BDW17 London - Abed Ajraou - First Utility - Putting Data Science in your Bus...Big Data Week
Data Science is now well established in our businesses, and everyone considers data as a key asset and critical for our competitiveness.
However, Data Science is not easy to manage, very often projects failed and the investment made is not seeing as profitable.
The aim of this talk is to share the knowledge in different areas:
* avoid classical mistakes in Data Science
* use the right Big Data technology
* apply the right methodology
* make the Data Science team more efficient
Data Quality Analytics: Understanding what is in your data, before using itDomino Data Lab
Analytics and data science are ever growing fields, as business decision makers continue to use data to drive decisions. The pinnacle of these fields are the models and their accuracy/fit,; what about the data? Is your data clean, and how do you know that? Our discussion will focus on best practices for data preprocessing for analytic uses. Beginning with essential distributional checks of a dataset to a propose method for automated data validation process during ETL for transactional data.
What you till learn:
GOALS - What is the bar for data science teams
PITFALLS - What are common data science struggles
DIAGNOSES - Why so many of our efforts fail to deliver value
RECOMMENDATIONS - How to address these struggles with best practices
Presented by Mac Steele
Director of Product at Domino Data Lab
Most of analytics modeling work today focuses on the production of single-purpose "artisanal" models for predictions. This approach to analytics is fragile with respect to model consistency, reorganization, and resource availability. This talk will argue that instead the focus of analytics modeling should be toward the production of analytics interchangeable parts, which can be combined in creative ways to produce a wide variety of analytics results. This "nuts and bolts" approach allows analytics groups to produce results in an agile way where the time between ask and answer is determined by the right combination of analytics, rather than the modeling.
Supporting innovation in insurance with randomized experimentationDomino Data Lab
Recent technological advances, a dynamic competitive landscape, and an evolving regulatory environment have led to a period of rapid innovation for many insurance providers. Here, we’ll explore how data scientists may use randomized experiments to rigorously assess the causal impact of innovations on business outcomes. Particular emphasis will be placed on experimentation in “offline” channels, with some of the challenges and mitigation strategies highlighted.
BDW17 London - Abed Ajraou - First Utility - Putting Data Science in your Bus...Big Data Week
Data Science is now well established in our businesses, and everyone considers data as a key asset and critical for our competitiveness.
However, Data Science is not easy to manage, very often projects failed and the investment made is not seeing as profitable.
The aim of this talk is to share the knowledge in different areas:
* avoid classical mistakes in Data Science
* use the right Big Data technology
* apply the right methodology
* make the Data Science team more efficient
Data Quality Analytics: Understanding what is in your data, before using itDomino Data Lab
Analytics and data science are ever growing fields, as business decision makers continue to use data to drive decisions. The pinnacle of these fields are the models and their accuracy/fit,; what about the data? Is your data clean, and how do you know that? Our discussion will focus on best practices for data preprocessing for analytic uses. Beginning with essential distributional checks of a dataset to a propose method for automated data validation process during ETL for transactional data.
What you till learn:
GOALS - What is the bar for data science teams
PITFALLS - What are common data science struggles
DIAGNOSES - Why so many of our efforts fail to deliver value
RECOMMENDATIONS - How to address these struggles with best practices
Presented by Mac Steele
Director of Product at Domino Data Lab
Most of analytics modeling work today focuses on the production of single-purpose "artisanal" models for predictions. This approach to analytics is fragile with respect to model consistency, reorganization, and resource availability. This talk will argue that instead the focus of analytics modeling should be toward the production of analytics interchangeable parts, which can be combined in creative ways to produce a wide variety of analytics results. This "nuts and bolts" approach allows analytics groups to produce results in an agile way where the time between ask and answer is determined by the right combination of analytics, rather than the modeling.
Supporting innovation in insurance with randomized experimentationDomino Data Lab
Recent technological advances, a dynamic competitive landscape, and an evolving regulatory environment have led to a period of rapid innovation for many insurance providers. Here, we’ll explore how data scientists may use randomized experiments to rigorously assess the causal impact of innovations on business outcomes. Particular emphasis will be placed on experimentation in “offline” channels, with some of the challenges and mitigation strategies highlighted.
Slides for the presentation given at the Data Science Scotland Meetup (https://www.meetup.com/Scotland-Data-Science-Technology-Meetup/events/256269263/).
This talk aimed to give some general advice, tips, and tricks about how to run a successful data science project.
Hosted by:
Incremental Group - https://www.linkedin.com/company/incremental-group/
MBN Solutions - https://www.linkedin.com/company/mbn-recruitment-solutions/
The Datalab - https://www.linkedin.com/company/the-data-lab-innovation-centre/
Agile Analytics: The Secret to Test, Improve, Fail & Succeed Quickly.Venveo
We all want to get our ideas to market quickly and see results as fast as possible, but are we losing valuable insights in the process? This talk is aimed at illustrating how companies are changing their approach to marketing and innovation in order to be better informed about the decisions they make and be more customer-focused in the process. Find out how your organization can succeed more quickly by finding the right kinds of data and innovating in the process.
Decision Engineering is a novel and innovative way of delivering analytics for business decision makers. You don't just get analytics or insights, you get better decisions through Decision Engineering
"Making Data Actionable" by Budiman Rusly (KMK Online)Tech in Asia ID
***
This slide was shared at Tech in Asia Product Development Conference 2017 (PDC'17) on 9-10 August 2017.
Get more insightful updates from TIA by subscribing techin.asia/updateselalu
This presentation is contains slides explaining the basics of big data and predictive analytics.
It also shows how predictive analytics can be used by non programmers by off-the-shelf tools such as RapidMiner, Excel, etc.
The tool for the hands on/demo session in this presentation was RapidMiner 5.1 Community edition
Big Data Agile Analytics by Ken Collier - Director Agile Analytics, Thoughtwo...Thoughtworks
We are in the midst of an exciting time. There is an explosion of very interesting data, and emergence of powerful new technologies for harnessing data, and devices that enable humans to receive tremendous benefits from it. What is required are innovative processes that enable the creation and delivery of value from all of that data. More often than not, it is the predictive (what will happen?) and prescriptive (how to make it happen!) analytics that produces this value, not the raw data itself.
Agile software teams are continuously involved in projects that involve rich, complex, and messy data. Often this data represents innovative analytics opportunities. Being analytics-aware gives these teams the opportunity to collaborate with stakeholders to innovate by creating additional value from the data. This session is aimed at making Agile software teams more analytics-aware so that they will recognize these innovation opportunities.
The trouble with conventional analytics (like conventional software development) is that it involves long, phased, sequential steps that take too long and fail to deliver actionable results. This talk will examine the convergence of the following elements of an exciting emerging field called Agile Analytics:
•sophisticated analytics techniques, plus
•lean learning principles, plus
•agile delivery methods, plus
•so-called "big data" technologies
Learn:
•The analytical modeling process and techniques
•How analytical models are deployed using modern technologies
•The complexities of data discovery, harvesting, and preparation
•How to apply agile techniques to shorten the analytics development cycle
•How to apply lean learning principles to develop actionable and valuable analytics
•How to apply continuous delivery techniques to operationalize analytical models
Architecting a Data Platform For Enterprise Use (Strata NY 2018)mark madsen
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT.
Long:
The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure.
The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture.
Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions.
Topics covered:
* A brief history of data infrastructure and past design assumptions
* Categories of data and data use in organizations
* Data architecture
* Functional architecture
* Technology planning assumptions and guidance
Slides for the presentation given at the Data Science Scotland Meetup (https://www.meetup.com/Scotland-Data-Science-Technology-Meetup/events/256269263/).
This talk aimed to give some general advice, tips, and tricks about how to run a successful data science project.
Hosted by:
Incremental Group - https://www.linkedin.com/company/incremental-group/
MBN Solutions - https://www.linkedin.com/company/mbn-recruitment-solutions/
The Datalab - https://www.linkedin.com/company/the-data-lab-innovation-centre/
Agile Analytics: The Secret to Test, Improve, Fail & Succeed Quickly.Venveo
We all want to get our ideas to market quickly and see results as fast as possible, but are we losing valuable insights in the process? This talk is aimed at illustrating how companies are changing their approach to marketing and innovation in order to be better informed about the decisions they make and be more customer-focused in the process. Find out how your organization can succeed more quickly by finding the right kinds of data and innovating in the process.
Decision Engineering is a novel and innovative way of delivering analytics for business decision makers. You don't just get analytics or insights, you get better decisions through Decision Engineering
"Making Data Actionable" by Budiman Rusly (KMK Online)Tech in Asia ID
***
This slide was shared at Tech in Asia Product Development Conference 2017 (PDC'17) on 9-10 August 2017.
Get more insightful updates from TIA by subscribing techin.asia/updateselalu
This presentation is contains slides explaining the basics of big data and predictive analytics.
It also shows how predictive analytics can be used by non programmers by off-the-shelf tools such as RapidMiner, Excel, etc.
The tool for the hands on/demo session in this presentation was RapidMiner 5.1 Community edition
Big Data Agile Analytics by Ken Collier - Director Agile Analytics, Thoughtwo...Thoughtworks
We are in the midst of an exciting time. There is an explosion of very interesting data, and emergence of powerful new technologies for harnessing data, and devices that enable humans to receive tremendous benefits from it. What is required are innovative processes that enable the creation and delivery of value from all of that data. More often than not, it is the predictive (what will happen?) and prescriptive (how to make it happen!) analytics that produces this value, not the raw data itself.
Agile software teams are continuously involved in projects that involve rich, complex, and messy data. Often this data represents innovative analytics opportunities. Being analytics-aware gives these teams the opportunity to collaborate with stakeholders to innovate by creating additional value from the data. This session is aimed at making Agile software teams more analytics-aware so that they will recognize these innovation opportunities.
The trouble with conventional analytics (like conventional software development) is that it involves long, phased, sequential steps that take too long and fail to deliver actionable results. This talk will examine the convergence of the following elements of an exciting emerging field called Agile Analytics:
•sophisticated analytics techniques, plus
•lean learning principles, plus
•agile delivery methods, plus
•so-called "big data" technologies
Learn:
•The analytical modeling process and techniques
•How analytical models are deployed using modern technologies
•The complexities of data discovery, harvesting, and preparation
•How to apply agile techniques to shorten the analytics development cycle
•How to apply lean learning principles to develop actionable and valuable analytics
•How to apply continuous delivery techniques to operationalize analytical models
Architecting a Data Platform For Enterprise Use (Strata NY 2018)mark madsen
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT.
Long:
The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure.
The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture.
Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions.
Topics covered:
* A brief history of data infrastructure and past design assumptions
* Categories of data and data use in organizations
* Data architecture
* Functional architecture
* Technology planning assumptions and guidance
A practical guide for startups to drive growth and innovation.
Denver Startup Week Product Track presentation by Argie Angeleas, Taylor Names, Matt Reynolds
DataOps - Big Data and AI World London - March 2020 - Harvinder AtwalHarvinder Atwal
Title
DataOps, the secret weapon for delivering AI, data science, and business intelligence value at speed.
Synopsis
● According to recent research, just 7.3% of organisations say the state of their data and analytics is excellent, and only 22% of companies are currently seeing a significant return from data science expenditure.
● Poor returns on data & analytics investment are often the result of applying 20th-century thinking to 21st-century challenges and opportunities.
● Modern data science and analytics require secure, efficient processes to turn raw data from multiple sources and in numerous formats into useful inputs to a data product.
● Developing, orchestrating and iterating modern data pipelines is an extremely complex process requiring multiple technologies and skills.
● Other domains have to successfully overcome the challenge of delivering high-quality products at speed in complex environments. DataOps applies proven agile principles, lean thinking and DevOps practices to the development of data products.
● A DataOps approach aligns data producers, analytical data consumers, processes and technology with the rest of the organisation and its goals.
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
Webinar: AI as a Shared Service by Salesforce Senior Director of ProductProduct School
Main Takeaways:
-AI is just a mean to an end, the end goal need to be extremely clear
-Prioritize project where you know you have the data - data access can be the most challenging piece of an AI project
-Build your solution for a use case but find ways to make it a shared service
According to recent research report by Wall Street Journal, AI project failure rates near 50%, more than 53% terminates at proof of concept level and does not make it to production. Gartner report says that nearly 80% of the analytics projects are not delivering any business value. That means for every 10 projects, only 2 projects are useful to the organization. Let us pause here a moment, rather than looking at what makes AI projects to fail, let’s look at the challenges involved in AI projects and find a solution to overcome these challenges.
AI projects are different from traditional software projects. Typical software projects, as shown in Figure 1, consist of well-defined software requirements, high level design, coding, unit testing, system testing, and deployment along with beta testing or field testing. Now, organizations are adopting Agile process instead of traditional V or waterfall model, but still steps mentioned are valid.
However, AI and Machine Learning projects’ methodology is different from the above. Our experience working on many AI/ML projects has given us insights on some of the challenges of executing AI projects. Also, we are in regular touch with senior executives and thought leaders from different industries who understand the success formula. The following discussion is based on our practical experience and knowledge gained in the field.
Successful execution of AI projects depends on the following factors:
1. Clearly aligned Business Expectations
2. Clarity on Terminologies
3. Meeting Data Requirements
4. Tools and Technology
5. Right Resources
6. Understanding Output Results
7. Project Planning and the Process
AI as a Shared Service by Salesforce Senior Director of ProductProduct School
Main Takeaways:
-AI is just a mean to an end, the end goal need to be extremely clear
-Prioritize project where you know you have the data - data access can be the most challenging piece of an AI project
-Build your solution for a use case but find ways to make it a shared service
[DSC Europe 22] The Making of a Data Organization - Denys HolovatyiDataScienceConferenc1
Data teams often struggle to deliver value. KPIs, data pipelines, or ML driven predictions aren't inherently useful - unless the data team enables the business to use them. Having worked on 37 data projects over the past 5 years, with total client revenue clocking at about $350B, I started noticing simple success factors - and summarized those in the Operating Model Canvas & the Value Delivery Process. With those, I branched out into what I call data organization consulting and help clients build their data teams for success, the one you see not only on paper but also in your P&L. In this talk, I'll share some insight with you.
How to classify documents automatically using NLPSkyl.ai
About the webinar
Documents come in different shapes and sizes - From technical documents, customer support chat, emails, reviews to news articles - all of them contain information that is valuable to the business.
Managing these large volume data documents in a traditional manual way has been a complex and time-consuming task that requires enormous human efforts.
In this webinar, we will discuss how Machine learning can be used to identify and automatically label news articles into categories like business, politics, music, etc. This can be applied in another context like categorizing emails, reviews, and processing text documents, etc.
What you will learn
- How businesses are leveraging document classification to their advantage
- Best practice to automate machine learning models in hours not months
- Demo: Classify news articles into the right category using convolution neural network
It’s Not About Big Data – It’s About Big Insights - SAP Webinar - 20 Aug 201...Edgar Alejandro Villegas
Presentation slides of:
It’s Not About Big Data – It’s About Big Insights - SAP Webinar - 20 Aug 2013 - PDF
Scott Mackenzie - Sr. Director, Platform & Analytics CoE
Michael Golzc - CIO for SAP Americas
Ken Demma - VP, Insight Driven Marketing
20 Aug 2013 - Webcast - http://goo.gl/T74WAL
The Black Box: Interpretability, Reproducibility, and Data Managementmark madsen
The growing complexity of data science leads to black box solutions that few people in an organization understand. You often hear about the difficulty of interpretability—explaining how an analytic model works—and that you need it to deploy models. But people use many black boxes without understanding them…if they’re reliable. It’s when the black box becomes unreliable that people lose trust.
Mistrust is more likely to be created by the lack of reliability, and the lack of reliability is often the result of misunderstanding essential elements of analytics infrastructure and practice. The concept of reproducibility—the ability to get the same results given the same information—extends your view to include the environment and the data used to build and execute models.
Mark Madsen examines reproducibility and the areas that underlie production analytics and explores the most frequently ignored and yet most essential capability, data management. The industry needs to consider its practices so that systems are more transparent and reliable, improving trust and increasing the likelihood that your analytic solutions will succeed.
This talk will treat the black boxed of ML the way management perceives them, as black boxes.
There is much work on explainable models, interpretability, etc. that are important to the task of reproducibility. Much of that is relevant to the practitioner, but the practitioner can become too focused on the part they are most familiar with and focused on. Reproducing the results needs more.
Operationalizing Machine Learning in the Enterprisemark madsen
TDWI Munich 2019
What does it take to operationalize machine learning and AI in an enterprise setting?
Machine learning in an enterprise setting is difficult, but it seems easy. All you need is some smart people, some tools, and some data. It’s a long way from the environment needed to build ML applications to the environment to run them in an enterprise.
Most of what we know about production ML and AI come from the world of web and digital startups and consumer services, where ML is a core part of the services they provide. These companies have fewer constraints than most enterprises do.
This session describes the nature of ML and AI applications and the overall environment they operate in, explains some important concepts about production operations, and offers some observations and advice for anyone trying to build and deploy such systems.
Analytic next gen usecases - presented for ISB, HyderabadSandeep akinapelli
How the big data platforms are empowering to 1) build market mix modeling and digital attribution and help make marketing budget decision 2) financial institutions to tackle new generation usecases.
How to Build an AI/ML Product and Sell it by SalesChoice CPOProduct School
Main takeaways:
- How to identify the use cases to build an AI/ML product?
- What are the challenges that you would face and how to over come them?
- How to establish stake holder buy-in and design the go-to market strategy?
Leveraging Generative AI & Best practicesDianaGray10
In this event we will cover:
- What is Generative AI and how it is being for future of work.
- Best practices for developing and deploying generative AI based models in productions.
- Future of Generative AI, how generative AI is expected to evolve in the coming years.
Similar to Putting data science in your business a first utility feedback (20)
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.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Enhancing Performance with Globus and the Science DMZGlobus
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
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.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
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.
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
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/
6. Data – THE NEW POWER
Internal Data
Allow us to deliver a
better service for our
customers
Allow us to optimise the
business and give the
better price to our
customers
Allow us to give more
knowledge to our
customers
7. Industry
Data
Individual
Transaction-Level Data Internal Data
Better Agility
Data Lake and Data Warehousing in the
same platform
Enable Data Discovery
Collect more data
Analyse the data with high performance
Next Gen of Data Visualisation on top of
Hadoop
14. The creativity part and lot
of trial / error process.
Feature engineering
Andrew Fogg win the competition
by categorising the colours of cars.
15. ● ML is often used in DS
● Currently, the buzz/trend ML is xgboost which gives most of the
time better result than the traditional Random Forest & Neural
Networks.
● Reason of the success? More Accurate, more efficient, easy to
use, customized and distributed.
● Need less spending time in Feature engineering but still need
some creativity.
Models to predict
17. ● ML is often used in DS
● Currently, the buzz/trend ML is xgboost which gives most of the
time better result than the traditional Random Forest & Neural
Networks.
● Reason of the success? More Accurate, more efficient, easy to
use, customized and distributed.
● Need less spending time in Feature engineering but still need
some creativity.
Models to predict
18. Evaluation - validations
● Overfitting/Underfitting
is the biggest fear of a
Data Scientist.
● Cross validation is one
way to protect the
model to not overfit
19. Feedback loop
● ML algorithm is a life system …
like any life specimen, it needs cares !!!
● Learning by his mistakes, it’s the only way
to progress and to fit a real AI model.
20. Bad Methodology
Main reasons:
• No clear business case
• Try to create the best accurate model in the first place
• No agility
• No code version control
21. An iterative delivery is key
Sprint 1
Sprint 2
Main take away:
• Agility is required
• Weekly delivered is highly recommended to avoid
falling to the “tunnel effect”
23. Gartner Says
“More Than 40
Percent of Data
Science Tasks Will
Be Automated by
2020”
Source: https://www.gartner.com/newsroom/id/3570917
Automation in Machine Learning is starting
24. Gain in Efficiency
● In the old age of BI world, we gain in efficiency by using ETL tool
rather than scripting codes.
However, ML is often associate with R/Python/Scala coding.
25. Dataiku Flow => enable AML
My favorite app
The Collaborative Data Science Platform: Dataiku