Generalized B2B Machine Learning by Andrew Waage
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Abstract:- In this talk, we propose a generalized machine learning framework for e-commerce businesses. The framework is responsible for over 30 different user-level predictions including lifetime value, recommendations, churn predictions, engagement and lead scoring. These predictions provide a vital layer of intelligence for a digital marketer. Kinesis is used to capture browsing information from over 120M users across 100 companies (both in-app and web). A data processing and feature engineering layer is build on Apache Spark. These features provide inputs to predictive models for business applications. Different models each for Churn, Lifetime value, Product recommendation and search are written on Spark. These models can be plugged into any marketing campaign for any integrated e-commerce company leading to a generalized system. We finally present a monitoring system for machine learning called RS Sauron. This system provides more than 200 objective metrics measuring the health of predictive models, and depicts KPIs for model accuracy in a continual setting.
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모든 것을 연결하여 최적의 인프라 기저를 형성하고 그 위에 빅데이터 저장 및 분석의 기반을 만들어 인공지능을 얹은 글로벌 시장의 리더와 붙을 수 있는 플랫폼을 소개합니다.
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Check out the recording at https://info.looker.com/h/i/308848878-power-to-the-people-a-stack-to-empower-every-user-to-make-data-driven-decisions
Why is LEO CDP important for digital business ?
The slide for my startup to get funding
1) Current problems of digital business
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Exploratory analysis
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GraphTour 2020 - Customer Journey with Neo4j Services
This document provides an overview of Neo4j's customer journey and solutions for working with graph databases. It includes sections on problem identification and modeling sessions, desired business outcomes and data integration. It also shows examples of graph queries and discusses architecture, sizing and implementation considerations. The document aims to illustrate Neo4j's full end-to-end process for helping customers adopt graph databases from initial problem assessment through solution delivery.
Storytelling & Interactive reporting with Power BI bookmarks and drillthrough
How to make sure that the end-user of your Power BI report understands the information well? There is way more involved in presenting your data then only a chart. Think about interaction, presenting the right information at the right time and much more!
By using Storytelling it is possible to tell the story behind the data. Where does the information need come from and how did we get to an answer? The complete story-line covered in your reports and dashboards to make sure the data is interpreted in the right way by the users.
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Digitalisation from the back office to the factory floor
AI is a huge set of tools for making computers behave intelligently - Andrew Ng
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Identifying Users Across Platforms with a Universal ID Webinar Slides
Before you can dive into any kind of advanced customer behavior analysis, you have to make sure you're counting each user once and only once. This is a surprisingly difficult problem, especially with customer touchpoints across devices. To accurately measure your users, you have to tie identities across anonymous and logged-in sessions, account for when people change their email addresses, and plan for cross-platform interactions. In this webinar, Erin Franz, data analyst from Looker, and Will Johnson, Success Lead at Segment, share signals that you are miscalculating your users and methods for creating a universal user ID.
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Tableau Conference 2018: Binging on Data - Enabling Analytics at Netflix
In this conference session we share how we are using Tableau “out of the box” and also describe how it fits into our overall data environment. In addition, we’ll describe how we expect to use the Data Catalog and Object Model, our explorations of large-scale data stores, and challenges we are working on including governance and data lineage. Video of session can be viewed here: https://youtu.be/Nr24tw3dmZQ
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모든 것을 연결하여 최적의 인프라 기저를 형성하고 그 위에 빅데이터 저장 및 분석의 기반을 만들어 인공지능을 얹은 글로벌 시장의 리더와 붙을 수 있는 플랫폼을 소개합니다.
멀티벤더 지향의 기본 개념위에 메이저 금융S사의 지원으로 이미 많은 시스템 및 메이저 기업의 IoT데이터를 수집하여 빅데이터 분석을 진행중이며, S사의 로봇 기술 지원 및 향후 Drone의 제어, 데이터의 머신러닝등을 통하여 이미 글로벌 시장을 향하여 나아가고 있습니다.
많은 분들의 관심과 협력 부탁 드립니다.
문의 및 파트너쉽 : contact@littleworld.net
PaaS for Dummies
PaaS provides a platform in the cloud for developing, testing, and deploying applications without having to manage the underlying infrastructure. It offers tools for selecting database and middleware configurations and accessing development tools via a web browser. PaaS sits between IaaS, which provides cloud infrastructure resources, and SaaS, which delivers fully managed business applications. Drivers for adopting PaaS include enabling business agility, reducing costs through economies of scale, and providing a better customer experience through digital innovation.
Production testing and disaster recovery
This document discusses advanced concepts in Logic Apps including production testing and disaster recovery. It outlines using Azure API Management for production testing by routing traffic to different Logic App versions. This allows rolling out new versions, onboarding partners, and rolling back if needed. The document also discusses using multiple Logic App Integration Accounts across regions for disaster recovery. It demonstrates configuring an API to route traffic between regions and using Azure tools like Log Analytics for monitoring.
The Biggest Lies That Digital Marketers Tell Themselves - 3XE Digital
3XE Digital Proudly presents:
Samuel Scott, Marcom Director at Logz.io & Columnist at The Drum
Too much of the time, people accept whatever so-called experts and influencers tell them. We believe whatever we hear within the digital marketing echo chamber — even when those ideas are presented without evidence and later shown to be false. In this keynote address, Samuel Scott, the Marcom Director of log analytics platform Logz.io and a columnist for The Drum, will highlight the biggest lies and falsehoods within online marketing.
**presentation is NOT for commercial use**
For more info visit: www.3xedigital.com
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Open Source Monitoring Tools Shootout
The Open source market is getting overcrowded with different Network monitoring solutions, and not without reason, monitoring your infrastructure become more important each day, you have to know what's going on for your boss, your customers and for yourself. Nagios started the evolution, but today OpenNMS, Zabix, Zenoss, Groundworks, Hyperic and different others are showing up in the market. Do you want lightweight, or feature full, how far do you want to go with your monitoring, just on os level, or do you want to dig into your applications, do you want to know how many query per seconds your MySQL database is serving, or do you want to know about the internal state of your JBoss, or be triggered if the OOM killer will start working soon. This presentation will guide the audience trough the different alternatives, based on our experiences in the field. We will be looking both at alerting and trending and how easy or difficult it is to deploy such an environment.
SPSNL17 - Securing Office 365 and Microsoft Azure like a rock star (or groupi...
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Plan de transport 2014: le Brabant Flamand
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Nato Constitution- & Laws. Chris Helweg
NATO is een militaire organisatie. Zodoende is zij net als elk
e krijgsmacht gebonden aan wetten! Hier worden ze
artikelen genoemd, Hst NATO handvest is wel degelijk een
constitutie.
Wettelijk gebonden verplichtingen waaraan elke deelnemer
zich MOET houden. Net als de krijgswet
Chris Helweg
Things you should know about Scalability!
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The Loss of HMAS SYDNEY 2: Medical Aspects- Westphalen
The document summarizes the medical aspects of the sinking of HMAS SYDNEY II after encountering the German raider KORMORAN in 1941. It describes that SYDNEY had 10 medical staff including 2 doctors and was likely hit by over 200,000 shell fragments, resulting in over 70% casualties of the 645 crew including 225 killed in action and 225 wounded in action. The document pays tribute to the medical personnel who all perished when the ship sank with no survivors.
AI = SE , giip system manage automation with A.I
인공지능이 시스템 엔지니어의 모든일을 할 수 있다면 어떨까?
클라우드 및 레거시의 다양한 인프라 환경에서 지속적으로 배워가면서 시스템 운영(모니터링, 장애대응, 백업, 관리등)을 자동으로 처리 하는 서비스를 만나보세요.
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AWS Summit - Trends in Advanced Monitoring for AWS environments
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The document discusses challenges in evaluating recommender systems and moving towards more real-world online evaluation. It summarizes two industry challenges from plista and XING that aim to address these issues. The plista challenge involves news recommendations using live recommendation streams. The XING challenge involves job recommendations and evaluates systems based on user interactions from a live job recommendation platform. Both challenges aim to better simulate real-world evaluation using online and offline metrics on real-world data streams and user feedback.
Decision Matrix for IoT Product Development
At first sight, the development of "hardware" products hardly differs from that of IoT devices. Here you can see the methodology of IoT product development based on an IoT framework by Daniel Elizalde. It’s a convenient and simple model that estimates expenses and potential income, evaluates the technological complexity and at the same time is easily understood by the client.
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Managing an Experimentation Platform by LinkedIn Product Leader
Main Takeaways:
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Operationalizing Machine Learning—Managing Provenance from Raw Data to Predic...
Our team at Comcast is challenged with operationalizing predictive ML models to improve customer experience. Our goal is to eliminate bottlenecks in the process from model inception to deployment and monitoring.
Traditionally CI/CD manages code and infrastructure artifacts like container definitions. We want to extend it to support granular traceability enabling tracking of ML Models from use-case, to feature/attribute selection, development of versioned datasets, model training code, model evaluation artifacts, model prediction deployment containers, and sinks to which the predictions/outcomes are persisted to. Our framework stack enables us to track models from use-case to deployments, manage and evaluate multiple models simultaneously in the live yet dark mode and continue to monitor models in production against real-world outcomes using configurable policies.
The technologies/components which drive this vision are:
1. FeatureStore – Enables data scientists to reuse versioned features and review feature metrics by models. Self-Service capabilities allow all teams to onboard their events data into the feature store.
2. ModelRepository – Manages meta-data about models including pre-processing parameters (Ex. Scaling parameters for features), mapping to the features needed to execute the model, model discovery mechanisms, etc.
3. Spark on Alluxio – Alluxio provides the universal data plane on top of various under-stores (Ex. S3, HDFS, RDBMS). Apache Spark with its Data Sources API provides a unified query language which Data Scientist use to consume features to create training/validation/test datasets which are versioned and integrated into the full model pipeline using Ground-Context discussed next.
4. Ground-Context – This open-source vendor-neutral data context service enables full traceability from use-case, models, features, model to features mapping, versioned datasets, model training codebase, model deployment containers and prediction/outcome sinks. It integrates with the Feature-Store, Container Repository and Git to integrate data, code and run-time artifacts for CI/CD integration.
MLSEV Virtual. One Platform to Rule Them All
One Platform to Rule Them All, by Francis Cepero, Head of Vertical Market Solutions at A1Digital, a spinoff of the Telekom Austria Group. This talk presents how to create a data analytics platform that delivers insights for the connected enterprise.
*MLSEV 2020: Virtual Conference.
AI projects - Lifecyle & Best Practices
As companies around the world look to get a jump on AI efforts, there’s one major question: with dozens of potential AI use cases but limited resources, how can organizations prioritize the right projects?
Carefully selected and well-designed Enterprise AI projects facilitate faster and more efficient collaboration among AI scientists and engineers and ultimately help organizations set up for AI success. In this presentation we share a comprehensive framework for:
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Machine Learning and Industrie 4.0
Overview presentation at TU Munich
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The document provides an overview of a cloud computing course, including introductions to cloud concepts and technologies, demonstrations of cloud capabilities, security considerations, hands-on labs, and a business case study. The course outline covers cloud models, elasticity, pay-per-use, on-demand services, virtual private clouds, storage solutions, serverless technologies, and implementing security and governance in the cloud.
Machine Learning & Predictive Maintenance
This presentation introduces the concept of Machine Learning and then discusses how Machine Learning is being used in the Predictive Maintenance domain.
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Intelligent Digital Mesh Testing
This document discusses intelligent digital testing in an environment of intelligent hyperconnectivity across domains. It outlines the benefits of AI/ML for testing, such as using AI to generate test scripts and identify bugs. An intelligent testing approach is proposed that uses a bot to identify assets, optimize test cases by eliminating duplicates, automate testing, and analyze results to predict future defects. Challenges of intelligent testing are also addressed. The business benefits are improved quality, efficiency, revenues and customer satisfaction through optimized testing coverage, quicker time to market, and data-driven insights.
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Barga Galvanize Sept 2015
Roger S. Barga discusses his experience in data science and predictive analytics projects across multiple industries. He provides examples of predictive models built for customer segmentation, predictive maintenance, customer targeting, and network intrusion prevention. Barga also outlines a sample predictive analytics project for a real estate client to predict whether they can charge above or below market rates. The presentation emphasizes best practices for building predictive models such as starting small, leveraging third-party tools, and focusing on proxy metrics that drive business outcomes.
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How to Build an AI/ML Product and Sell it by SalesChoice CPO
Main takeaways:
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MLOps and Data Quality: Deploying Reliable ML Models in Production
Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure.
For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).
Agenda
- Data Quality and why it matters
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Managing AI Products
This document discusses key considerations for managing AI products. It begins with an overview of intelligent systems and the OODA loop model of decision making. It then covers the different areas of AI including machine learning, deep learning, and supervised vs unsupervised learning. The rest of the document provides guidance on strategic areas for AI product management such as corporate and data strategy, analyzing use cases, building minimal viable products, and influencing other teams to deliver AI capabilities. It emphasizes the importance of data acquisition, network effects, and focusing on practical applications that create business value.
Digital strategy overview
The document provides an overview of digital strategy and transformation. It discusses 5 key domains: customers, competitors, data, innovation, and value. For each domain, it outlines various aspects to consider. For customers, it discusses engaging customers, managing diversity and experiences. For competitors, it discusses how customers and industries can become competitors. For data, it discusses collecting, analyzing and visualizing data to make decisions. For innovation, it outlines a discover-ideate-validate-execute process. For value, it discusses understanding customer behavior through 5W1H (who, what, where, when, whom, how). The document provides guidance on developing a digital strategy across these important domains.
Building a Real-Time Security Application Using Log Data and Machine Learning...
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- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
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Data Con LA 2022 Keynotes
1. LAUSD has been developing its enterprise data and reporting capabilities since 2000, with various systems and dashboards launched over the years to provide different types of data and reporting, including student outcomes and achievement reports, individual student records, and teacher/staff data.
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Data Con LA 2022 Keynotes
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Data Con LA 2022 Keynote
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Mike Limcaco, Analytics Specialist / Customer Engineer at Google
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Melinda Thielbar, Data Science Practice Lead and Director of Data Science at Fidelity Investments
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Antje Barth, Principal Developer Advocate, AI/ML at AWS & Chris Fregly, Principal Engineer, AI & ML at AWS
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4. Interpreting the results is more work -- and more fun -- than running the algo itself. We'll discuss how we transform from ""segment 1"", ""segment 2"", etc. to something that non-analytics users (Marketing, Operations, etc.) could actually benefit from.
5. So what? How did team across Shopkick change their approach given what Analytics had discovered.
Data Con LA 2022 - Modernizing Analytics & AI for today's needs: Intuit Turbo...
Ravi Pillala, Chief Data Architect & Distinguished Engineer at Intuit
TurboTax is one of the well known consumer software brand which at its peak serves 385K+ concurrent users. In this session, We start with looking at how user behavioral data & tax domain events are captured in real time using the event bus and analyzed to drive real time personalization with various TurboTax data pipelines. We will also look at solutions performing analytics which make use of these events, with the help of Kafka, Apache Flink, Apache Beam, Spark, Amazon S3, Amazon EMR, Redshift, Athena and Amazon lambda functions. Finally, we look at how SageMaker is used to create the TurboTax model to predict if a customer is at risk or needs help.
Data Con LA 2022 - Moving Data at Scale to AWS
George Mansoor, Chief Information Systems Officer at California State University
Overview of the CSU Data Architecture on moving on-prem ERP data to the AWS Cloud at scale using Delphix for Data Replication/Virtualization and AWS Data Migration Service (DMS) for data extracts
Data Con LA 2022 - Collaborative Data Exploration using Conversational AI
Anand Ranganathan, Chief AI Officer at Unscrambl
Conversational AI is getting more and more widely used for customer support and employee support use-cases. In this session, I'm going to talk about how it can be extended for data analysis and data science use-cases ... i.e., how users can interact with a bot to ask analytical questions on data in relational databases.
This allows users to explore complex datasets using a combination of text and voice questions, in natural language, and then get back results in a combination of natural language and visualizations. Furthermore, it allows collaborative exploration of data by a group of users in a channel in platforms like Microsoft Teams, Slack or Google Chat.
For example, a group of users in a channel can ask questions to a bot in plain English like ""How many cases of Covid were there in the last 2 months by state and gender"" or ""Why did the number of deaths from Covid increase in May 2022"", and jointly look at the results that come back. This facilitates data awareness, data-driven collaboration and joint decision making among teams in enterprises and outside.
In this talk, I'll describe how we can bring together various features including natural-language understanding, NL-to-SQL translation, dialog management, data story-telling, semantic modeling of data and augmented analytics to facilitate collaborate exploration of data using conversational AI.
Data Con LA 2022 - Why Database Modernization Makes Your Data Decisions More ...
Anil Inamdar, VP & Head of Data Solutions at Instaclustr
The most modernized enterprises utilize polyglot architecture, applying the best-suited database technologies to each of their organization's particular use cases. To successfully implement such an architecture, though, you need a thorough knowledge of the expansive NoSQL data technologies now available.
Attendees of this Data Con LA presentation will come away with:
-- A solid understanding of the decision-making process that should go into vetting NoSQL technologies and how to plan out their data modernization initiatives and migrations.
-- They will learn the types of functionality that best match the strengths of NoSQL key-value stores, graph databases, columnar databases, document-type databases, time-series databases, and more.
-- Attendees will also understand how to navigate database technology licensing concerns, and to recognize the types of vendors they'll encounter across the NoSQL ecosystem. This includes sniffing out open-core vendors that may advertise as “open source,"" but are driven by a business model that hinges on achieving proprietary lock-in.
-- Attendees will also learn to determine if vendors offer open-code solutions that apply restrictive licensing, or if they support true open source technologies like Hadoop, Cassandra, Kafka, OpenSearch, Redis, Spark, and many more that offer total portability and true freedom of use.
Data Con LA 2022 - Intro to Data Science
Zia Khan, Computer Systems Analyst and Data Scientist at LearningFuze
Data Science tutorial is designed for people who are new to Data Science. This is a beginner level session so no prior coding or technical knowledge is required. Just bring your laptop with WiFi capability. The session starts with a review of what is data science, the amount of data we generate and how companies are using that data to get insight. We will pick a business use case, define the data science process, followed by hands-on lab using python and Jupyter notebook. During the hands-on portion we will work with pandas, numpy, matplotlib and sklearn modules and use a machine learning algorithm to approach the business use case.
Data Con LA 2022 - How are NFTs and DeFi Changing Entertainment
Mariana Danilovic, Managing Director at Infiom, LLC
We will address:
(1) Community creation and engagement using tokens and NFTs
(2) Organization of DAO structures and ways to incentivize Web3 communities
(3) DeFi business models applied to Web3 ventures
(4) Why Metaverse matters for new entertainment and community engagement models.
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...
Curtis ODell, Global Director Data Integrity at Tricentis
Join me to learn about a new end-to-end data testing approach designed for modern data pipelines that fills dangerous gaps left by traditional data management tools—one designed to handle structured and unstructured data from any source. You'll hear how you can use unique automation technology to reach up to 90 percent test coverage rates and deliver trustworthy analytical and operational data at scale. Several real world use cases from major banks/finance, insurance, health analytics, and Snowflake examples will be presented.
Key Learning Objective
1. Data journeys are complex and you have to ensure integrity of the data end to end across this journey from source to end reporting for compliance
2. Data Management tools do not test data, they profile and monitor at best, and leave serious gaps in your data testing coverage
3. Automation with integration to DevOps and DataOps' CI/CD processes are key to solving this.
4. How this approach has impact in your vertical
Data Con LA 2022-Perfect Viral Ad prediction of Superbowl 2022 using Tease, T...
1. The document discusses methods for predicting and engineering viral Super Bowl ads, including a panel-based analysis of video content characteristics and a deep learning model measuring social media effects.
2. It provides examples of ads from Super Bowl 2022 that scored well using these methods, such as BMW and Budweiser ads, and compares predicted viral rankings to actual results.
3. The document also demonstrates how to systematically test, tweak, and target an ad campaign like Bajaj Pulsar's to increase virality through modifications to title, thumbnail, tags and content based on audience feedback.
Data Con LA 2022- Embedding medical journeys with machine learning to improve...
Jai Bansal, Senior Manager, Data Science at Aetna
This talk describes an internal data product called Member Embeddings that facilitates modeling of member medical journeys with machine learning.
Medical claims are the key data source we use to understand health journeys at Aetna. Claims are the data artifacts that result from our members' interactions with the healthcare system. Claims contain data like the amount the provider billed, the place of service, and provider specialty. The primary medical information in a claim is represented in codes that indicate the diagnoses, procedures, or drugs for which a member was billed. These codes give us a semi-structured view into the medical reason for each claim and so contain rich information about members' health journeys. However, since the codes themselves are categorical and high-dimensional (10K cardinality), it's challenging to extract insight or predictive power directly from the raw codes on a claim.
To transform claim codes into a more useful format for machine learning, we turned to the concept of embeddings. Word embeddings are widely used in natural language processing to provide numeric vector representations of individual words.
We use a similar approach with our claims data. We treat each claim code as a word or token and use embedding algorithms to learn lower-dimensional vector representations that preserve the original high-dimensional semantic meaning.
This process converts the categorical features into dense numeric representations. In our case, we use sequences of anonymized member claim diagnosis, procedure, and drug codes as training data. We tested a variety of algorithms to learn embeddings for each type of claim code.
We found that the trained embeddings showed relationships between codes that were reasonable from the point of view of subject matter experts. In addition, using the embeddings to predict future healthcare-related events outperformed other basic features, making this tool an easy way to improve predictive model performance and save data scientist time.
Data Con LA 2022 - Data Streaming with Kafka
Jie Chen, Manager Advisory, KPMG
Data is the new oil. However, many organizations have fragmented data in siloed line of businesses. In this topic, we will focus on identifying the legacy patterns and their limitations and introducing the new patterns packed by Kafka's core design ideas. The goal is to tirelessly pursue better solutions for organizations to overcome the bottleneck in data pipelines and modernize the digital assets for ready to scale their businesses. In summary, we will walk through three uses cases, recommend Dos and Donts, Take aways for Data Engineers, Data Scientist, Data architect in developing forefront data oriented skills.
More from Data Con LA (20)
Data Con LA 2022 - Using Google trends data to build product recommendations
Data Con LA 2022 - Using Google trends data to build product recommendations
Data Con LA 2022 - Improving disaster response with machine learning
Data Con LA 2022 - Improving disaster response with machine learning
Data Con LA 2022 - What's new with MongoDB 6.0 and Atlas
Data Con LA 2022 - What's new with MongoDB 6.0 and Atlas
Data Con LA 2022 - Real world consumer segmentation
Data Con LA 2022 - Real world consumer segmentation
Data Con LA 2022 - Modernizing Analytics & AI for today's needs: Intuit Turbo...
Data Con LA 2022 - Modernizing Analytics & AI for today's needs: Intuit Turbo...
Data Con LA 2022 - Collaborative Data Exploration using Conversational AI
Data Con LA 2022 - Collaborative Data Exploration using Conversational AI
Data Con LA 2022 - Why Database Modernization Makes Your Data Decisions More ...
Data Con LA 2022 - Why Database Modernization Makes Your Data Decisions More ...
Data Con LA 2022 - How are NFTs and DeFi Changing Entertainment
Data Con LA 2022 - How are NFTs and DeFi Changing Entertainment
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...
Data Con LA 2022-Perfect Viral Ad prediction of Superbowl 2022 using Tease, T...
Data Con LA 2022-Perfect Viral Ad prediction of Superbowl 2022 using Tease, T...
Data Con LA 2022- Embedding medical journeys with machine learning to improve...
Data Con LA 2022- Embedding medical journeys with machine learning to improve...
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“I’m still / I’m still / Chaining from the Block”
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How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
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Climate Impact of Software Testing at Nordic Testing Days
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.
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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.
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20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
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Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
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Generalized B2B Machine Learning by Andrew Waage
- 2. 2012 Santa Monica ~50 (and hiring!)
- 3. AI Powered Marketing Automation Platform Step 1 Collect Data Step 2 Generate Predictions Step 3 Automation Powers Intelligent Campaigns Across Channels Ecom / Retail Behavioral Custom Demographic Email Campaigns On-Site Display Mobile Call Center
- 4. What kind of Scale? 100’s Clients 210M+ Customers Tracked 1000+ Client-specific Models 2 Billion+ Predictions Daily10K+ Actions / second
- 5. “Generalized ML Platform” What’s the challenge?
- 6. The Challenge • Many Clients • Dirty Data • Sparse Datasets • Custom Attributes • Various Industries Clean PredictionsModel Layer! C1 C2 C3 C4
- 7. What Kind of Predictions? Purchase Probability: High-likelihood Lifecycle Stage: Ready to Buy Churn Time: 300 days Customer Future Value: $925 Contact Frequency: Every 3 days Optimal Time to Engage: Thursday 7-9PM Optimal Incentive/Discount: Dollars Off Product Recommendations: Based on interest Optimal Subject Line: Individual preference Optimal Template: Individual preference
- 8. Our Approach & Learnings 1. Robust Ingestion Pipeline 2. Common Feature Engineering Layer 3. “Plug-in” Architecture for Models 4. Evaluation / AB Testing 5. Robust Monitoring & Visualization
- 9. 1. Robust Ingestion Pipeline 10K+ Actions Per Second auto-scaling! auto-scaling lambdas! • Abstraction Layer: Data Ingestion • Do not compromise for clean data • Auto-scaling everywhere • High confidence in upstream data Flume Kinesis
- 10. 2. Common Feature Engineering Layer • Abstraction: Feature Layer • Allow custom features • Handle feature selection • Modelers know what to expect Raw Data User Behavior Features Product Features User Sta4c Features Timing Model CLV Model Recommender
- 11. 3. Model Plug-in Architecture C6 C3 C4 C5 C7 • Plug-in Architecture • Tune model hyper-parameters • A/B test models per client C1 C8 C2 Client’s Model Execution Plan
- 12. Recommender System Multi-Layer Personalization: • Layer 1: ML / Algorithmic • ALS CF, Content-based, Item-Item • Layer 2: User-level Domain Logic • User-level predictions (gender, location, shoe sizes) • Layer 3: Client-tuned Domain Logic / Controls • Rank by profit-margin • Increase discovery rate influxer 1. Algorithmic 2. User-Level Domain Logic 3. Client-Level Controls
- 13. 4. Model Evaluation / Fast Feedback A/B Framework M1 M2 M3 • Start Simple • Collect feedback data • Skip long production cycle • Unbiased policy generation is important M1 Campaign Predic4ons
- 14. 5. Robust Model Monitoring and Visualization “Sauron” (LOTR)! Monitor, monitor, monitor!!
- 15. Monitor Recs: Distributions, Coverage, Diversity REF: hQp://sauron.rsci.co/advanced/sites/181/data_types/31
- 16. Monitoring Subject Line Bandit Models
- 17. Churn Rates, ROC Curve, Reliability Curve
- 18. Our Data Science Stack Persistence! Pipeline / Process! Viz / Monitor! Code!
- 19. Takeaways 1. Use abstraction layers - Clean / common interfaces 2. Monitor, monitor, monitor - Fast feedback 3. Start simple and keep iterating