Build your own data challenge, or just organize team work
•
0 likes•584 views
We have open sourced the toolkit behind www.ramp.studio and all the starting kits. You can use rampwf to build your own predictive workflows, organize workflow building and optimization within an internal data science team, and submit the kit to us if you want to run a code submission data challenge. https://www.ramp.studio https://github.com/paris-saclay-cds/ramp-workflow https://github.com/ramp-kits https://ramp-studio.slack.com
Report
Share
Report
Share
Download to read offline
Recommended
RAMP: Collaborative challenge with code submission
We describe the RAMP, a rapid data challenge format and tool we developed at the Paris-Saclay Center for Data Science.
What is wrong with data challenges
This document summarizes discussions from a workshop on data science challenges. It notes both benefits and limitations of crowdsourced data challenges, including lack of direct access to solutions and limited incentive for collaboration. It then describes the development of a new format called Rapid Analytics and Model Prototyping (RAMP) that aims to address these limitations by facilitating direct access to code and data, and incentivizing diversity and collaboration. RAMP was created based on experiences with the Higgs Boson Machine Learning Challenge and drawing from literature on open innovation.
The Paris-Saclay Center for Data Science
My slides from the MASTODONS (big data) workshop of the CNRS.
http://www.cnrs.fr/mi/spip.php?article631&lang=fr
On defining and managing the data science ecosystem at Université Paris-Saclay. Challenges and tools.
The systemic challenges in data science initiatives (and some solutions)
The document discusses the challenges of data science initiatives and some solutions implemented by the Center for Data Science in Paris-Saclay. It outlines three tools - Rapid Analytics and Model Prototyping (RAMPs), Data Challenges, and Training Sprints - used to promote collaboration between domain scientists and data scientists. It provides examples showing improvements in predictive accuracy and errors achieved through these tools on problems involving particle classification, weather prediction, and more.
RAMP Data Challenge
This document discusses the RAMP (Rapid Analytics and Model Prototyping) program, which hosts data science competitions that allow participants to submit code. RAMP aims to enable collaborative prototyping, teaching, and managing the data science process. It has hosted many challenges across various domains. The document outlines RAMP's goals, examples of past and upcoming challenges, and tools for defining and testing predictive workflows that are submitted as part of the challenges.
Deep learning and the systemic challenges of data science initiatives
The document discusses challenges and tools for data science initiatives. It describes how data science challenges are often organizational rather than technological due to issues like misaligned incentives between domain scientists and data scientists. It outlines tools developed by the Center for Data Science in Paris-Saclay to address these challenges, including design and innovation workshops to develop projects, rapid analytics and model prototyping sessions, and an IT platform to link and map data sets. The goal is to better facilitate collaboration between domain experts, data scientists, and software engineers.
Frankfurt Big Data Lab & Refugee Projeect
The Frankfurt Big Data Lab carries out research in big data and data analytics from the perspective of information systems and computer science. The lab is located in Frankfurt and targets research applications for both industry and academia. Current research areas include big data management technologies, data analytics, graph databases/linked open data, and using big data for social good. One project focuses on using data to help with the inclusion of refugees in Frankfurt.
2016-11-21 BioExcel Workflows and Pipelines Interest Group
BioExcel Workflows and Pipelines Interest Group
http://bioexcel.eu/interest-groups/
Stian Soiland-Reyes
http://orcid.org/0000-0001-9842-9718
Source: http://slides.com/soilandreyes/2016-11-21-bioexcel-workflows-ig/
Presented at 2016-10-21 BioExcel Workflow Training, BSC, Barcelona
http://bioexcel.eu/events/bioexcel-workflow-training-for-computational-biomolecular-research/
Recommended
RAMP: Collaborative challenge with code submission
We describe the RAMP, a rapid data challenge format and tool we developed at the Paris-Saclay Center for Data Science.
What is wrong with data challenges
This document summarizes discussions from a workshop on data science challenges. It notes both benefits and limitations of crowdsourced data challenges, including lack of direct access to solutions and limited incentive for collaboration. It then describes the development of a new format called Rapid Analytics and Model Prototyping (RAMP) that aims to address these limitations by facilitating direct access to code and data, and incentivizing diversity and collaboration. RAMP was created based on experiences with the Higgs Boson Machine Learning Challenge and drawing from literature on open innovation.
The Paris-Saclay Center for Data Science
My slides from the MASTODONS (big data) workshop of the CNRS.
http://www.cnrs.fr/mi/spip.php?article631&lang=fr
On defining and managing the data science ecosystem at Université Paris-Saclay. Challenges and tools.
The systemic challenges in data science initiatives (and some solutions)
The document discusses the challenges of data science initiatives and some solutions implemented by the Center for Data Science in Paris-Saclay. It outlines three tools - Rapid Analytics and Model Prototyping (RAMPs), Data Challenges, and Training Sprints - used to promote collaboration between domain scientists and data scientists. It provides examples showing improvements in predictive accuracy and errors achieved through these tools on problems involving particle classification, weather prediction, and more.
RAMP Data Challenge
This document discusses the RAMP (Rapid Analytics and Model Prototyping) program, which hosts data science competitions that allow participants to submit code. RAMP aims to enable collaborative prototyping, teaching, and managing the data science process. It has hosted many challenges across various domains. The document outlines RAMP's goals, examples of past and upcoming challenges, and tools for defining and testing predictive workflows that are submitted as part of the challenges.
Deep learning and the systemic challenges of data science initiatives
The document discusses challenges and tools for data science initiatives. It describes how data science challenges are often organizational rather than technological due to issues like misaligned incentives between domain scientists and data scientists. It outlines tools developed by the Center for Data Science in Paris-Saclay to address these challenges, including design and innovation workshops to develop projects, rapid analytics and model prototyping sessions, and an IT platform to link and map data sets. The goal is to better facilitate collaboration between domain experts, data scientists, and software engineers.
Frankfurt Big Data Lab & Refugee Projeect
The Frankfurt Big Data Lab carries out research in big data and data analytics from the perspective of information systems and computer science. The lab is located in Frankfurt and targets research applications for both industry and academia. Current research areas include big data management technologies, data analytics, graph databases/linked open data, and using big data for social good. One project focuses on using data to help with the inclusion of refugees in Frankfurt.
2016-11-21 BioExcel Workflows and Pipelines Interest Group
BioExcel Workflows and Pipelines Interest Group
http://bioexcel.eu/interest-groups/
Stian Soiland-Reyes
http://orcid.org/0000-0001-9842-9718
Source: http://slides.com/soilandreyes/2016-11-21-bioexcel-workflows-ig/
Presented at 2016-10-21 BioExcel Workflow Training, BSC, Barcelona
http://bioexcel.eu/events/bioexcel-workflow-training-for-computational-biomolecular-research/
NYC_2016_slides
This document provides an overview of a machine learning workshop including tutorials on decision tree classification for flight delays, clustering news articles with k-means clustering, and collaborative filtering for movie recommendations using Spark. The tutorials demonstrate loading and preparing data, training models, evaluating performance, and making predictions or recommendations. They use Spark MLlib and are run in Apache Zeppelin notebooks.
Slack EduRooms
This document provides an overview of a session on SlackEduRooms and xAPI integration. The session covers the fundamentals of Slack and EduRooms, how to design learning environments in Slack, integrating data and xAPI, and the use of AI. It also describes a case study conducted with military personnel using a Slack EduRoom to evaluate design considerations. Key takeaways are to focus on the learning experience for students, moderate the living Slack environment, and use growth data as a metric of success.
AMP Camp 5 Intro
The AMPLab at UC Berkeley was launched in 2011 with 6-year funding from the NSF and DARPA to do research at the intersection of machine learning, large-scale distributed systems, and data management. It has around 65 students, faculty and staff working on these topics. Some of its key projects include Apache Spark and other open-source big data tools. The lab aims to build a unified platform for analytics that can handle different types of algorithms and data at large scale. It also runs training programs like AMPCamp to disseminate its research.
Sharing massive data analysis: from provenance to linked experiment reports
The document discusses scientific workflows, provenance, and linked data. It covers:
1) Scientific workflows can automate data analysis at scale, abstract complex processes, and capture provenance for transparency.
2) Provenance represents the origin and history of data and can be represented using standards like PROV. It allows reasoning about how results were produced.
3) Capturing and publishing provenance as linked open data can help make scientific results more reusable and queryable, but challenges remain around multi-site studies and producing human-readable reports.
Sharing - Collecting our DAH Thoughts
This document discusses various tools and frameworks for data visualization and digital humanities projects. It provides brief overviews of GraphViz, R Programming Language, JavaScript InfoVis Toolkit, Protovis, D3, Processing, Tableau, Prefuse, Gephi, and Exhibit. It also discusses points of departure for digital humanities work and considerations for "selling" a digital humanities project, including having a concrete idea, finding examples of successful similar projects, getting relevant facts, and taking action to start a project.
How a Social Knowledge Graph Improves Remote Working by Capturing Context fro...
Neo4j Webinar 23 April, 2020
Neo4j has always been a geographically distributed company. Our employee count of 300+ people is spread over more than 20 countries. Consequently, remote collaboration is in our DNA.
A Social Knowledge Graph can extract topics or moods from instant messaging to improve information sharing. It can also identify “lonely nodes” in times of remote working.
In our latest webinar, we will demonstrate how we at Neo4j have leveraged our own technology to improve the efficiency of remote collaboration and avoid "lonely nodes". The webinar illustrates how instant messaging conversations (in this case from Slack) are used to analyze collaboration. And, finally, we will explain how companies can roll out a similar solution in less than two months.
Train, explain, acclaim. Build a good model in three steps
New trends in modelling with Explainable Artificial Intelligence and Automated Machine Learning. Presented by AI & NLP workshop days 2019.
Using hadoop for big data
This document provides an agenda for a workshop on Hadoop and Spark. It begins with background on big data, analytics, and data science. It then outlines workshops that will be conducted on installing and using Hadoop and Spark for tasks like word counting. Real-world use cases for Hadoop are also discussed. The document concludes by discussing trends in Hadoop and Spark.
Fiducia & GAD IT AG: From Fraud Detection to Big Data Platform: Bringing Hado...
The document summarizes the experience of Fiducia & GAD IT AG in bringing Hadoop to their enterprise for fraud detection purposes. They faced challenges of handling high volumes of transaction data in real-time for model-based fraud evaluation. Their solution was to implement an Apache Hadoop platform to address the velocity, variety and volume of transaction data. Key lessons learned included that Hadoop is a complex platform requiring new skills, ongoing support is critical, and standard tasks can generate significant effort. Their blueprint recommends starting with a simple use case, few components, agile development, and budgeting time for training and bug fixing when establishing a big data platform.
Horton+Pruim+Kaplan_MOSAIC-StudentGuide.pdf Nicholas J. .docx
Horton+Pruim+Kaplan_MOSAIC-StudentGuide.pdf
Nicholas J. Horton
Randall Pruim
Daniel T. Kaplan
A Student's
Guide to
R
Project MOSAIC
2 horton, kaplan, pruim
Copyright (c) 2015 by Nicholas J. Horton, Randall
Pruim, & Daniel Kaplan.
Edition 1.2, November 2015
This material is copyrighted by the authors under a
Creative Commons Attribution 3.0 Unported License.
You are free to Share (to copy, distribute and transmit
the work) and to Remix (to adapt the work) if you
attribute our work. More detailed information about
the licensing is available at this web page: http:
//www.mosaic-web.org/go/teachingRlicense.html.
Cover Photo: Maya Hanna.
http://www.mosaic-web.org/go/teachingRlicense.html
http://www.mosaic-web.org/go/teachingRlicense.html
Contents
1 Introduction 13
2 Getting Started with RStudio 15
3 One Quantitative Variable 27
4 One Categorical Variable 39
5 Two Quantitative Variables 45
6 Two Categorical Variables 55
7 Quantitative Response, Categorical Predictor 61
8 Categorical Response, Quantitative Predictor 69
9 Survival Time Outcomes 73
4 horton, kaplan, pruim
10 More than Two Variables 75
11 Probability Distributions & Random Variables 83
12 Power Calculations 89
13 Data Management 93
14 Health Evaluation (HELP) Study 107
15 Exercises and Problems 111
16 Bibliography 115
17 Index 117
About These Notes
We present an approach to teaching introductory and in-
termediate statistics courses that is tightly coupled with
computing generally and with R and RStudio in particular.
These activities and examples are intended to highlight
a modern approach to statistical education that focuses
on modeling, resampling based inference, and multivari-
ate graphical techniques. A secondary goal is to facilitate
computing with data through use of small simulation
studies and appropriate statistical analysis workflow. This
follows the philosophy outlined by Nolan and Temple
Lang1. The importance of modern computation in statis- 1 D. Nolan and D. Temple Lang.
Computing in the statistics
curriculum. The American
Statistician, 64(2):97–107, 2010
tics education is a principal component of the recently
adopted American Statistical Association’s curriculum
guidelines2.
2 ASA Undergraduate Guide-
lines Workgroup. 2014 cur-
riculum guidelines for under-
graduate programs in statisti-
cal science. Technical report,
American Statistical Associa-
tion, November 2014. http:
//www.amstat.org/education/
curriculumguidelines.cfm
Throughout this book (and its companion volumes),
we introduce multiple activities, some appropriate for
an introductory course, others suitable for higher levels,
that demonstrate key concepts in statistics and modeling
while also supporting the core material of more tradi-
tional courses.
A Work in Progress
Caution!
Despite our best efforts, you
WILL find bugs both in this
document and in our code.
Please let us know when y ...
Driving Enterprise Adoption: Tragedies, Triumphs and Our NEXT
Standard Bank is a leading South African bank with a vision to be the leading financial services organization in and for Africa. We will share our vision, greatest challenges, and most valuable lessons learned on our journey towards enterprise adoption of a big data strategy.
This includes our implementation of: a multi-tenant enterprise data lake, a real time streaming capability, appropriate data management and governance principles, a data science workbench, and a process for model productionisation to support data science teams across the Group and across Africa and Europe.
Speakers
Zakeera Mahomen, Standard Bank, Big Data Practice Lead
Kristel Sampson, Standard Bank, Platform Lead
FAIRDOM - FAIR Asset management and sharing experiences in Systems and Synthe...
Over the past 5 years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs and so forth. Don’t stop reading. Data management isn’t likely to win anyone a Nobel prize. But publications should be supported and accompanied by data, methods, procedures, etc. to assure reproducibility of results. Funding agencies expect data (and increasingly software) management retention and access plans as part of the proposal process for projects to be funded. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems Biology demands the interlinking and exchange of assets and the systematic recording
of metadata for their interpretation.
The FAIR Guiding Principles for scientific data management and stewardship (http://www.nature.com/articles/sdata201618) has been an effective rallying-cry for EU and USA Research Infrastructures. FAIRDOM (Findable, Accessible, Interoperable, Reusable Data, Operations and Models) Initiative has 8 years of experience of asset sharing and data infrastructure ranging across European programmes (SysMO and EraSysAPP ERANets), national initiatives (de.NBI, German Virtual Liver Network, UK SynBio centres) and PI's labs. It aims to support Systems and Synthetic Biology researchers with data and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety.
This talk will use the FAIRDOM Initiative to discuss the FAIR management of data, SOPs, and models for Sys Bio, highlighting the challenges of and approaches to sharing, credit, citation and asset infrastructures in practice. I'll also highlight recent experiments in affecting sharing using behavioural interventions.
http://www.fair-dom.org
http://www.fairdomhub.org
http://www.seek4science.org
Presented at COMBINE 2016, Newcastle, 19 September.
http://co.mbine.org/events/COMBINE_2016
Big Data LDN 2018: HOW RANK GAMING PRODUCTIONISED & AUTOMATED THE MANAGEMENT ...
Date: 13th November 2018
Location: Data Ops Theatre
Time: 13:50 - 14:20
Speakers: Terry McCann, Adatis & Chris Conroy, Rank Group
About: Rank Group approached Adatis Consulting Ltd in 2017 to help tackle a key issue their data science team were encountering – “How do you to gracefully transition from one machine learning model to another as models are retrained and rewritten?”
Rank Group is the owner of many popular gaming brands in the UK, including Grosvenor Casinos and Mecca Bingo. Rank use Machine Learning to optimise and influence business decisions across their enterprise. Models are deployed to identify customers churn, improve cross sale, enhance retention and most importantly to identify customers who are at risk of having a gambling addiction. These models are constantly being evaluated and retrained as gambling habits change and new games are introduced.
London-based advanced analytics consultancy Adatis implemented a new advanced Machine Learning Model Management service based on the "Rendezvous" Architecture created by Ellen Friedman and Ted Dunning. Rendezvous handles the distribution of a single request to multiple models, scoring all in parallel, then decides on the most appropriate output to return. This is a massively scalable, flexible architecture that solves one of the key problems encountered by Data Science teams today. In this session we will look at the original problem and the architecture which was used to solve it.
Learning in the Modern Workplace
The document discusses learning in the modern workplace. It notes that learning habits are changing, with people participating in online communities, using online tools to solve problems, and keeping up to date through knowledge flows. The key features of learning on the web are described as occurring in short bursts, on demand, on mobile devices, socially, continuously, and autonomously. Various internal and external resources that individuals use for learning are illustrated. The presentation recommends ways for companies to modernize workplace learning, such as by creating short, appealing content; running learning campaigns; making learning available on demand and for any device; encouraging social content; curating and coordinating learning opportunities; supporting everyday learning in teams; and encouraging self-improvement through individual learning
Open science in cognitive modeling
Talk given at the International Conference on Cognitive Modelling, University of Groningen on 10 April 2015.#
CC0 - Public Domain
To the extent possible under law, Caspar Addyman has waived all copyright and related or neighboring rights to Open science in cognitive modeling. This work is published from: United Kingdom.
eROSA Stakeholder WS1: Cirad - Dataverse: A platform to manage, work, share a...
Presentation by Sophie Fortuno delivered at the 1st eROSA Stakeholder Workshop in Montpellier, 6/7/2017
Digital Science: Towards the executable paper
The science performed in Astronomy is digital science, from observing proposals to final publication, including data and software used: each of the elements and actions involved in the scientific output could be recorded in electronic form.
This fact does not prevent the final outcome of an experiment is still difficult to reproduce. An exhaustive process of documentation can be long, tedious, where access to all the resources must be granted, and after all, the repeatability of results is not even guaranteed. At the same time, we have access to a wealth of files, observational data and publications that could be used more efficiently with a better visibility of the scientific production, avoiding duplication of effort and reinvention.
ODSC West 2022 – Kitbashing in ML
We're in the age of toolbox Machine Learning. What should you know about how to use emerging technologies like pre-trained models, large-language models, fine-tuning, and MLOps solutions to quickly and effectively build AI products.
Beyond instructional design: how performance support is revolutionizng e-lear...
The document discusses how performance support is revolutionizing e-learning by focusing on providing resources that help users complete tasks rather than taking courses or tests. It outlines how learning habits are changing to be more on-demand, in short bursts across different devices. This means instructional designers need to create small, flexible resources in different formats. Examples of different types of performance support resources like checklists, step-by-step guides, videos and infographics are provided. The performance support design process involves analyzing the performance problem, designing the optimal resource format, and making it available for on-demand use. Success is measured through metrics like usage and evidence of performance improvement.
Ultra Fast Deep Learning in Hybrid Cloud Using Intel Analytics Zoo & Alluxio
Alluxio Global Online Meetup
Apr 23, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Jiao (Jennie) Wang, Intel
Tsai Louie, Intel
Bin Fan, Alluxio
Today, many people run deep learning applications with training data from separate storage such as object storage or remote data centers. This presentation will demo the Intel Analytics Zoo + Alluxio stack, an architecture that enables high performance while keeping cost and resource efficiency balanced without network being I/O bottlenecked.
Intel Analytics Zoo is a unified data analytics and AI platform open-sourced by Intel. It seamlessly unites TensorFlow, Keras, PyTorch, Spark, Flink, and Ray programs into an integrated pipeline, which can transparently scale from a laptop to large clusters to process production big data. Alluxio, as an open-source data orchestration layer, accelerates data loading and processing in Analytics Zoo deep learning applications.
This talk, we will go over:
- What is Analytics Zoo and how it works
- How to run Analytics Zoo with Alluxio in deep learning applications
- Initial performance benchmark results using the Analytics Zoo + Alluxio stack
Data-driven hypothesis generation using deep neural nets
1. Machine learning in science
- induction, inference, simulation, generation
2. Stretching the scientific method
- the p-value controversy and the problem of automated hypothesis generation
3. Generative models and novelty generation
Model-based reinforcement learning and self-driving engineering systems
DeepLearning summer school, Gran Canaria 2021
https://www.youtube.com/watch?v=B5UBJiwL_D0
I introduce reinforcement learning from a model-based perspective. In this paradigm the core of the algorithm is the system model: a multivariate generative (probabilistic) time-series predictor. The system model is combined with online planning and/or learning model-free agents on the system model. The course is designed for students with basic classical machine learning knowledge. My goal is to open up an interesting perspective while also giving you useful tools to tackle practical applications.
The main motivation of the course is learning and improving policies to control engineering systems (autopilots or "self-driving" systems). Unlike popular game benchmarks, these systems are low-dimensional (~10s to ~100s of dimensions) with rewards coming continuously or with a short delay. On the other hand, they are physical, slow, and system access is usually extremely restricted. The focus and the main algorithmic challenge is thus not representation learning and handling sparse rewards (as in games), rather learning robust system models on extremely small (100s or 1000s of time steps) non-iid data. The perspective is also an interesting extension of the classical supervised learning paradigm, in which functions are learned in a single shot on data generated (sampled and labeled) by an imaginary oracle. In the real world, supervised models are usually re-learned often, on non-iid data generated by a process which we partially control. The questions that we will ponder on (exploration, distribution shift, non-iid data) may thus also interest students planning to work on supervised machine learning in the real world.
More Related Content
Similar to Build your own data challenge, or just organize team work
NYC_2016_slides
This document provides an overview of a machine learning workshop including tutorials on decision tree classification for flight delays, clustering news articles with k-means clustering, and collaborative filtering for movie recommendations using Spark. The tutorials demonstrate loading and preparing data, training models, evaluating performance, and making predictions or recommendations. They use Spark MLlib and are run in Apache Zeppelin notebooks.
Slack EduRooms
This document provides an overview of a session on SlackEduRooms and xAPI integration. The session covers the fundamentals of Slack and EduRooms, how to design learning environments in Slack, integrating data and xAPI, and the use of AI. It also describes a case study conducted with military personnel using a Slack EduRoom to evaluate design considerations. Key takeaways are to focus on the learning experience for students, moderate the living Slack environment, and use growth data as a metric of success.
AMP Camp 5 Intro
The AMPLab at UC Berkeley was launched in 2011 with 6-year funding from the NSF and DARPA to do research at the intersection of machine learning, large-scale distributed systems, and data management. It has around 65 students, faculty and staff working on these topics. Some of its key projects include Apache Spark and other open-source big data tools. The lab aims to build a unified platform for analytics that can handle different types of algorithms and data at large scale. It also runs training programs like AMPCamp to disseminate its research.
Sharing massive data analysis: from provenance to linked experiment reports
The document discusses scientific workflows, provenance, and linked data. It covers:
1) Scientific workflows can automate data analysis at scale, abstract complex processes, and capture provenance for transparency.
2) Provenance represents the origin and history of data and can be represented using standards like PROV. It allows reasoning about how results were produced.
3) Capturing and publishing provenance as linked open data can help make scientific results more reusable and queryable, but challenges remain around multi-site studies and producing human-readable reports.
Sharing - Collecting our DAH Thoughts
This document discusses various tools and frameworks for data visualization and digital humanities projects. It provides brief overviews of GraphViz, R Programming Language, JavaScript InfoVis Toolkit, Protovis, D3, Processing, Tableau, Prefuse, Gephi, and Exhibit. It also discusses points of departure for digital humanities work and considerations for "selling" a digital humanities project, including having a concrete idea, finding examples of successful similar projects, getting relevant facts, and taking action to start a project.
How a Social Knowledge Graph Improves Remote Working by Capturing Context fro...
Neo4j Webinar 23 April, 2020
Neo4j has always been a geographically distributed company. Our employee count of 300+ people is spread over more than 20 countries. Consequently, remote collaboration is in our DNA.
A Social Knowledge Graph can extract topics or moods from instant messaging to improve information sharing. It can also identify “lonely nodes” in times of remote working.
In our latest webinar, we will demonstrate how we at Neo4j have leveraged our own technology to improve the efficiency of remote collaboration and avoid "lonely nodes". The webinar illustrates how instant messaging conversations (in this case from Slack) are used to analyze collaboration. And, finally, we will explain how companies can roll out a similar solution in less than two months.
Train, explain, acclaim. Build a good model in three steps
New trends in modelling with Explainable Artificial Intelligence and Automated Machine Learning. Presented by AI & NLP workshop days 2019.
Using hadoop for big data
This document provides an agenda for a workshop on Hadoop and Spark. It begins with background on big data, analytics, and data science. It then outlines workshops that will be conducted on installing and using Hadoop and Spark for tasks like word counting. Real-world use cases for Hadoop are also discussed. The document concludes by discussing trends in Hadoop and Spark.
Fiducia & GAD IT AG: From Fraud Detection to Big Data Platform: Bringing Hado...
The document summarizes the experience of Fiducia & GAD IT AG in bringing Hadoop to their enterprise for fraud detection purposes. They faced challenges of handling high volumes of transaction data in real-time for model-based fraud evaluation. Their solution was to implement an Apache Hadoop platform to address the velocity, variety and volume of transaction data. Key lessons learned included that Hadoop is a complex platform requiring new skills, ongoing support is critical, and standard tasks can generate significant effort. Their blueprint recommends starting with a simple use case, few components, agile development, and budgeting time for training and bug fixing when establishing a big data platform.
Horton+Pruim+Kaplan_MOSAIC-StudentGuide.pdf Nicholas J. .docx
Horton+Pruim+Kaplan_MOSAIC-StudentGuide.pdf
Nicholas J. Horton
Randall Pruim
Daniel T. Kaplan
A Student's
Guide to
R
Project MOSAIC
2 horton, kaplan, pruim
Copyright (c) 2015 by Nicholas J. Horton, Randall
Pruim, & Daniel Kaplan.
Edition 1.2, November 2015
This material is copyrighted by the authors under a
Creative Commons Attribution 3.0 Unported License.
You are free to Share (to copy, distribute and transmit
the work) and to Remix (to adapt the work) if you
attribute our work. More detailed information about
the licensing is available at this web page: http:
//www.mosaic-web.org/go/teachingRlicense.html.
Cover Photo: Maya Hanna.
http://www.mosaic-web.org/go/teachingRlicense.html
http://www.mosaic-web.org/go/teachingRlicense.html
Contents
1 Introduction 13
2 Getting Started with RStudio 15
3 One Quantitative Variable 27
4 One Categorical Variable 39
5 Two Quantitative Variables 45
6 Two Categorical Variables 55
7 Quantitative Response, Categorical Predictor 61
8 Categorical Response, Quantitative Predictor 69
9 Survival Time Outcomes 73
4 horton, kaplan, pruim
10 More than Two Variables 75
11 Probability Distributions & Random Variables 83
12 Power Calculations 89
13 Data Management 93
14 Health Evaluation (HELP) Study 107
15 Exercises and Problems 111
16 Bibliography 115
17 Index 117
About These Notes
We present an approach to teaching introductory and in-
termediate statistics courses that is tightly coupled with
computing generally and with R and RStudio in particular.
These activities and examples are intended to highlight
a modern approach to statistical education that focuses
on modeling, resampling based inference, and multivari-
ate graphical techniques. A secondary goal is to facilitate
computing with data through use of small simulation
studies and appropriate statistical analysis workflow. This
follows the philosophy outlined by Nolan and Temple
Lang1. The importance of modern computation in statis- 1 D. Nolan and D. Temple Lang.
Computing in the statistics
curriculum. The American
Statistician, 64(2):97–107, 2010
tics education is a principal component of the recently
adopted American Statistical Association’s curriculum
guidelines2.
2 ASA Undergraduate Guide-
lines Workgroup. 2014 cur-
riculum guidelines for under-
graduate programs in statisti-
cal science. Technical report,
American Statistical Associa-
tion, November 2014. http:
//www.amstat.org/education/
curriculumguidelines.cfm
Throughout this book (and its companion volumes),
we introduce multiple activities, some appropriate for
an introductory course, others suitable for higher levels,
that demonstrate key concepts in statistics and modeling
while also supporting the core material of more tradi-
tional courses.
A Work in Progress
Caution!
Despite our best efforts, you
WILL find bugs both in this
document and in our code.
Please let us know when y ...
Driving Enterprise Adoption: Tragedies, Triumphs and Our NEXT
Standard Bank is a leading South African bank with a vision to be the leading financial services organization in and for Africa. We will share our vision, greatest challenges, and most valuable lessons learned on our journey towards enterprise adoption of a big data strategy.
This includes our implementation of: a multi-tenant enterprise data lake, a real time streaming capability, appropriate data management and governance principles, a data science workbench, and a process for model productionisation to support data science teams across the Group and across Africa and Europe.
Speakers
Zakeera Mahomen, Standard Bank, Big Data Practice Lead
Kristel Sampson, Standard Bank, Platform Lead
FAIRDOM - FAIR Asset management and sharing experiences in Systems and Synthe...
Over the past 5 years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs and so forth. Don’t stop reading. Data management isn’t likely to win anyone a Nobel prize. But publications should be supported and accompanied by data, methods, procedures, etc. to assure reproducibility of results. Funding agencies expect data (and increasingly software) management retention and access plans as part of the proposal process for projects to be funded. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems Biology demands the interlinking and exchange of assets and the systematic recording
of metadata for their interpretation.
The FAIR Guiding Principles for scientific data management and stewardship (http://www.nature.com/articles/sdata201618) has been an effective rallying-cry for EU and USA Research Infrastructures. FAIRDOM (Findable, Accessible, Interoperable, Reusable Data, Operations and Models) Initiative has 8 years of experience of asset sharing and data infrastructure ranging across European programmes (SysMO and EraSysAPP ERANets), national initiatives (de.NBI, German Virtual Liver Network, UK SynBio centres) and PI's labs. It aims to support Systems and Synthetic Biology researchers with data and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety.
This talk will use the FAIRDOM Initiative to discuss the FAIR management of data, SOPs, and models for Sys Bio, highlighting the challenges of and approaches to sharing, credit, citation and asset infrastructures in practice. I'll also highlight recent experiments in affecting sharing using behavioural interventions.
http://www.fair-dom.org
http://www.fairdomhub.org
http://www.seek4science.org
Presented at COMBINE 2016, Newcastle, 19 September.
http://co.mbine.org/events/COMBINE_2016
Big Data LDN 2018: HOW RANK GAMING PRODUCTIONISED & AUTOMATED THE MANAGEMENT ...
Date: 13th November 2018
Location: Data Ops Theatre
Time: 13:50 - 14:20
Speakers: Terry McCann, Adatis & Chris Conroy, Rank Group
About: Rank Group approached Adatis Consulting Ltd in 2017 to help tackle a key issue their data science team were encountering – “How do you to gracefully transition from one machine learning model to another as models are retrained and rewritten?”
Rank Group is the owner of many popular gaming brands in the UK, including Grosvenor Casinos and Mecca Bingo. Rank use Machine Learning to optimise and influence business decisions across their enterprise. Models are deployed to identify customers churn, improve cross sale, enhance retention and most importantly to identify customers who are at risk of having a gambling addiction. These models are constantly being evaluated and retrained as gambling habits change and new games are introduced.
London-based advanced analytics consultancy Adatis implemented a new advanced Machine Learning Model Management service based on the "Rendezvous" Architecture created by Ellen Friedman and Ted Dunning. Rendezvous handles the distribution of a single request to multiple models, scoring all in parallel, then decides on the most appropriate output to return. This is a massively scalable, flexible architecture that solves one of the key problems encountered by Data Science teams today. In this session we will look at the original problem and the architecture which was used to solve it.
Learning in the Modern Workplace
The document discusses learning in the modern workplace. It notes that learning habits are changing, with people participating in online communities, using online tools to solve problems, and keeping up to date through knowledge flows. The key features of learning on the web are described as occurring in short bursts, on demand, on mobile devices, socially, continuously, and autonomously. Various internal and external resources that individuals use for learning are illustrated. The presentation recommends ways for companies to modernize workplace learning, such as by creating short, appealing content; running learning campaigns; making learning available on demand and for any device; encouraging social content; curating and coordinating learning opportunities; supporting everyday learning in teams; and encouraging self-improvement through individual learning
Open science in cognitive modeling
Talk given at the International Conference on Cognitive Modelling, University of Groningen on 10 April 2015.#
CC0 - Public Domain
To the extent possible under law, Caspar Addyman has waived all copyright and related or neighboring rights to Open science in cognitive modeling. This work is published from: United Kingdom.
eROSA Stakeholder WS1: Cirad - Dataverse: A platform to manage, work, share a...
Presentation by Sophie Fortuno delivered at the 1st eROSA Stakeholder Workshop in Montpellier, 6/7/2017
Digital Science: Towards the executable paper
The science performed in Astronomy is digital science, from observing proposals to final publication, including data and software used: each of the elements and actions involved in the scientific output could be recorded in electronic form.
This fact does not prevent the final outcome of an experiment is still difficult to reproduce. An exhaustive process of documentation can be long, tedious, where access to all the resources must be granted, and after all, the repeatability of results is not even guaranteed. At the same time, we have access to a wealth of files, observational data and publications that could be used more efficiently with a better visibility of the scientific production, avoiding duplication of effort and reinvention.
ODSC West 2022 – Kitbashing in ML
We're in the age of toolbox Machine Learning. What should you know about how to use emerging technologies like pre-trained models, large-language models, fine-tuning, and MLOps solutions to quickly and effectively build AI products.
Beyond instructional design: how performance support is revolutionizng e-lear...
The document discusses how performance support is revolutionizing e-learning by focusing on providing resources that help users complete tasks rather than taking courses or tests. It outlines how learning habits are changing to be more on-demand, in short bursts across different devices. This means instructional designers need to create small, flexible resources in different formats. Examples of different types of performance support resources like checklists, step-by-step guides, videos and infographics are provided. The performance support design process involves analyzing the performance problem, designing the optimal resource format, and making it available for on-demand use. Success is measured through metrics like usage and evidence of performance improvement.
Ultra Fast Deep Learning in Hybrid Cloud Using Intel Analytics Zoo & Alluxio
Alluxio Global Online Meetup
Apr 23, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Jiao (Jennie) Wang, Intel
Tsai Louie, Intel
Bin Fan, Alluxio
Today, many people run deep learning applications with training data from separate storage such as object storage or remote data centers. This presentation will demo the Intel Analytics Zoo + Alluxio stack, an architecture that enables high performance while keeping cost and resource efficiency balanced without network being I/O bottlenecked.
Intel Analytics Zoo is a unified data analytics and AI platform open-sourced by Intel. It seamlessly unites TensorFlow, Keras, PyTorch, Spark, Flink, and Ray programs into an integrated pipeline, which can transparently scale from a laptop to large clusters to process production big data. Alluxio, as an open-source data orchestration layer, accelerates data loading and processing in Analytics Zoo deep learning applications.
This talk, we will go over:
- What is Analytics Zoo and how it works
- How to run Analytics Zoo with Alluxio in deep learning applications
- Initial performance benchmark results using the Analytics Zoo + Alluxio stack
Similar to Build your own data challenge, or just organize team work (20)
Sharing massive data analysis: from provenance to linked experiment reports
Sharing massive data analysis: from provenance to linked experiment reports
How a Social Knowledge Graph Improves Remote Working by Capturing Context fro...
How a Social Knowledge Graph Improves Remote Working by Capturing Context fro...
Train, explain, acclaim. Build a good model in three steps
Train, explain, acclaim. Build a good model in three steps
Fiducia & GAD IT AG: From Fraud Detection to Big Data Platform: Bringing Hado...
Fiducia & GAD IT AG: From Fraud Detection to Big Data Platform: Bringing Hado...
Horton+Pruim+Kaplan_MOSAIC-StudentGuide.pdf Nicholas J. .docx
Horton+Pruim+Kaplan_MOSAIC-StudentGuide.pdf Nicholas J. .docx
Driving Enterprise Adoption: Tragedies, Triumphs and Our NEXT
Driving Enterprise Adoption: Tragedies, Triumphs and Our NEXT
FAIRDOM - FAIR Asset management and sharing experiences in Systems and Synthe...
FAIRDOM - FAIR Asset management and sharing experiences in Systems and Synthe...
Big Data LDN 2018: HOW RANK GAMING PRODUCTIONISED & AUTOMATED THE MANAGEMENT ...
Big Data LDN 2018: HOW RANK GAMING PRODUCTIONISED & AUTOMATED THE MANAGEMENT ...
eROSA Stakeholder WS1: Cirad - Dataverse: A platform to manage, work, share a...
eROSA Stakeholder WS1: Cirad - Dataverse: A platform to manage, work, share a...
Beyond instructional design: how performance support is revolutionizng e-lear...
Beyond instructional design: how performance support is revolutionizng e-lear...
Ultra Fast Deep Learning in Hybrid Cloud Using Intel Analytics Zoo & Alluxio
Ultra Fast Deep Learning in Hybrid Cloud Using Intel Analytics Zoo & Alluxio
More from Balázs Kégl
Data-driven hypothesis generation using deep neural nets
1. Machine learning in science
- induction, inference, simulation, generation
2. Stretching the scientific method
- the p-value controversy and the problem of automated hypothesis generation
3. Generative models and novelty generation
Model-based reinforcement learning and self-driving engineering systems
DeepLearning summer school, Gran Canaria 2021
https://www.youtube.com/watch?v=B5UBJiwL_D0
I introduce reinforcement learning from a model-based perspective. In this paradigm the core of the algorithm is the system model: a multivariate generative (probabilistic) time-series predictor. The system model is combined with online planning and/or learning model-free agents on the system model. The course is designed for students with basic classical machine learning knowledge. My goal is to open up an interesting perspective while also giving you useful tools to tackle practical applications.
The main motivation of the course is learning and improving policies to control engineering systems (autopilots or "self-driving" systems). Unlike popular game benchmarks, these systems are low-dimensional (~10s to ~100s of dimensions) with rewards coming continuously or with a short delay. On the other hand, they are physical, slow, and system access is usually extremely restricted. The focus and the main algorithmic challenge is thus not representation learning and handling sparse rewards (as in games), rather learning robust system models on extremely small (100s or 1000s of time steps) non-iid data. The perspective is also an interesting extension of the classical supervised learning paradigm, in which functions are learned in a single shot on data generated (sampled and labeled) by an imaginary oracle. In the real world, supervised models are usually re-learned often, on non-iid data generated by a process which we partially control. The questions that we will ponder on (exploration, distribution shift, non-iid data) may thus also interest students planning to work on supervised machine learning in the real world.
Managing the AI process: putting humans (back) in the loop
AI today is all over the news, yet it has not transformed the workplace as promised. We will argue that the reason is that we forgot humans, both as developers of AI (lacking organizational tools in AI development) and as consumers of AI (lacking transparent interfaces). I will show how we deal with these issues at Huawei’s Noah’s Ark lab where we (AI researchers) work closely with system engineers (our AI consumers).
DARMDN: Deep autoregressive mixture density nets for dynamical system mode...
Unlike computers, physical engineering systems (such as data center cooling or wireless network control) do not get faster with time. This is arguably one of the main reasons why recent beautiful advances in deep reinforcement learning (RL) stay mostly in the realm of simulated worlds and do not immediately translate to practical success in the real world. In order to make the best use of the small data sets these systems generate, we develop data-driven neural simulators to model the system and apply model-based control to optimize them. In this talk I will present the first step of this research agenda, a new versatile system modelling tool called deep autoregressive mixture density net (DARMDN – pronounced darm-dee-en). We argue that the performance of model-based reinforcement learning is partly limited by the approximation capacity of the currently used conditional density models and show how DARMDN alleviates these limitations. The model, combined with a random shooting controller, establishes a new state of the art on the popular Acrobot benchmark. Our most interesting and counter-intuitive finding is that the “sincos” Acrobot system which requires no multimodal posterior predictives, can be solved with a deterministic model, but only if it is trained as a probabilistic model. A deterministic model that is trained to minimize MSE leads to prediction error accumulation.
Machine learning in scientific workflows
This document discusses using machine learning in scientific workflows. It outlines challenges including lack of collaboration tools, bottlenecks in data collection and annotation, and accounting for systematic uncertainties. Technical challenges include designing workflows and metrics, generating training data, and dealing with non-iid data. ML can be used for data collection, inference, generation/model reduction, and hypothesis generation. Examples of applications discussed include classifying insect photos, detecting Mars craters, classifying variable stars, and predicting molecular spectra. RAMP is introduced as a tool for collaborative prototyping, teaching, and managing data science processes with an emphasis on code submission and data challenges.
A historical introduction to deep learning: hardware, data, and tricks
The bumpy 60-year history that led to the current state of the art of deep learning and an overview of what yo can do with it.
Learning do discover: machine learning in high-energy physics
My slides from my CERN talk on data science and high-energy physics.
http://cds.cern.ch/record/1702668
More from Balázs Kégl (7)
Data-driven hypothesis generation using deep neural nets
Data-driven hypothesis generation using deep neural nets
Model-based reinforcement learning and self-driving engineering systems
Model-based reinforcement learning and self-driving engineering systems
Managing the AI process: putting humans (back) in the loop
Managing the AI process: putting humans (back) in the loop
DARMDN: Deep autoregressive mixture density nets for dynamical system mode...
DARMDN: Deep autoregressive mixture density nets for dynamical system mode...
A historical introduction to deep learning: hardware, data, and tricks
A historical introduction to deep learning: hardware, data, and tricks
Learning do discover: machine learning in high-energy physics
Learning do discover: machine learning in high-energy physics
Recently uploaded
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
原版办理【微信号:BYZS866】【南十字星大学毕业证(SCU毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
毕业原版【微信:176555708】【(UCSB毕业证书)圣芭芭拉分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版格里菲斯大学毕业证(Griffith毕业证书)学历如何办理
原版制作【微信号:BYZS866】【格里菲斯大学毕业证(Griffith毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
毕业原版【微信:176555708】【(UMN毕业证书)明尼苏达大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
原版一比一爱尔兰都柏林大学毕业证(UCD毕业证书)如何办理
原版制作【微信:41543339】【爱尔兰都柏林大学毕业证(UCD毕业证书)】【微信:41543339】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
一比一原版(UO毕业证)渥太华大学毕业证如何办理
原件一模一样【微信：95270640】【渥太华大学毕业证UO学位证成绩单】【微信：95270640】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：95270640】
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办理渥太华大学毕业证毕业证offerUO学位证【微信：95270640 】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理渥太华大学毕业证UO学位证毕业证offer【微信：95270640 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理渥太华大学毕业证毕业证offerUO学位证【微信：95270640 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理渥太华大学毕业证毕业证offerUO学位证【微信：95270640 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Build applications with generative AI on Google Cloud
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
一比一原版(harvard毕业证书）哈佛大学毕业证如何办理
一模一样【微信：A575476】【(harvard毕业证书）哈佛大学毕业证成绩单Offer】【微信：A575476】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：A575476】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：A575476】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理
原版办【微信号:BYZS866】【英属哥伦比亚大学毕业证(UBC毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版爱尔兰都柏林大学毕业证(本硕）ucd学位证书如何办理
原版一模一样【微信：741003700 】【爱尔兰都柏林大学毕业证(本硕）ucd成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
原版制作【微信:41543339】【多伦多大学毕业证(UofT毕业证书)】【微信:41543339】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版英国赫特福德大学毕业证（hertfordshire毕业证书）如何办理
原版一模一样【微信：741003700 】【英国赫特福德大学毕业证（hertfordshire毕业证书）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理英国赫特福德大学毕业证（hertfordshire毕业证书）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理英国赫特福德大学毕业证（hertfordshire毕业证书）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理英国赫特福德大学毕业证（hertfordshire毕业证书）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理英国赫特福德大学毕业证（hertfordshire毕业证书）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Recently uploaded (20)
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024
Build applications with generative AI on Google Cloud
Build applications with generative AI on Google Cloud
A presentation that explain the Power BI Licensing
A presentation that explain the Power BI Licensing
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Build your own data challenge, or just organize team work
- 1. RAMP-WORKFLOW & RAMP-KITS 1 Université Paris-Saclay / CNRS BALÁZS KÉGL Center for Data Science Paris-Saclay
- 2. 2 Most classical data challenges are HR and publicity events
- 3. 3 We decided to turn them into a tool for 1. Collaborative prototyping 2. Teaching aid 3. Data science team management
- 4. 4 We are open sourcing it toolkit: https://github.com/paris-saclay-cds/ramp-workflow examples: https://github.com/ramp-kits
- 5. 5 Funded by Université Paris-Saclay
- 6. 6 RAMP.STUDIO DATA CHALLENGE WITH CODE SUBMISSION
- 7. Center for Data Science Paris-Saclay B. Kégl (CNRS) 7 what you achieved with a well tuned deep net the diversity gap the human blender gap competitive phase collaborative phase THE POWER OF THE (COLLABORATING) CROWD
- 8. Center for Data Science Paris-Saclay B. Kégl (CNRS) OPEN PHASE LETS PARTICIPANTS CATCH UP THE GOAL OF TEACHING 8
- 9. Center for Data Science Paris-Saclay B. Kégl (CNRS) 9 COMMUNICATION AND REUSE
- 10. 10 You can 1. Participate in upcoming RAMPs 2. Use RAMP in teaching or training
- 11. 11 Setting up the RAMP is was long and hard.
- 12. 12 Separate workflow building and workflow optimization
- 13. 13 Before solving the problem, set it up (even put it into production)
- 14. • toolkit: https://github.com/paris-saclay-cds/ramp-workflow • for designing workflows • set of ready-made metrics, workflows, CV schemes, data readers • unique command-line test script • examples: https://github.com/ramp-kits • a zoo of problems, experiments, workflows • (at least) one initial solution 14 RAMP-WORKFLOW & RAMP-KITS
- 15. Center for Data Science Paris-Saclay B. Kégl (CNRS) CLASSIFYING AND REGRESSING ON MOLECULAR SPECTRA 15 chemotherapy drug in elastic pocket laser spectrometer molecular spectra feature extractor 1 feature extractor 2 regressor concentration classifier drug type
- 16. Center for Data Science Paris-Saclay B. Kégl (CNRS) FORECASTING EL NINO SIX MONTHS AHEAD 16 … 300.14 299.83 298.76 299.87 299.82 300.15 300.10 299.50… … time series feature extractor x (a fixed length feature vector)regressor
- 17. 17 A SINGLE SCRIPT TO DEFINE THE BUNDLE X ypred score type score cross-validation scheme dataconnectors FE CLF workflow
- 18. 18 A SINGLE EXECUTABLE TO TEST THE SUBMISSIONS • Keep your different submissions in a simple file structure • Communicate them on git • Execute them also from the notebook
- 19. 19 You can 1. Use rampwf for your own workflows 2. Use rampwf to organize workflow building and optimization in an internal data science team 3. Submit it to us if you want to run a data challenge