Multi-model recommendation engines use multiple kinds of behavior as input and can be implemented using standard search engine technology. I show how and why starting with basic recommendations all the way through full multi-modal systems.
Recent work in recommendations allows some really amazing simplicity of implementation while extending the inputs handled to multiple kinds of interactions against items different from the ones being recommended.
This is the position talk that I gave at CIKM. Included are 4 algorithms that I feel don't get much academic attention, but which are very important industrially. It isn't necessarily true that these algorithms *should* get academic attention, but I do feel that it is true that they are quite important pragmatically speaking.
Building multi-modal recommendation engines using search enginesTed Dunning
This is my strata NY talk about how to build recommendation engines using common items. In particular, I show how multi-modal recommendations can be built using the same framework.
Metabase is the free, easy, open source way for everyone in your company to ask questions and learn from data. Easily filter and group data to find what you are looking for. Explore connections between your data. Visualize results.
Using Mahout and a Search Engine for RecommendationTed Dunning
I presented this talk at the Open World Forum in Paris in 2013. The ideas here are that you can do basic recommendations and extended forms of recommendation such as intelligent search or cross recommendation or multi-modal recommendation using Mahout's cooccurrence analysis together with a search engine.
Recent work in recommendations allows some really amazing simplicity of implementation while extending the inputs handled to multiple kinds of interactions against items different from the ones being recommended.
This is the position talk that I gave at CIKM. Included are 4 algorithms that I feel don't get much academic attention, but which are very important industrially. It isn't necessarily true that these algorithms *should* get academic attention, but I do feel that it is true that they are quite important pragmatically speaking.
Building multi-modal recommendation engines using search enginesTed Dunning
This is my strata NY talk about how to build recommendation engines using common items. In particular, I show how multi-modal recommendations can be built using the same framework.
Metabase is the free, easy, open source way for everyone in your company to ask questions and learn from data. Easily filter and group data to find what you are looking for. Explore connections between your data. Visualize results.
Using Mahout and a Search Engine for RecommendationTed Dunning
I presented this talk at the Open World Forum in Paris in 2013. The ideas here are that you can do basic recommendations and extended forms of recommendation such as intelligent search or cross recommendation or multi-modal recommendation using Mahout's cooccurrence analysis together with a search engine.
Recent work in recommendations allows some really amazing simplicity of implementation while extending the inputs handled to multiple kinds of interactions against items different from the ones being recommended.
Crowd sourced intelligence built into search over hadooplucenerevolution
Presented by Ted Dunning, Chief Application Architect, MapR
& Grant Ingersoll, Chief Technology Officer, LucidWorks
Search has quickly evolved from being an extension of the data warehouse to being run as a real time decision processing system. Search is increasingly being used to gather intelligence on multi-structured data leveraging distributed platforms such as Hadoop in the background. This session will provide details on how search engines can be abused to use not text, but mathematically derived tokens to build models that implement reflected intelligence. In such a system, intelligent or trend-setting behavior of some users is reflected back at other users. More importantly, the mathematics of evaluating these models can be hidden in a conventional search engine like SolR, making the system easy to build and deploy. The session will describe how to integrate Apache Solr/Lucene with Hadoop. Then we will show how crowd-sourced search behavior can be looped back into analysis and how constantly self-correcting models can be created and deployed. Finally, we will show how these models can respond with intelligent behavior in realtime.
When recommendation is described in mathematical terms as a matrix equation, a striking symmetry in the form of the equation becomes apparent.
Exploiting this symmetry allows us to build search engines that don't need meta-data and self-organizing web-sites.
The unification of big and little data processing onto a single platform is an important requirement for Hadoop. How can this be achieved? Ted Dunning explains what is needed for three important use cases.
What is the future of Hadoop?
What is the new future of Hadoop?
How is that different from the old one?
Here is how Ted Dunning answered these questions at the winter Hadoop Conference of Japan 2013.
These are the slides from my talk at FAR Con in Minneapolis recently. The topics are the implications of buried treasure hoards on data security, horror stories and new, simpler and provably secure methods for public data disclosure.
DFW Big Data talk on Mahout RecommendersTed Dunning
This talk focussed on how to build recommenders using new technology and capabilities from Mahout. The key here is that recommenders can be built much more easily than you might expect.
This talk focuses on how larger data sets are not only enabling advanced techniques, but also increasing the number of problems within reach of relatively simple techniques, that is "cheap learning".
Complement Deep Learning with Cheap Learning: Recent results of deep learning on hard problems has set the data world all a titter and made deep learning the fashion of the time.
But it is very important to remember that as data expands, the learning problems that are encountered are often nearly green field problems and it is often possible to solve these problems using remarkably simple techniques. Indeed, on many problems these simple techniques will give results as good as more complex ones, not because they are profound, but because many problems become simpler at scale.
That said, it isn’t always obvious how to do this. I will describe some of these techniques and show how they can be applied in practice.
Achieving Business Value by Fusing Hadoop and Corporate DataInside Analysis
The Briefing Room with Richard Hackathorn and Teradata
Live Webcast March 25, 2015
Watch the Archive: https://bloorgroup.webex.com/bloorgroup/onstage/g.php?MTID=e7254708146d056339a0974f097f569b2
Hadoop data lakes are emerging as peers to corporate data warehouses. However, successful analytic solutions require a fusion of all relevant data, big and small, which has proven challenging for many companies. By allowing business analysts to quickly access data wherever it rests, success factors shift to focus on three key aspects: 1) business objectives, 2) organizational workflow, and 3) data placement.
Register for this Special Edition of The Briefing Room to hear veteran Analyst Richard Hackathorn as he provides details from his recent research report focused on success stories using Teradata QueryGrid. Examples of use cases described will include:
Joining sensor data in Hadoop with data warehouse labor schedules in seconds
How bridging corporate cultures and systems creates new business opportunities
The 360 view of customer journeys using weblogs in Hadoop via BI tools
How can you put the data where you want and query it however you want
Virtualizing Hadoop data with Teradata QueryGrid
Visit InsideAnalysis.com for more information.
An introductory but highly practical talk on starting a Data Science career and life. It touches upon all the main aspects of the path towards becoming a Data scientist, also seen through a personal development perspective. Moreover, we talk about the role that a data scientist ultimately fulfills - as an individual or as a team - in the technology innovation life cycle and the product life-cycle.
Cognitive computing with big data, high tech and low tech approachesTed Dunning
I explain some very approachable methods for analyzing big data via a detour through clipper ships and the 19th century open source scene.
Note that I mixed up the route of the Flying Cloud record in this talk. The Flying Cloud's record was actually from New York to San Francisco and was even more impressive than what I said. The usual time had been about 180 days. With Maury's charts, the time was reduced to about 135 days. The Flying Cloud's time was 89 days.
Thanks to Chen Kung for noticing my error.
We introduce the idea that metadata, including project information, data labels, data characteristics and indications of valuable use, can be propagated through a data processing lineage graph. Further, finding examples of significant cooccurrence of propagated and original metadata gives us the basis of an interesting kind of search engine gives interesting recommendations of data given a problem statement even in a near cold-start situation.
Recent work in recommendations allows some really amazing simplicity of implementation while extending the inputs handled to multiple kinds of interactions against items different from the ones being recommended.
Crowd sourced intelligence built into search over hadooplucenerevolution
Presented by Ted Dunning, Chief Application Architect, MapR
& Grant Ingersoll, Chief Technology Officer, LucidWorks
Search has quickly evolved from being an extension of the data warehouse to being run as a real time decision processing system. Search is increasingly being used to gather intelligence on multi-structured data leveraging distributed platforms such as Hadoop in the background. This session will provide details on how search engines can be abused to use not text, but mathematically derived tokens to build models that implement reflected intelligence. In such a system, intelligent or trend-setting behavior of some users is reflected back at other users. More importantly, the mathematics of evaluating these models can be hidden in a conventional search engine like SolR, making the system easy to build and deploy. The session will describe how to integrate Apache Solr/Lucene with Hadoop. Then we will show how crowd-sourced search behavior can be looped back into analysis and how constantly self-correcting models can be created and deployed. Finally, we will show how these models can respond with intelligent behavior in realtime.
When recommendation is described in mathematical terms as a matrix equation, a striking symmetry in the form of the equation becomes apparent.
Exploiting this symmetry allows us to build search engines that don't need meta-data and self-organizing web-sites.
The unification of big and little data processing onto a single platform is an important requirement for Hadoop. How can this be achieved? Ted Dunning explains what is needed for three important use cases.
What is the future of Hadoop?
What is the new future of Hadoop?
How is that different from the old one?
Here is how Ted Dunning answered these questions at the winter Hadoop Conference of Japan 2013.
These are the slides from my talk at FAR Con in Minneapolis recently. The topics are the implications of buried treasure hoards on data security, horror stories and new, simpler and provably secure methods for public data disclosure.
DFW Big Data talk on Mahout RecommendersTed Dunning
This talk focussed on how to build recommenders using new technology and capabilities from Mahout. The key here is that recommenders can be built much more easily than you might expect.
This talk focuses on how larger data sets are not only enabling advanced techniques, but also increasing the number of problems within reach of relatively simple techniques, that is "cheap learning".
Complement Deep Learning with Cheap Learning: Recent results of deep learning on hard problems has set the data world all a titter and made deep learning the fashion of the time.
But it is very important to remember that as data expands, the learning problems that are encountered are often nearly green field problems and it is often possible to solve these problems using remarkably simple techniques. Indeed, on many problems these simple techniques will give results as good as more complex ones, not because they are profound, but because many problems become simpler at scale.
That said, it isn’t always obvious how to do this. I will describe some of these techniques and show how they can be applied in practice.
Achieving Business Value by Fusing Hadoop and Corporate DataInside Analysis
The Briefing Room with Richard Hackathorn and Teradata
Live Webcast March 25, 2015
Watch the Archive: https://bloorgroup.webex.com/bloorgroup/onstage/g.php?MTID=e7254708146d056339a0974f097f569b2
Hadoop data lakes are emerging as peers to corporate data warehouses. However, successful analytic solutions require a fusion of all relevant data, big and small, which has proven challenging for many companies. By allowing business analysts to quickly access data wherever it rests, success factors shift to focus on three key aspects: 1) business objectives, 2) organizational workflow, and 3) data placement.
Register for this Special Edition of The Briefing Room to hear veteran Analyst Richard Hackathorn as he provides details from his recent research report focused on success stories using Teradata QueryGrid. Examples of use cases described will include:
Joining sensor data in Hadoop with data warehouse labor schedules in seconds
How bridging corporate cultures and systems creates new business opportunities
The 360 view of customer journeys using weblogs in Hadoop via BI tools
How can you put the data where you want and query it however you want
Virtualizing Hadoop data with Teradata QueryGrid
Visit InsideAnalysis.com for more information.
An introductory but highly practical talk on starting a Data Science career and life. It touches upon all the main aspects of the path towards becoming a Data scientist, also seen through a personal development perspective. Moreover, we talk about the role that a data scientist ultimately fulfills - as an individual or as a team - in the technology innovation life cycle and the product life-cycle.
Cognitive computing with big data, high tech and low tech approachesTed Dunning
I explain some very approachable methods for analyzing big data via a detour through clipper ships and the 19th century open source scene.
Note that I mixed up the route of the Flying Cloud record in this talk. The Flying Cloud's record was actually from New York to San Francisco and was even more impressive than what I said. The usual time had been about 180 days. With Maury's charts, the time was reduced to about 135 days. The Flying Cloud's time was 89 days.
Thanks to Chen Kung for noticing my error.
Similar to Buzz words-dunning-multi-modal-recommendation (20)
We introduce the idea that metadata, including project information, data labels, data characteristics and indications of valuable use, can be propagated through a data processing lineage graph. Further, finding examples of significant cooccurrence of propagated and original metadata gives us the basis of an interesting kind of search engine gives interesting recommendations of data given a problem statement even in a near cold-start situation.
The folk wisdom has always been that when running stateful applications inside containers, the only viable choice is to externalize the state so that the containers themselves are stateless or nearly so. Keeping large amounts of state inside containers is possible, but it’s considered a problem because stateful containers generally can’t preserve that state across restarts.
In practice, this complicates the management of large-scale Kubernetes-based infrastructure because these high-performance storage systems require separate management. In terms of overall system management, it would be ideal if we could run a software-defined storage system directly in containers managed by Kubernetes, but that has been hampered by lack of direct device access and difficult questions about what happens to the state on container restarts.
Ted Dunning describes recent developments that make it possible for Kubernetes to manage both compute and storage tiers in the same cluster. Container restarts can be handled gracefully without loss of data or a requirement to rebuild storage structures and access to storage from compute containers is extremely fast. In some environments, it’s even possible to implement elastic storage frameworks that can fold data onto just a few containers during quiescent periods or explode it in just a few seconds across a large number of machines when higher speed access is required.
The benefits of systems like this extend beyond management simplicity, because applications can be more Agile precisely because the storage layer is more stable and can be uniformly accessed from any container host. Even better, it makes it a snap to configure and deploy a full-scale compute and storage infrastructure.
Ellen Friedman and I spoke at the ACM meetup about how stream-first architecture can have a big impact and how the logistics of machine learning is a great example of that impact.
This is my half of the presentation.
Tensor Abuse - how to reuse machine learning frameworksTed Dunning
Tensors are a very useful tool for mathematical programming. Moreover, the optimization frameworks that are part of most machine learning frameworks have some very cool uses outside of the normal machine learning kinds of tasks.
The logistics of machine learning typically take waaay more effort than the machine learning itself. Moreover, machine learning systems aren't like normal software projects so continuous integration takes on new meaning.
You know that a single number isn't a good summary of a measurement. T-digest and other non-uniform histograms can make it easy to keep track of an entire distribution and can be combined in OLAP queries.
The latest t-digest is faster, more accurate and has hard bounds on size.
This talk shows practical methods for find changes in a variety of kinds of data as well as giving real-world examples from finance, telecom, systems monitoring and natural language processing.
This was one of the talks that I gave at the Strata San Jose conference. I migrated my topic a bit, but here is the original abstract:
Application developers and architects today are interested in making their applications as real-time as possible. To make an application respond to events as they happen, developers need a reliable way to move data as it is generated across different systems, one event at a time. In other words, these applications need messaging.
Messaging solutions have existed for a long time. However, when compared to legacy systems, newer solutions like Apache Kafka offer higher performance, more scalability, and better integration with the Hadoop ecosystem. Kafka and similar systems are based on drastically different assumptions than legacy systems and have vastly different architectures. But do these benefits outweigh any tradeoffs in functionality? Ted Dunning dives into the architectural details and tradeoffs of both legacy and new messaging solutions to find the ideal messaging system for Hadoop.
Topics include:
* Queues versus logs
* Security issues like authentication, authorization, and encryption
* Scalability and performance
* Handling applications that span multiple data centers
* Multitenancy considerations
* APIs, integration points, and more
Real-time Puppies and Ponies - Evolving Indicator Recommendations in Real-timeTed Dunning
This talk describes how indicator-based recommendations can be evolved in real time. Normally, indicator-based recommendations use a large off-line computation to understand the general structure of items to be recommended and then make recommendations in real-time to users based on a comparison of their recent history versus the large-scale product of the off-line computation.
In this talk, I show how the same components of the off-line computation that guarantee linear scalability in a batch setting also give strict real-time bounds on the cost of a practical real-time implementation of the indicator computation.
How the Internet of Things is Turning the Internet Upside DownTed Dunning
This is a wide-ranging talk that goes into how the internet is architected, how that architecture is changing as a result of internet of things, how the internet of things worked in the 19th century big data, open-source community and how to build time-series databases to make this all possible.
Really.
Apache Kylin - OLAP Cubes for SQL on HadoopTed Dunning
Apache Kylin (incubating) is a new project to bring OLAP cubes to Hadoop. I walk through the project and describe how it works and how users see the project.
Many statistics are impossible to compute precisely on streaming data. There are some very clever algorithms, however, which allow us to compute very good approximations of these values efficiently in terms of CPU and memory.
Anomaly Detection - New York Machine LearningTed Dunning
Anomaly detection is the art of finding what you don't know how to ask for. In this talk, I walk through the why and how of building probabilistic models for a variety of problems including continuous signals and web traffic. This talk blends theory and practice in a highly approachable way.
Apache Mahout is changing radically. Here is a report on what is coming, notably including an R like domain specific language that can use multiple computational engines such as Spark.
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
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/
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.
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/
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
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
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
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.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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.
