video https://www.parleys.com/speaker/5148920c0364bc17fc5697a5
How Data-Driven Approaches are Changing Your Data Management Strategies
Introducing data-driven strategies into your business model alters the way your organization manages and provides information to your customers, partners and employees. Gone are the days of “waterfall” implementation strategies from relational data to applications within a data center. Now, data-driven business models require agile implementation of applications based on information from all across an organization–on-premises, cloud, and mobile–and includes information from outside corporate walls from partners, third-party vendors, and customers. Data management strategies need to be ready to meet these challenges or your new and disruptive business models will fail at the most critical time: when your customers want to access it.
ML Workshop 2: Machine Learning Model Comparison & EvaluationMapR Technologies
How Rendezvous Architecture Improves Evaluation in the Real World
In this addition of our machine learning logistics webinar series we build on the ideas of the key requirements for effective management of machine learning logistics presented in the Overview webinar and in Part I Workshop. Here we focus on model-to-model comparison & evaluation, use of decoy models and more. Listen here: http://info.mapr.com/machine-learning-workshop2.html?_ga=2.35695522.324200644.1511891424-416597139.1465233415
Self-Service Data Science for Leveraging ML & AI on All of Your DataMapR Technologies
MapR has launched the MapR Data Science Refinery which leverages a scalable data science notebook with native platform access, superior out-of-the-box security, and access to global event streaming and a multi-model NoSQL database.
Enabling Real-Time Business with Change Data CaptureMapR Technologies
Machine learning (ML) and artificial intelligence (AI) enable intelligent processes that can autonomously make decisions in real-time. The real challenge for effective ML and AI is getting all relevant data to a converged data platform in real-time, where it can be processed using modern technologies and integrated into any downstream systems.
Machine Learning for Chickens, Autonomous Driving and a 3-year-old Who Won’t ...MapR Technologies
Big data technologies are being applied to a wide variety of use cases. We will review tangible examples of machine learning, discuss an autonomous driving project and illustrate the role of MapR in next generation initiatives. More: http://info.mapr.com/WB_Machine-Learning-for-Chickens_Global_DG_17.11.02_RegistrationPage.html
ML Workshop 1: A New Architecture for Machine Learning LogisticsMapR Technologies
Having heard the high-level rationale for the rendezvous architecture in the introduction to this series, we will now dig in deeper to talk about how and why the pieces fit together. In terms of components, we will cover why streams work, why they need to be persistent, performant and pervasive in a microservices design and how they provide isolation between components. From there, we will talk about some of the details of the implementation of a rendezvous architecture including discussion of when the architecture is applicable, key components of message content and how failures and upgrades are handled. We will touch on the monitoring requirements for a rendezvous system but will save the analysis of the recorded data for later. Listen to the webinar on demand: https://mapr.com/resources/webinars/machine-learning-workshop-1/
Machine Learning Success: The Key to Easier Model ManagementMapR Technologies
Join Ellen Friedman, co-author (with Ted Dunning) of a new short O’Reilly book Machine Learning Logistics: Model Management in the Real World, to look at what you can do to have effective model management, including the role of stream-first architecture, containers, a microservices approach and a DataOps style of work. Ellen will provide a basic explanation of a new architecture that not only leverages stream transport but also makes use of canary models and decoy models for accurate model evaluation and for efficient and rapid deployment of new models in production.
Data Warehouse Modernization: Accelerating Time-To-Action MapR Technologies
Data warehouses have been the standard tool for analyzing data created by business operations. In recent years, increasing data volumes, new types of data formats, and emerging analytics technologies such as machine learning have given rise to modern data lakes. Connecting application databases, data warehouses, and data lakes using real-time data pipelines can significantly improve the time to action for business decisions. More: http://info.mapr.com/WB_MapR-StreamSets-Data-Warehouse-Modernization_Global_DG_17.08.16_RegistrationPage.html
Live Tutorial – Streaming Real-Time Events Using Apache APIsMapR Technologies
For this talk we will explore the power of streaming real time events in the context of the IoT and smart cities.
http://info.mapr.com/WB_Streaming-Real-Time-Events_Global_DG_17.08.02_RegistrationPage.html
Bringing Structure, Scalability, and Services to Cloud-Scale StorageMapR Technologies
Deploying storage with a forklift is so 1990s, right? Today’s applications and infrastructure demand systems and services that scale. Customers require performance and capacity to fit the use case and workloads, not the other way around. Architects need multi-temperature, multi-location, highly available, and compliance friendly platforms that grow with the generational shift in data growth and utility.
Churn prediction is big business. It minimizes customer defection by predicting which customers are likely to cancel a service. Though originally used within the telecommunications industry, it has become common practice for banks, ISPs, insurance firms, and other verticals. More: http://info.mapr.com/WB_PredictingChurn_Global_DG_17.06.15_RegistrationPage.html
The prediction process is data-driven and often uses advanced machine learning techniques. In this webinar, we'll look at customer data, do some preliminary analysis, and generate churn prediction models – all with Spark machine learning (ML) and a Zeppelin notebook.
Spark’s ML library goal is to make machine learning scalable and easy. Zeppelin with Spark provides a web-based notebook that enables interactive machine learning and visualization.
In this tutorial, we'll do the following:
Review classification and decision trees
Use Spark DataFrames with Spark ML pipelines
Predict customer churn with Apache Spark ML decision trees
Use Zeppelin to run Spark commands and visualize the results
An Introduction to the MapR Converged Data PlatformMapR Technologies
Listen to the webinar on-demand: http://info.mapr.com/WB_Partner_CDP_Intro_EMEA_DG_17.05.31_RegistrationPage.html
In this 90-minute webinar, we discuss:
- The MapR Converged Data Platform and its components
- Use cases for the Converged Data Platform
- MapR Converged Partner Program
- How to get started with MapR
- Becoming a partner
How to Leverage the Cloud for Business Solutions | Strata Data Conference Lon...MapR Technologies
IT budgets are shrinking, and the move to next-generation technologies is upon us. The cloud is an option for nearly every company, but just because it is an option doesn’t mean it is always the right solution for every problem.
Most cloud providers would prefer that every customer be tightly coupled with their proprietary services and APIs to create lock-in with that cloud provider. The savvy customer will leverage the cloud as infrastructure and stay loosely bound to a cloud provider. This creates an opportunity for the customer to execute a multicloud strategy or even a hybrid on-premises and cloud solution.
Jim Scott explores different use cases that may be best run in the cloud versus on-premises, points out opportunities to optimize cost and operational benefits, and explains how to get the data moved between locations. Along the way, Jim discusses security, backups, event streaming, databases, replication, and snapshots across a variety of use cases that run most businesses today.
Is your organization at the analytics crossroads? Have you made strides collecting and sharing massive amounts of data from electronic health records, insurance claims, and health information exchanges but found these efforts made little impact on efficiency, patient outcomes, or costs?
Changes in how business is done combined with multiple technology drivers make geo-distributed data increasingly important for enterprises. These changes are causing serious disruption across a wide range of industries, including healthcare, manufacturing, automotive, telecommunications, and entertainment. Technical challenges arise with these disruptions, but the good news is there are now innovative solutions to address these problems. http://info.mapr.com/WB_Geo-distributed-Big-Data-and-Analytics_Global_DG_17.05.16_RegistrationPage.html
MapR announced a few new releases in 2017, and we want to go over those exciting new products and features that are available now. We’d like to invite our customers and partners to this webinar in which members of the MapR product team will share details about the latest updates.
3 Benefits of Multi-Temperature Data Management for Data AnalyticsMapR Technologies
SAP® HANA and SAP® IQ are popular platforms for various analytical and transactional use cases. If you’re an SAP customer, you’ve experienced the benefits of deploying these solutions. However, as data volumes grow, you’re likely asking yourself: How do I scale storage to support these applications? How can I have one platform for various applications and use cases?
Cisco & MapR bring 3 Superpowers to SAP HANA DeploymentsMapR Technologies
SAP HANA is an increasingly popular platform for various analytical and transactional use cases with its in-memory architecture. If you’re an SAP customer you’ve experienced the benefits.
However, the underlying storage for SAP HANA is painfully expensive. This slows down your ability to grow your SAP HANA footprint and serve up more applications.
You’re not the only one still loading your data into data warehouses and building marts or cubes out of it. But today’s data requires a much more accessible environment that delivers real-time results. Prepare for this transformation because your data platform and storage choices are about to undergo a re-platforming that happens once in 30 years.
With the MapR Converged Data Platform (CDP) and Cisco Unified Compute System (UCS), you can optimize today’s infrastructure and grow to take advantage of what’s next. Uncover the range of possibilities from re-platforming by intimately understanding your options for density, performance, functionality and more.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
5. Why k-means?
• Clustering allows fast search
– k-nn models allow agile modeling
– lots of data points, 108 typical
– lots of clusters, 104 typical
• Model features
– Distance to nearest centroids
– Poor man’s manifold discovery
6. What is Quality?
• Robust clustering not a goal
– we don’t care if the same clustering is replicated
• Generalization to unseen data critical
– number of points per cluster
– distance distributions
– target function distributions
– model performance stability
• Agreement to “gold standard” is a non-issue
8. The Problem
• Spirals are a classic “counter” example for k-
means
• Classic low dimensional manifold with added
noise
• But clustering still makes modeling work well
11. The Cluster Proximity Features
• Every point can be described by the nearest
cluster
– 4.3 bits per point in this case
– Significant error that can be decreased (to a point)
by increasing number of clusters
• Or by the proximity to the 2 nearest clusters (2
x 4.3 bits + 1 sign bit + 2 proximities)
– Error is negligible
– Unwinds the data into a simple representation
14. The Limiting Case
• Too many clusters lead to over-fitting
• Which we mediate by averaging over several
nearby clusters
• In the limit we get k-nn modeling
– and probably use k-means to speed up search
16. Intuitive Theory
• Traditionally, minimize over all distributions
– optimization is NP-complete
– that isn’t like real data
• Recently, assume well-clusterable data
• Interesting approximation bounds provable
s 2
Dk-1
2
(X) > Dk
2
(X)
1+O(s 2
)
19. Lloyd’s Algorithm
• Part of CS folk-lore
• Developed in the late 50’s for signal quantization, published
in 80’s
initialize k cluster centroids somehow
for each of many iterations:
for each data point:
assign point to nearest cluster
recompute cluster centroids from points assigned to clusters
• Highly variable quality, several restarts recommended
21. Ball k-means
• Provably better for highly clusterable data
• Tries to find initial centroids in each “core” of each real
clusters
• Avoids outliers in centroid computation
initialize centroids randomly with distance maximizing
tendency
for each of a very few iterations:
for each data point:
assign point to nearest cluster
recompute centroids using only points much closer than
closest cluster
22. Still Not a Win
• Ball k-means is nearly guaranteed with k = 2
• Probability of successful seeding drops
exponentially with k
• Alternative strategy has high probability of
success, but takes O(nkd + k3d) time
24. Surrogate Method
• Start with sloppy clustering into κ = k log n
clusters
• Use this sketch as a weighted surrogate for the
data
• Cluster surrogate data using ball k-means
• Results are provably good for highly clusterable
data
• Sloppy clustering is on-line
• Surrogate can be kept in memory
• Ball k-means pass can be done at any time
25. Algorithm Costs
• O(k d log n) per point per iteration for Lloyd’s
algorithm
• Number of iterations not well known
• Iteration > log n reasonable assumption
26. Algorithm Costs
• Surrogate methods
– fast, sloppy single pass clustering with κ = k log n
– fast sloppy search for nearest cluster,
O(d log κ) = O(d (log k + log log n)) per point
– fast, in-memory, high-quality clustering of κ weighted
centroids
O(κ k d + k3 d) = O(k2 d log n + k3 d) for small k, high quality
O(κ d log k) or O(d log κ log k) for larger k, looser quality
– result is k high-quality centroids
• Even the sloppy surrogate may suffice
27. Algorithm Costs
• How much faster for the sketch phase?
– take k = 2000, d = 10, n = 100,000
– k d log n = 2000 x 10 x 26 = 500,000
– d (log k + log log n) = 10(11 + 5) = 170
– 3,000 times faster is a bona fide big deal
28. Pragmatics
• But this requires a fast search internally
• Have to cluster on the fly for sketch
• Have to guarantee sketch quality
• Previous methods had very high complexity
29. How It Works
• For each point
– Find approximately nearest centroid (distance = d)
– If (d > threshold) new centroid
– Else if (u > d/threshold) new cluster
– Else add to nearest centroid
• If centroids > κ ≈ C log N
– Recursively cluster centroids with higher threshold
30. Resulting Surrogate
• Result is large set of centroids
– these provide approximation of original
distribution
– we can cluster centroids to get a close
approximation of clustering original
– or we can just use the result directly
• Either way, we win
32. How Can We Search Faster?
• First rule: don’t do it
– If we can eliminate most candidates, we can do less work
– Projection search and k-means search
• Second rule: don’t do it
– We can convert big floating point math to clever bit-wise
integer math
– Locality sensitive hashing
• Third rule: reduce dimensionality
– Projection search
– Random projection for very high dimension
35. LSH Search
• Each random projection produces independent sign bit
• If two vectors have the same projected sign bits, they
probably point in the same direction (i.e. cos θ ≈ 1)
• Distance in L2 is closely related to cosine
• We can replace (some) vector dot products with long
integer XOR
x - y 2
= x2
- 2(x× y)+ y2
= x2
- 2 x y cosq + y2
40. What About Map-Reduce?
• Map-reduce implementation is nearly trivial
– Compute surrogate on each split
– Total surrogate is union of all partial surrogates
– Do in-memory clustering on total surrogate
• Threaded version shows linear speedup
already
• Map-reduce speedup shows same linear
speedup
41. How Well Does it Work?
• Theoretical guarantees for well clusterable
data
– Shindler, Wong and Meyerson, NIPS, 2011
• Evaluation on synthetic data
– Rough clustering produces correct surrogates
– Ball k-means strategy 1 performance is very good
with large k
42. How Well Does it Work?
• Empirical evaluation on 20 newsgroups
• Alternative algorithms include ball k-means
versus streaming k-means|ball k-means
• Results
Average distance to nearest cluster on held-out data
same or slightly smaller
Median distance to nearest cluster is smaller
> 10x faster (I/O and encoding limited)
44. The Business Case
• Our customer has 100 million cards in
circulation
• Quick and accurate decision-making is key.
– Marketing offers
– Fraud prevention
45. Opportunity
• Demand of modeling is increasing rapidly
• So they are testing something simpler and
more agile
• Like k-nearest neighbor
46. What’s that?
• Find the k nearest training examples – lookalike
customers
• This is easy … but hard
– easy because it is so conceptually simple and you don’t
have knobs to turn or models to build
– hard because of the stunning amount of math
– also hard because we need top 50,000 results
• Initial rapid prototype was massively too slow
– 3K queries x 200K examples takes hours
– needed 20M x 25M in the same time
48. Required Scale and Speed and
Accuracy
• Want 20 million queries against 25 million
references in 10,000 s
• Should be able to search > 100 million
references
• Should be linearly and horizontally scalable
• Must have >50% overlap against reference
search
49. How Hard is That?
• 20 M x 25 M x 100 Flop = 50 P Flop
• 1 CPU = 5 Gflops
• We need 10 M CPU seconds => 10,000 CPU’s
• Real-world efficiency losses may increase that by
10x
• Not good!
50. K-means Search
• First do clustering with lots (thousands) of clusters
• Then search nearest clusters to find nearest points
• We win if we find >50% overlap with “true” answer
• We lose if we can’t cluster super-fast
– more on this later
53. Some Details
• Clumpy data works better
– Real data is clumpy
• Speedups of 100-200x seem practical with
50% overlap
– Projection search and LSH give additional 100x
• More experiments needed
54. Summary
• Nearest neighbor algorithms can be blazing
fast
• But you need blazing fast clustering
– Which we now have
55. Contact Me!
• We’re hiring at MapR in US and Europe
• MapR software available for research use
• Come get the slides at
http://www.mapr.com/company/events/acmsf-2-25-13
• Get the code as part of Mahout trunk
• Contact me at tdunning@maprtech.com or @ted_dunning
Editor's Notes
The basic idea here is that I have colored slides to be presented by you in blue. You should substitute and reword those slides as you like. In a few places, I imagined that we would have fast back and forth as in the introduction or final slide where we can each say we are hiring in turn.The overall thrust of the presentation is for you to make these points:Amex does lots of modelingit is expensivehaving a way to quickly test models and new variables would be awesomeso we worked on a new project with MapRMy part will say the following:Knn basic pictorial motivation (could move to you if you like)describe knn quality metric of overlapshow how bad metric breaks knn (optional)quick description of LSH and projection searchpicture of why k-means search is coolmotivate k-means speed as tool for k-means searchdescribe single pass k-means algorithmdescribe basic data structuresshow parallel speedupOur summary should state that we have achievedsuper-fast k-means clusteringinitial version of super-fast knn search with good overlap
The sub-bullets are just for reference and should be deleted later
This slide is red to indicate missing data
The idea here is to guess what color a new dot should be by looking at the points within the circle. The first should obviously be purple. The second cyan. The third is uncertain, but probably isn’t green or cyan and probably is a bit more likely to be red than purple.