Scaling Data Science: Engineering a Platform
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At DataScience, all analysts work on a single virtual machine called “data science tools.” This presentation discusses the path to building a singular virtualized machine used to scale across our current staff and to quickly onboard new staff.
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สไลด์จาก Session "มีเว็บไปทำไม ถ้าไม่หัดเก็บข้อมูล" (Data Science for WordPress websites) จากงาน WordCamp Bangkok 2017 เมื่อวันที่ 18 กุมภาพันธ์ 2560
นำเสนอโดย วรธนะ งามตระกูลชล (Woratana Ngarmtrakulchol) จาก "Data Science ชิลชิล" - https://www.facebook.com/datasciencechill
06. graph mining
이번 슬라이드는 Graph mining의 기초에 대한 것이다.
고전 문제인 Graph cut에 대한 개념과 수학적인 배경을 설명하고, 이 개념이 clustering (클러스터링)에서 어떻게 사용되는지를 설명한다.
Graph mining, cut, clustring의 기초를 알기에 매우 적합한 자료이다.
บทความ Big Data School ใน IMC e-Magazine
บทความ Big Data School ใน IT Trends, e-Magazine ของ IMC Institute
ContrattiPubblici.org - Seminario ANCI - Roma - SAA2017
Venerdì 10 Marzo 2017, sala Conferenze ANCI a Roma, Federico Morando presenta ContrattiPubblici.org nell’ambito di un seminario del Corso di Formazione Specialistica in Amministrazione Municipale (ForsAM_V Edizione): "L'Open Government per i giovani amministratori: incontro con gli studenti del Corso di Formazione specialistica in Amministrazione Municipale".
Iniziativa nell'ambito della Settimana dell’Amministrazione Aperta.
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ContrattiPubblici.org - Seminario ANCI - Roma - SAA2017
ContrattiPubblici.org - Seminario ANCI - Roma - SAA2017
Similar to Scaling Data Science: Engineering a Platform
Building successful data science teams
Speaker: Venkatesh Umaashankar
LinkedIn: https://www.linkedin.com/in/venkateshumaashankar/
What will be discussed?
What is Data Science?
Types of data scientists
What makes a Data Science Team? Who are its members?
Why does a DS team need Full Stack Developer?
Who should lead the DS Team
Building a Data Science team in a Startup Vs Enterprise
Case studies on:
Evolution Of Airbnb’s DS Team
How Facebook on-boards DS team and trains them
Apple’s Acqui-hiring Strategy to build DS team
Spotify -‘Center of Excellence’ Model
Who should attend?
Managers
Technical Leaders who want to get started with Data Science
The Business Value of Modernizing your Windows Infrastructure and Bringing Li...
This document discusses how modernizing a Windows infrastructure with Puppet and Chocolatey can provide efficiency, cost containment, business continuity, security, and faster time to value. It provides an overview of Puppet for infrastructure automation and Chocolatey for software package management on Windows. Companies that have used Puppet and Chocolatey report significant time savings, reduced downtime, increased reliability, and a reduction in software management time by up to 90%. The document advocates that organizations invest now in automation to gain these benefits and a competitive advantage in the new normal of IT.
Bridging the Gap: from Data Science to Production
A recent but quite common observation in industry is that although there is an overall high adoption of data science, many companies struggle to get it into production. Huge teams of well-payed data scientists often present one fancy model after the other to their managers but their proof of concepts never manifest into something business relevant. The frustration grows on both sides, managers and data scientists.
In my talk I elaborate on the many reasons why data science to production is such a hard nut to crack. I start with a taxonomy of data use cases in order to easier assess technical requirements. Based thereon, my focus lies on overcoming the two-language-problem which is Python/R loved by data scientists vs. the enterprise-established Java/Scala. From my project experiences I present three different solutions, namely 1) migrating to a single language, 2) reimplementation and 3) usage of a framework. The advantages and disadvantages of each approach is presented and general advices based on the introduced taxonomy is given.
Additionally, my talk also addresses organisational as well as problems in quality assurance and deployment. Best practices and further references are presented on a high-level in order to cover all facets of data science to production.
With my talk I hope to convey the message that breakdowns on the road from data science to production are rather the rule than the exception, so you are not alone. At the end of my talk, you will have a better understanding of why your team and you are struggling and what to do about it.
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Our DevSecOps meetup:
https://www.meetup.com/DevSecOps-NoVA
The Handbook:
https://tech.gsa.gov/guides/dev_sec_ops_guide/
Our speakers group:
https://handbook.tts.gsa.gov/tech-portfolio/
His team's areas of responsibility:
https://digital.gov/services/
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Nadine Schöne, Dataiku. The Complete Data Value Chain in a Nutshell
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Speech Overview:
How can you get the most out of your data – while staying flexible in your choice of infrastructure and without having to integrate a multitude of tools for the different personas involved? Maximizing the value you get out of your data is a necessity today. Looking at the whole picture as well as careful planning are the key for success. We will have a look at the complete data value chain from end to end: from the data stores, collaboration features, data preparation, visualization and automation capabilities, and external compute to scheduling, operationalization, monitoring and security.
Rsqrd AI: From R&D to ROI of AI
Machine learning models are difficult to operationalize at scale due to infrastructure challenges like supporting different frameworks and languages, managing models through versioning and reproducibility, and deploying models at large scale. Most organizations struggle with successfully moving projects from proof-of-concept to production as lack of process, incentives, skills, champions, and appropriate technology impede operationalization. Adopting practices like integrating engineering and data science teams, defining clear production criteria, and choosing infrastructure-agnostic platforms can help organizations realize value from machine learning by addressing these barriers to operationalization.
Agile Secure Development
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Gluon Consulting - Specialized Software Development for Finance
Gluon offers specialized software development and consultancy services for fintechs and financial services SMEs.
Our unique delivery model combines London based analysis, project management and QA with delivery from our team of talented developers in the Philippines.
Results, service and care levels are those you would expect from the best front-office tech teams in London, New York and Silicon Valley. Costs? Refreshingly close to your average total outsourcing initiative, but without the risk and the hassle.
OVER 40 YEARS OF EXPERIENCE IN LONDON
Our core team members are veteran software consultants and enterprise architects with a combined 40 years of experience writing high-performance, robust and scalable software for some of the world's largest financial organizations and independent technology providers.
TECH BEFORE FINTECH
In our journeys, we built trading and portfolio analytics platforms, distributed and parallel calculation and workflow engines, high volume messaging middleware, dashboards with advanced interaction and visualization features. We did numerical and statistical computing, machine learning and semantic processing.
We produced real-time, interactive scenario analysis for one of the most profitable trading desks on the planet. We helped e-commerce and brokerage companies to scale their operations without breaking a sweat. We contributed to pioneering robotics and field automation platforms before IoT and event streams were cool.
For more info, please contact
Dennis Cabarroguis
dennis.cabarroguis@gluonconsulting.com
Building a Software Chain of Custody: A Guide for CTOs, CIOs, and Enterprise ...
For most of us, compliance audits are painful processes that interfere with our ability to do our job – building and delivering software – and steal time and resources away from that next great innovation. Until now.
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Learn how a Software Chain of Custody helps:
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InfoSec teams and auditors instantly get the reports they need so they can quickly approve releases
Big data and other buzzwords
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Group 3 slide presentation
The document compares the leading data visualization tools Tableau, Power BI, and Qlik. It reviews the strengths, weaknesses, and unique features of each tool. Tableau is seen as the gold standard but is very expensive. Power BI is easy to use and affordable but best for Excel users. Qlik Sense has powerful scripting but a confusing licensing model. The document recommends tools based on cost, capabilities, and intended users.
On the road to Engineering excellence
The document discusses improving engineering excellence through better tools and processes. It outlines goals of boosting developer productivity, driving innovation, and becoming a top performing organization. Metrics from the DORA study like deployment frequency, lead time, MTTR, and change failure rate are discussed. Challenges implementing these metrics include cultural resistance, lack of tooling, data quality issues, and misuse of metrics. The team's journey moving to a new platform with Kubernetes, standardized services, and automated deployments is described. Using DevLake and Backstage to provide a unified view of metrics and improve the developer experience is part of the vision.
Transforming to OpenStack: a sample roadmap to DevOps
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Last summit I had the pleasure to present a talk which encountered some success "Are enterprise ready for the OpenStack transformation?" (also published on SlideShare) . This talk is a follow up on what are the best practices that are successful in operating the transformation. We will first focus on identifying the right use cases for a generic enterprise, then define a roadmap with an organisational and a technical track, to finish with the definition what would be our success criterias for our group. This will happen as a workshop summary based on the multiple engagements eNovance has been delivering over the past 2 years.
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High-performing engineering teams regularly dedicate time on measuring the performance & quality of the systems and applications they’re building or on measuring & improving the various aspects of the development lifecycle. High-performing product companies are also data-driven when it comes to measuring the impact of new features & products in terms of business KPIs and Northstar metrics.
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Code to Cloud: Three Trends for Faster, Safer Continuous Delivery
You’re faced with the business imperative to deliver value to your customers faster, with less risk, and at enterprise scale. But where do you start? There are so many approaches and products for implementing continuous delivery (CD) of modern apps.
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● turning monitoring into automated action
● developer experience at the heart
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This webinar is especially relevant for those who:
● think full software delivery automation is a pipe dream.
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Speakers:
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
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Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
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Scaling Data Science: Engineering a Platform
- 1. Engineering a Platform Scaling DataScience
- 2. Who is DataScience? 2 ● Do you need… ● …insights about your data? ● …to hire or train data scientists to provide actionable insights? ● …a platform for consuming cutting-edge data science and publishing within your org, without having data scientists doing ops work? ● …to focus your data scientists on your core business, while we provide models for LTV, pricing, retention and more? ● Visit www.datascience.com!
- 3. Two Primary Challenges 3 ● Rapid Team Growth ● Team member onboarding requires a low-risk, standardized toolchain. Time to first contribution is measured in hours, not days or weeks. ● Dynamic Tooling Landscape ● Best-of-breed data tools are always changing. Our culture and platform encourage experimentation and evaluation of new tools and techniques.
- 4. Rapid Onboarding 4 ● A packaged virtual development environment ● No wrestling with complex system dependencies and version compatibilities ● A clean starting point to quickly retreat at any time ● Monitoring and diagnostics ● Scripted automation for customizing per user
- 5. Rapid Onboarding 5 ● Upgrades are vetted in advance prior to wide release ● Continuous integration provides automated feedback ● Group chat pushes institutional knowledge out into searchable, company- wide record. ● A culture of sharing and demonstration
- 6. Dynamic Landscape ● Configuration management to quickly compose systems based on requirements ● Software tools are constantly evolving. A robust virtual environment promotes experimentation and iteration. ● Think in terms of categories of tools not specific techs. 6
- 7. Dynamic Landscape ● Version control: track changes to analysis over time. promote reproducibility ● Automated testing: An engineering approach to analysis quality ● Integrated publishing: A publishing workflow that closely follows the underlying analysis 7
- 11. Thank you.