Quick and Dirty: Scaling Out Predictive Models Using Revolution Analytics on Teradata
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[Presentation by Skylar Lyon at DataWeek 2014, September 17 2014.] I recently faced the task of how to scale out an existing analytics process. The schedule was compressed - it always is in my world. The data was big - 400+ million rows waiting in database. What did I do? I offered my favorite type of solution - quick and dirty. At the outset, I wasn't sure how easy it would be. Nor was I certain of realized performance gains. But the concept seemed sound and the exercise fun. Let's move the compute to the data via Revolution R Enterprise for Teradata. This presentation outlines my approach in leveraging a colleague's R models as I experimented with running R in-database. Would my path lead to significant improvement? Could it be used to productionalize the workflow?
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![Optimization process
Recode CRAN R to Rx R
Before
trainit <- glm(as.formula(specs[[i]]), data = training.data,
family='binomial', maxit=iters)
fits <- predict(trainit, newdata=test.data, type='response')
After
trainit <- rxGlm(as.formula(specs[[i]]), data = training.data,
family='binomial', maxIterations=iters)
fits <- rxPredict(trainit, newdata=test.data, type='response')
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Nix(OS) for Python Developers - PyCon 25 (Bologna, Italia)
Nix(OS) for Python Developers - PyCon 25 (Bologna, Italia)Peter Bittner How do you onboard new colleagues in 2025? How long does it take? Would you love a standardized setup under version control that everyone can customize for themselves? A stable desktop setup, reinstalled in just minutes. It can be done.
This talk was given in Italian, 29 May 2025, at PyCon 25, Bologna, Italy. All slides are provided in English.
Original slides at https://slides.com/bittner/pycon25-nixos-for-python-developers
TrustArc Webinar: Mastering Privacy Contracting
TrustArc Webinar: Mastering Privacy ContractingTrustArc As data privacy regulations become more pervasive across the globe and organizations increasingly handle and transfer (including across borders) meaningful volumes of personal and confidential information, the need for robust contracts to be in place is more important than ever.
This webinar will provide a deep dive into privacy contracting, covering essential terms and concepts, negotiation strategies, and key practices for managing data privacy risks.
Whether you're in legal, privacy, security, compliance, GRC, procurement, or otherwise, this session will include actionable insights and practical strategies to help you enhance your agreements, reduce risk, and enable your business to move fast while protecting itself.
This webinar will review key aspects and considerations in privacy contracting, including:
- Data processing addenda, cross-border transfer terms including EU Model Clauses/Standard Contractual Clauses, etc.
- Certain legally-required provisions (as well as how to ensure compliance with those provisions)
- Negotiation tactics and common issues
- Recent lessons from recent regulatory actions and disputes
Measuring Microsoft 365 Copilot and Gen AI Success
Measuring Microsoft 365 Copilot and Gen AI SuccessNikki Chapple Session | Measuring Microsoft 365 Copilot and Gen AI Success with Viva Insights and Purview
Presenter | Nikki Chapple 2 x MVP and Principal Cloud Architect at CloudWay
Event | European Collaboration Conference 2025
Format | In person Germany
Date | 28 May 2025
📊 Measuring Copilot and Gen AI Success with Viva Insights and Purview
Presented by Nikki Chapple – Microsoft 365 MVP & Principal Cloud Architect, CloudWay
How do you measure the success—and manage the risks—of Microsoft 365 Copilot and Generative AI (Gen AI)? In this ECS 2025 session, Microsoft MVP and Principal Cloud Architect Nikki Chapple explores how to go beyond basic usage metrics to gain full-spectrum visibility into AI adoption, business impact, user sentiment, and data security.
🎯 Key Topics Covered:
Microsoft 365 Copilot usage and adoption metrics
Viva Insights Copilot Analytics and Dashboard
Microsoft Purview Data Security Posture Management (DSPM) for AI
Measuring AI readiness, impact, and sentiment
Identifying and mitigating risks from third-party Gen AI tools
Shadow IT, oversharing, and compliance risks
Microsoft 365 Admin Center reports and Copilot Readiness
Power BI-based Copilot Business Impact Report (Preview)
📊 Why AI Measurement Matters: Without meaningful measurement, organizations risk operating in the dark—unable to prove ROI, identify friction points, or detect compliance violations. Nikki presents a unified framework combining quantitative metrics, qualitative insights, and risk monitoring to help organizations:
Prove ROI on AI investments
Drive responsible adoption
Protect sensitive data
Ensure compliance and governance
🔍 Tools and Reports Highlighted:
Microsoft 365 Admin Center: Copilot Overview, Usage, Readiness, Agents, Chat, and Adoption Score
Viva Insights Copilot Dashboard: Readiness, Adoption, Impact, Sentiment
Copilot Business Impact Report: Power BI integration for business outcome mapping
Microsoft Purview DSPM for AI: Discover and govern Copilot and third-party Gen AI usage
🔐 Security and Compliance Insights: Learn how to detect unsanctioned Gen AI tools like ChatGPT, Gemini, and Claude, track oversharing, and apply eDLP and Insider Risk Management (IRM) policies. Understand how to use Microsoft Purview—even without E5 Compliance—to monitor Copilot usage and protect sensitive data.
📈 Who Should Watch: This session is ideal for IT leaders, security professionals, compliance officers, and Microsoft 365 admins looking to:
Maximize the value of Microsoft Copilot
Build a secure, measurable AI strategy
Align AI usage with business goals and compliance requirements
🔗 Read the blog https://nikkichapple.com/measuring-copilot-gen-ai/
Protecting Your Sensitive Data with Microsoft Purview - IRMS 2025
Protecting Your Sensitive Data with Microsoft Purview - IRMS 2025Nikki Chapple Session | Protecting Your Sensitive Data with Microsoft Purview: Practical Information Protection and DLP Strategies
Presenter | Nikki Chapple (MVP| Principal Cloud Architect CloudWay) & Ryan John Murphy (Microsoft)
Event | IRMS Conference 2025
Format | Birmingham UK
Date | 18-20 May 2025
In this closing keynote session from the IRMS Conference 2025, Nikki Chapple and Ryan John Murphy deliver a compelling and practical guide to data protection, compliance, and information governance using Microsoft Purview. As organizations generate over 2 billion pieces of content daily in Microsoft 365, the need for robust data classification, sensitivity labeling, and Data Loss Prevention (DLP) has never been more urgent.
This session addresses the growing challenge of managing unstructured data, with 73% of sensitive content remaining undiscovered and unclassified. Using a mountaineering metaphor, the speakers introduce the “Secure by Default” blueprint—a four-phase maturity model designed to help organizations scale their data security journey with confidence, clarity, and control.
🔐 Key Topics and Microsoft 365 Security Features Covered:
Microsoft Purview Information Protection and DLP
Sensitivity labels, auto-labeling, and adaptive protection
Data discovery, classification, and content labeling
DLP for both labeled and unlabeled content
SharePoint Advanced Management for workspace governance
Microsoft 365 compliance center best practices
Real-world case study: reducing 42 sensitivity labels to 4 parent labels
Empowering users through training, change management, and adoption strategies
🧭 The Secure by Default Path – Microsoft Purview Maturity Model:
Foundational – Apply default sensitivity labels at content creation; train users to manage exceptions; implement DLP for labeled content.
Managed – Focus on crown jewel data; use client-side auto-labeling; apply DLP to unlabeled content; enable adaptive protection.
Optimized – Auto-label historical content; simulate and test policies; use advanced classifiers to identify sensitive data at scale.
Strategic – Conduct operational reviews; identify new labeling scenarios; implement workspace governance using SharePoint Advanced Management.
🎒 Top Takeaways for Information Management Professionals:
Start secure. Stay protected. Expand with purpose.
Simplify your sensitivity label taxonomy for better adoption.
Train your users—they are your first line of defense.
Don’t wait for perfection—start small and iterate fast.
Align your data protection strategy with business goals and regulatory requirements.
💡 Who Should Watch This Presentation?
This session is ideal for compliance officers, IT administrators, records managers, data protection officers (DPOs), security architects, and Microsoft 365 governance leads. Whether you're in the public sector, financial services, healthcare, or education.
🔗 Read the blog: https://nikkichapple.com/irms-conference-2025/
Data Virtualization: Bringing the Power of FME to Any Application
Data Virtualization: Bringing the Power of FME to Any ApplicationSafe Software Imagine building web applications or dashboards on top of all your systems. With FME’s new Data Virtualization feature, you can deliver the full CRUD (create, read, update, and delete) capabilities on top of all your data that exploit the full power of FME’s all data, any AI capabilities. Data Virtualization enables you to build OpenAPI compliant API endpoints using FME Form’s no-code development platform.
In this webinar, you’ll see how easy it is to turn complex data into real-time, usable REST API based services. We’ll walk through a real example of building a map-based app using FME’s Data Virtualization, and show you how to get started in your own environment – no dev team required.
What you’ll take away:
-How to build live applications and dashboards with federated data
-Ways to control what’s exposed: filter, transform, and secure responses
-How to scale access with caching, asynchronous web call support, with API endpoint level security.
-Where this fits in your stack: from web apps, to AI, to automation
Whether you’re building internal tools, public portals, or powering automation – this webinar is your starting point to real-time data delivery.
The case for on-premises AI
The case for on-premises AIPrincipled Technologies Exploring the advantages of on-premises Dell PowerEdge servers with AMD EPYC processors vs. the cloud for small to medium businesses’ AI workloads
AI initiatives can bring tremendous value to your business, but you need to support your new AI workloads effectively. That means choosing the best possible infrastructure for your needs—and many companies are finding that the cloud isn’t right for them. According to a recent Rackspace survey of IT executives, 69 percent of companies have moved some of their applications on-premises from the cloud, with half of those citing security and compliance as the reason and 44 percent citing cost.
On-premises solutions provide a number of advantages. With full control over your security infrastructure, you can be certain that all compliance requirements remain firmly in the hands of your IT team. Opting for on-premises also gives you the ability to design your infrastructure to the precise needs of that team and your new AI workloads. Depending on the workload, you may also see performance benefits, along with more predictable costs. As you start to build your next AI initiative, consider an on-premises solution utilizing AMD EPYC processor-powered Dell PowerEdge servers.
Agentic AI Explained: The Next Frontier of Autonomous Intelligence & Generati...
Agentic AI Explained: The Next Frontier of Autonomous Intelligence & Generati...Aaryan Kansari Agentic AI Explained: The Next Frontier of Autonomous Intelligence & Generative AI
Discover Agentic AI, the revolutionary step beyond reactive generative AI. Learn how these autonomous systems can reason, plan, execute, and adapt to achieve human-defined goals, acting as digital co-workers. Explore its promise, key frameworks like LangChain and AutoGen, and the challenges in designing reliable and safe AI agents for future workflows.
Sticky Note Bullets:
Definition: Next stage beyond ChatGPT-like systems, offering true autonomy.
Core Function: Can "reason, plan, execute and adapt" independently.
Distinction: Proactive (sets own actions for goals) vs. Reactive (responds to prompts).
Promise: Acts as "digital co-workers," handling grunt work like research, drafting, bug fixing.
Industry Outlook: Seen as a game-changer; Deloitte predicts 50% of companies using GenAI will have agentic AI pilots by 2027.
Key Frameworks: LangChain, Microsoft's AutoGen, LangGraph, CrewAI.
Development Focus: Learning to think in workflows and goals, not just model outputs.
Challenges: Ensuring reliability, safety; agents can still hallucinate or go astray.
Best Practices: Start small, iterate, add memory, keep humans in the loop for final decisions.
Use Cases: Limited only by imagination (e.g., drafting business plans, complex simulations).
6th Power Grid Model Meetup - 21 May 2025
6th Power Grid Model Meetup - 21 May 2025DanBrown980551 6th Power Grid Model Meetup
Join the Power Grid Model community for an exciting day of sharing experiences, learning from each other, planning, and collaborating.
This hybrid in-person/online event will include a full day agenda, with the opportunity to socialize afterwards for in-person attendees.
If you have a hackathon proposal, tell us when you register!
About Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
Kubernetes Cloud Native Indonesia Meetup - May 2025
Kubernetes Cloud Native Indonesia Meetup - May 2025Prasta Maha Kubernetes Cloud Native Indonesia Meetup - May 2025
Droidal: AI Agents Revolutionizing Healthcare
Droidal: AI Agents Revolutionizing HealthcareDroidal LLC Droidal’s AI Agents are transforming healthcare by bringing intelligence, speed, and efficiency to key areas such as Revenue Cycle Management (RCM), clinical operations, and patient engagement. Built specifically for the needs of U.S. hospitals and clinics, Droidal's solutions are designed to improve outcomes and reduce administrative burden.
Through simple visuals and clear examples, the presentation explains how AI Agents can support medical coding, streamline claims processing, manage denials, ensure compliance, and enhance communication between providers and patients. By integrating seamlessly with existing systems, these agents act as digital coworkers that deliver faster reimbursements, reduce errors, and enable teams to focus more on patient care.
Droidal's AI technology is more than just automation — it's a shift toward intelligent healthcare operations that are scalable, secure, and cost-effective. The presentation also offers insights into future developments in AI-driven healthcare, including how continuous learning and agent autonomy will redefine daily workflows.
Whether you're a healthcare administrator, a tech leader, or a provider looking for smarter solutions, this presentation offers a compelling overview of how Droidal’s AI Agents can help your organization achieve operational excellence and better patient outcomes.
A free demo trial is available for those interested in experiencing Droidal’s AI Agents firsthand. Our team will walk you through a live demo tailored to your specific workflows, helping you understand the immediate value and long-term impact of adopting AI in your healthcare environment.
To request a free trial or learn more:
https://droidal.com/
Multistream in SIP and NoSIP @ OpenSIPS Summit 2025
Multistream in SIP and NoSIP @ OpenSIPS Summit 2025Lorenzo Miniero Slides for my "Multistream support in the Janus SIP and NoSIP plugins" presentation at the OpenSIPS Summit 2025 event.
They describe my efforts refactoring the Janus SIP and NoSIP plugins to allow for the gatewaying of an arbitrary number of audio/video streams per call (thus breaking the current 1-audio/1-video limitation), plus some additional considerations on what this could mean when dealing with application protocols negotiated via SIP as well.
Supercharge Your AI Development with Local LLMs
Supercharge Your AI Development with Local LLMsFrancesco Corti In today's AI development landscape, developers face significant challenges when building applications that leverage powerful large language models (LLMs) through SaaS platforms like ChatGPT, Gemini, and others. While these services offer impressive capabilities, they come with substantial costs that can quickly escalate especially during the development lifecycle. Additionally, the inherent latency of web-based APIs creates frustrating bottlenecks during the critical testing and iteration phases of development, slowing down innovation and frustrating developers.
This talk will introduce the transformative approach of integrating local LLMs directly into their development environments. By bringing these models closer to where the code lives, developers can dramatically accelerate development lifecycles while maintaining complete control over model selection and configuration. This methodology effectively reduces costs to zero by eliminating dependency on pay-per-use SaaS services, while opening new possibilities for comprehensive integration testing, rapid prototyping, and specialized use cases.
Quick and Dirty: Scaling Out Predictive Models Using Revolution Analytics on Teradata
- 1. Rapid Productionalization of Predictive Models In-database Modeling with Revolution Analytics on Teradata Skylar Lyon Accenture Analytics
- 2. Introduction Skylar Lyon Accenture Analytics • 7 years of experience with focus on big data and predictive analytics - using discrete choice modeling, random forest classification, ensemble modeling, and clustering • Technology experience includes: Hadoop, Accumulo, PostgreSQL, qGIS, JBoss, Tomcat, R, GeoMesa, and more • Worked from Army installations across the nation and also had the opportunity to travel twice to Baghdad to deploy solutions downrange. Copyright © 2014 Accenture. All rights reserved. 2
- 3. How we got here Project background and my involvement • New Customer Analytics team for Silicon Valley Internet eCommerce giant • Data scientists developing predictive models • Deferred focus on productionalization • Joined as Big Data Infrastructure and Analytics Lead Copyright © 2014 Accenture. All rights reserved. 3
- 4. Colleague‘s CRAN R model Binomial logistic regression • 50+ Independent variables including categorical with indicator variables • Train from small sample (many thousands) – not a problem in and of itself • Scoring across entire corpus (many hundred millions) – slightly more challenging Copyright © 2014 Accenture. All rights reserved. 4
- 5. We optimized the current productionalization process We moved compute to data Before After Reduced 5+ hour process to 40 seconds Copyright © 2014 Accenture. All rights reserved. 5
- 6. Benchmarking our optimized process 5+ hours to 40 seconds: Recommendation is that this now become the defacto productionalization process Copyright © 2014 Accenture. All rights reserved. 6 rows minutes
- 7. Optimization process Recode CRAN R to Rx R Before trainit <- glm(as.formula(specs[[i]]), data = training.data, family='binomial', maxit=iters) fits <- predict(trainit, newdata=test.data, type='response') After trainit <- rxGlm(as.formula(specs[[i]]), data = training.data, family='binomial', maxIterations=iters) fits <- rxPredict(trainit, newdata=test.data, type='response') Copyright © 2014 Accenture. All rights reserved. 7
- 8. Additional benefits to new process Technology is increasing data science team’s options and opportunities • Train in-database on much larger set – reduces need to sample • Nearly “native” R language – decrease deploy time • Hadoop support – score in multiple data warehouses Copyright © 2014 Accenture. All rights reserved. 8
- 9. Appendix Table of Contents • Technical Considerations Copyright © 2014 Accenture. All rights reserved. 9
- 10. Technical considerations Environment setup • Teradata environment – 4 node, 1700 series appliance server • Revolution R Enterprise – version 7.1, running R 3.0.2 Copyright © 2014 Accenture. All rights reserved. 10