Graph Gurus Episode 37: Modeling for Kaggle COVID-19 DatasetTigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-37
In this Graph Gurus Episode, we:
-Learn how to process text and extract entities (words and phrases) as well as classes linking the entities using SciSpacy, a Natural Language Processing (NLP) tool.
-Import the output of NLP and semantically link it in TigerGraph
-Run advanced analytics queries with TigerGraph to analyze the relationships and deliver insights
Unlocking Geospatial Analytics Use Cases with CARTO and DatabricksDatabricks
Many companies need to analyze large datasets that include location information. To be able to derive business insights from these datasets you need a solution that provides geospatial analysis functionalities and can scale to manage large volumes of information. The combination of CARTO and Databricks allows you to solve this kind of large scale geospatial analytics problems. CARTO provides a location intelligence platform to discover and predict key insights through location data. In this session we will see how we can integrate CARTO and Databricks and how we can take advantage of this combination to solve specific problems for industries such as logistics, telecommunications or financial services.
Graph Gurus Episode 37: Modeling for Kaggle COVID-19 DatasetTigerGraph
Full Webinar: https://info.tigergraph.com/graph-gurus-37
In this Graph Gurus Episode, we:
-Learn how to process text and extract entities (words and phrases) as well as classes linking the entities using SciSpacy, a Natural Language Processing (NLP) tool.
-Import the output of NLP and semantically link it in TigerGraph
-Run advanced analytics queries with TigerGraph to analyze the relationships and deliver insights
Unlocking Geospatial Analytics Use Cases with CARTO and DatabricksDatabricks
Many companies need to analyze large datasets that include location information. To be able to derive business insights from these datasets you need a solution that provides geospatial analysis functionalities and can scale to manage large volumes of information. The combination of CARTO and Databricks allows you to solve this kind of large scale geospatial analytics problems. CARTO provides a location intelligence platform to discover and predict key insights through location data. In this session we will see how we can integrate CARTO and Databricks and how we can take advantage of this combination to solve specific problems for industries such as logistics, telecommunications or financial services.
H2O Machine Learning with KNIME Analytics Platform - Christian Dietz - H2O AI...Sri Ambati
This talk was recorded in London on October 30, 2018.
KNIME Analytics Platform is an easy to use and comprehensive open source data integration, analysis, and exploration platform, enabling data scientists to visually compose end to end data analysis workflows. The over 2,000 available modules ("nodes") cover each step of the analysis workflow, including blending heterogeneous data types, data transformation, wrangling and cleansing, advanced data visualization, or model training and deployment.
Many of these nodes are provided through open source integrations (why reinvent the wheel?). This provides seamless access to large open source projects such as Keras and Tensorflow for deep learning, Apache Spark for big data processing, Python and R for scripting, and more. These integrations can be used in combination with other KNIME nodes meaning that data scientists can freely select from a vast variety of options when tackling an analysis problem.
The integration of H2O in KNIME offers an extensive number of nodes and encapsulating functionalities of the H2O open source machine learning libraries, making it easy to use H2O algorithms from a KNIME workflow without touching any code - each of the H2O nodes looks and feels just like a normal KNIME node - and the data scientist benefits from the high performance libraries and proven quality of H2O during execution. For prototyping these algorithms are executed locally, however training and deployment can easily be scaled up using a Sparkling Water cluster.
In our talk we give a short introduction to KNIME Analytics Platform and then demonstrate how data scientists benefit from using KNIME Analytics Platform and H2O Machine Learning in combination by using a real world analysis example.
Bio: Christian received a Master’s degree in Computer Science from the University of Konstanz. Having gained experience as a research software engineer at the University of Konstanz, where he developed frameworks and libraries in the fields of bioimage analysis and machine learning, Christian moved on to become a software engineer at KNIME. He now focuses on developing new functionalities and extensions for KNIME Analytics Platform. Some of his recent projects include deep learning integrations built upon Keras and Tensorflow, extensions for image analysis and active learning, and the integration of H2O Machine Learning and H2O Sparkling Water in KNIME Analytics Platform.
Megan Kurka, H2O.ai - AutoDoc with H2O Driverless AI - H2O World 2019 NYCSri Ambati
This talk was recorded in NYC on October 22nd, 2019 and can be viewed here: https://youtu.be/aJJsrQHqsGg
AutoDoc with H2O Driverless AI
Driverless AI with Auto Doc is the next logical step of the data science workflow by taking the final step of automatically documenting and explaining the processes used by the platform. Auto Doc frees up the user from the time consuming task of documenting and summarizing their workflow while building machine learning models. The resulting documentation provides users with insight into machine learning workflow created by Driverless AI including details about the data used, the validation schema selected, model and feature tuning, and the final model created. With this capability in Driverless AI, users can focus on model insights and results.
Bio: Megan is a Customer Data Scientist at H2O. Prior to working at H2O, she worked as a Data Scientist building products driven by machine learning for B2B customers. She has experience working with customers across multiple industries, identifying common problems, and designing robust and automated solutions.
Building Identity Graph at Scale for Programmatic Media Buying Using Apache S...Databricks
The proliferation of digital channels has made it mandatory for marketers to understand an individual across multiple touchpoints. In order to develop market effectiveness, marketers need have a pretty good sense of its consumer’s identity so that it can reach him via mobile device, desktop or a big TV screen on living room. Examples of such identity tokens include cookies, app IDs etc.A consumer can use multiple devices at the same time and so the same consumer should not be treated as different people in the advertising space. The idea of identity resolution comes with this mission and goal to have an omnichannel view of a consumer.
Building an ML Tool to predict Article Quality Scores using Delta & MLFlowDatabricks
For Roularta, a news & media publishing company, it is of a great importance to understand reader behavior and what content attract, engage and convert readers. At Roularta, we have built an AI-driven article quality scoring solution on using Spark for parallelized compute, Delta for efficient data lake use, BERT for NLP and MLflow for model management. The article quality score solution is an NLP-based ML model which gives for every article published – a calculated and forecasted article quality score based on 3 dimensions (conversion, traffic and engagement).
FrugalML: Using ML APIs More Accurately and CheaplyDatabricks
Offering prediction APIs for fee is a fast growing industry and is an important aspect of machine learning as a service. While many such services are available, the heterogeneity in their price and performance makes it challenging for users to decide which API or combination of APIs to use for their own data and budget. We take a first step towards addressing this challenge by proposing FrugalML, a principled framework that jointly learns the strength and weakness of each API on different data, and performs an efficient optimization to automatically identify the best sequential strategy to adaptively use the available APIs within a budget constraint. Our theoretical analysis shows that natural sparsity in the formulation can be leveraged to make FrugalML efficient. We conduct systematic experiments using ML APIs from Google, Microsoft, Amazon, IBM, Baidu and other providers for tasks including facial emotion recognition, sentiment analysis and speech recognition. Across various tasks, FrugalML can achieve up to 90% cost reduction while matching the accuracy of the best single API, or up to 5% better accuracy while matching the best API’s cost.
ML Workshop 1: A New Architecture for Machine Learning LogisticsMapR Technologies
Having heard the high-level rationale for the rendezvous architecture in the introduction to this series, we will now dig in deeper to talk about how and why the pieces fit together. In terms of components, we will cover why streams work, why they need to be persistent, performant and pervasive in a microservices design and how they provide isolation between components. From there, we will talk about some of the details of the implementation of a rendezvous architecture including discussion of when the architecture is applicable, key components of message content and how failures and upgrades are handled. We will touch on the monitoring requirements for a rendezvous system but will save the analysis of the recorded data for later. Listen to the webinar on demand: https://mapr.com/resources/webinars/machine-learning-workshop-1/
Scaling Production Machine Learning Pipelines with DatabricksDatabricks
Conde Nast is a global leader in the media production space housing iconic brands such as The New Yorker, Wired, Vanity Fair, and Epicurious, among many others. Along with our content production, Conde Nast invests heavily in companion products to improve and enhance our audience’s experience.
Vertex AI: Pipelines for your MLOps workflowsMárton Kodok
In recent years, one of the biggest trends in applications development has been the rise of Machine Learning solutions, tools, and managed platforms. Vertex AI is a managed unified ML platform for all your AI workloads. On the MLOps side, Vertex AI Pipelines solutions let you adopt experiment pipelining beyond the classic build, train, eval, and deploy a model. It is engineered for data scientists and data engineers, and it’s a tremendous help for those teams who don’t have DevOps or sysadmin engineers, as infrastructure management overhead has been almost completely eliminated.
Based on practical examples we will demonstrate how Vertex AI Pipelines scores high in terms of developer experience, how fits custom ML needs, and analyze results. It’s a toolset for a fully-fledged machine learning workflow, a sequence of steps in the model development, a deployment cycle, such as data preparation/validation, model training, hyperparameter tuning, model validation, and model deployment. Vertex AI comes with all standard resources plus an ML metadata store, a fully managed feature store, and a fully managed pipelines runner.
Vertex AI Pipelines is a managed serverless toolkit, which means you don't have to fiddle with infrastructure or back-end resources to run workflows.
Pinterest - Big Data Machine Learning Platform at PinterestAlluxio, Inc.
This was presented by the Yongsheng Wu, head of big data and ML platform at Pinterest, at the Alluxio bay area meetup.
Yongsheng shares Pinterest's journey to build a fast and scalable big data and ML platform in AWS for Pinterest to handle the requests and complexity in data at scale. In this talk, he will cover different aspects from the requirements of the platform, the challenges encountered, the technologies chosen, and the tradeoffs that were made.
This presentation was made on June 18, 2020.
Video recording of the session can be viewed here: https://youtu.be/YEtDwYSXXJo
For many companies, model documentation is a requirement for any model to be used in the business. For other companies, model documentation is part of a data science team’s best practices. Model documentation includes how a model was created, training and test data characteristics, what alternatives were considered, how the model was evaluated, and information on model performance.
Collecting and documenting this information can take a data scientist days to complete for each model. The model document needs to be comprehensive and consistent across various projects. The process of creating this documentation is tedious for the data scientist and wasteful for the business because the data scientist could be using that time to build additional models and create more value. Inconsistent or inaccurate model documentation can be an issue for model validation, governance, and regulatory compliance.
In this virtual meetup, we will learn how to create comprehensive, high-quality model documentation in minutes that saves time, increases productivity, and improves model governance.
Speaker's Bio:
Nikhil Shekhar: Nikhil is a Machine Learning Engineer at H2O.ai. He is currently working on our automatic machine learning platform, Driverless AI. He graduated from the University of Buffalo majoring in Artificial Intelligence and is interested in developing scalable machine learning algorithms.
H2O Machine Learning with KNIME Analytics Platform - Christian Dietz - H2O AI...Sri Ambati
This talk was recorded in London on October 30, 2018.
KNIME Analytics Platform is an easy to use and comprehensive open source data integration, analysis, and exploration platform, enabling data scientists to visually compose end to end data analysis workflows. The over 2,000 available modules ("nodes") cover each step of the analysis workflow, including blending heterogeneous data types, data transformation, wrangling and cleansing, advanced data visualization, or model training and deployment.
Many of these nodes are provided through open source integrations (why reinvent the wheel?). This provides seamless access to large open source projects such as Keras and Tensorflow for deep learning, Apache Spark for big data processing, Python and R for scripting, and more. These integrations can be used in combination with other KNIME nodes meaning that data scientists can freely select from a vast variety of options when tackling an analysis problem.
The integration of H2O in KNIME offers an extensive number of nodes and encapsulating functionalities of the H2O open source machine learning libraries, making it easy to use H2O algorithms from a KNIME workflow without touching any code - each of the H2O nodes looks and feels just like a normal KNIME node - and the data scientist benefits from the high performance libraries and proven quality of H2O during execution. For prototyping these algorithms are executed locally, however training and deployment can easily be scaled up using a Sparkling Water cluster.
In our talk we give a short introduction to KNIME Analytics Platform and then demonstrate how data scientists benefit from using KNIME Analytics Platform and H2O Machine Learning in combination by using a real world analysis example.
Bio: Christian received a Master’s degree in Computer Science from the University of Konstanz. Having gained experience as a research software engineer at the University of Konstanz, where he developed frameworks and libraries in the fields of bioimage analysis and machine learning, Christian moved on to become a software engineer at KNIME. He now focuses on developing new functionalities and extensions for KNIME Analytics Platform. Some of his recent projects include deep learning integrations built upon Keras and Tensorflow, extensions for image analysis and active learning, and the integration of H2O Machine Learning and H2O Sparkling Water in KNIME Analytics Platform.
Megan Kurka, H2O.ai - AutoDoc with H2O Driverless AI - H2O World 2019 NYCSri Ambati
This talk was recorded in NYC on October 22nd, 2019 and can be viewed here: https://youtu.be/aJJsrQHqsGg
AutoDoc with H2O Driverless AI
Driverless AI with Auto Doc is the next logical step of the data science workflow by taking the final step of automatically documenting and explaining the processes used by the platform. Auto Doc frees up the user from the time consuming task of documenting and summarizing their workflow while building machine learning models. The resulting documentation provides users with insight into machine learning workflow created by Driverless AI including details about the data used, the validation schema selected, model and feature tuning, and the final model created. With this capability in Driverless AI, users can focus on model insights and results.
Bio: Megan is a Customer Data Scientist at H2O. Prior to working at H2O, she worked as a Data Scientist building products driven by machine learning for B2B customers. She has experience working with customers across multiple industries, identifying common problems, and designing robust and automated solutions.
Building Identity Graph at Scale for Programmatic Media Buying Using Apache S...Databricks
The proliferation of digital channels has made it mandatory for marketers to understand an individual across multiple touchpoints. In order to develop market effectiveness, marketers need have a pretty good sense of its consumer’s identity so that it can reach him via mobile device, desktop or a big TV screen on living room. Examples of such identity tokens include cookies, app IDs etc.A consumer can use multiple devices at the same time and so the same consumer should not be treated as different people in the advertising space. The idea of identity resolution comes with this mission and goal to have an omnichannel view of a consumer.
Building an ML Tool to predict Article Quality Scores using Delta & MLFlowDatabricks
For Roularta, a news & media publishing company, it is of a great importance to understand reader behavior and what content attract, engage and convert readers. At Roularta, we have built an AI-driven article quality scoring solution on using Spark for parallelized compute, Delta for efficient data lake use, BERT for NLP and MLflow for model management. The article quality score solution is an NLP-based ML model which gives for every article published – a calculated and forecasted article quality score based on 3 dimensions (conversion, traffic and engagement).
FrugalML: Using ML APIs More Accurately and CheaplyDatabricks
Offering prediction APIs for fee is a fast growing industry and is an important aspect of machine learning as a service. While many such services are available, the heterogeneity in their price and performance makes it challenging for users to decide which API or combination of APIs to use for their own data and budget. We take a first step towards addressing this challenge by proposing FrugalML, a principled framework that jointly learns the strength and weakness of each API on different data, and performs an efficient optimization to automatically identify the best sequential strategy to adaptively use the available APIs within a budget constraint. Our theoretical analysis shows that natural sparsity in the formulation can be leveraged to make FrugalML efficient. We conduct systematic experiments using ML APIs from Google, Microsoft, Amazon, IBM, Baidu and other providers for tasks including facial emotion recognition, sentiment analysis and speech recognition. Across various tasks, FrugalML can achieve up to 90% cost reduction while matching the accuracy of the best single API, or up to 5% better accuracy while matching the best API’s cost.
ML Workshop 1: A New Architecture for Machine Learning LogisticsMapR Technologies
Having heard the high-level rationale for the rendezvous architecture in the introduction to this series, we will now dig in deeper to talk about how and why the pieces fit together. In terms of components, we will cover why streams work, why they need to be persistent, performant and pervasive in a microservices design and how they provide isolation between components. From there, we will talk about some of the details of the implementation of a rendezvous architecture including discussion of when the architecture is applicable, key components of message content and how failures and upgrades are handled. We will touch on the monitoring requirements for a rendezvous system but will save the analysis of the recorded data for later. Listen to the webinar on demand: https://mapr.com/resources/webinars/machine-learning-workshop-1/
Scaling Production Machine Learning Pipelines with DatabricksDatabricks
Conde Nast is a global leader in the media production space housing iconic brands such as The New Yorker, Wired, Vanity Fair, and Epicurious, among many others. Along with our content production, Conde Nast invests heavily in companion products to improve and enhance our audience’s experience.
Vertex AI: Pipelines for your MLOps workflowsMárton Kodok
In recent years, one of the biggest trends in applications development has been the rise of Machine Learning solutions, tools, and managed platforms. Vertex AI is a managed unified ML platform for all your AI workloads. On the MLOps side, Vertex AI Pipelines solutions let you adopt experiment pipelining beyond the classic build, train, eval, and deploy a model. It is engineered for data scientists and data engineers, and it’s a tremendous help for those teams who don’t have DevOps or sysadmin engineers, as infrastructure management overhead has been almost completely eliminated.
Based on practical examples we will demonstrate how Vertex AI Pipelines scores high in terms of developer experience, how fits custom ML needs, and analyze results. It’s a toolset for a fully-fledged machine learning workflow, a sequence of steps in the model development, a deployment cycle, such as data preparation/validation, model training, hyperparameter tuning, model validation, and model deployment. Vertex AI comes with all standard resources plus an ML metadata store, a fully managed feature store, and a fully managed pipelines runner.
Vertex AI Pipelines is a managed serverless toolkit, which means you don't have to fiddle with infrastructure or back-end resources to run workflows.
Pinterest - Big Data Machine Learning Platform at PinterestAlluxio, Inc.
This was presented by the Yongsheng Wu, head of big data and ML platform at Pinterest, at the Alluxio bay area meetup.
Yongsheng shares Pinterest's journey to build a fast and scalable big data and ML platform in AWS for Pinterest to handle the requests and complexity in data at scale. In this talk, he will cover different aspects from the requirements of the platform, the challenges encountered, the technologies chosen, and the tradeoffs that were made.
This presentation was made on June 18, 2020.
Video recording of the session can be viewed here: https://youtu.be/YEtDwYSXXJo
For many companies, model documentation is a requirement for any model to be used in the business. For other companies, model documentation is part of a data science team’s best practices. Model documentation includes how a model was created, training and test data characteristics, what alternatives were considered, how the model was evaluated, and information on model performance.
Collecting and documenting this information can take a data scientist days to complete for each model. The model document needs to be comprehensive and consistent across various projects. The process of creating this documentation is tedious for the data scientist and wasteful for the business because the data scientist could be using that time to build additional models and create more value. Inconsistent or inaccurate model documentation can be an issue for model validation, governance, and regulatory compliance.
In this virtual meetup, we will learn how to create comprehensive, high-quality model documentation in minutes that saves time, increases productivity, and improves model governance.
Speaker's Bio:
Nikhil Shekhar: Nikhil is a Machine Learning Engineer at H2O.ai. He is currently working on our automatic machine learning platform, Driverless AI. He graduated from the University of Buffalo majoring in Artificial Intelligence and is interested in developing scalable machine learning algorithms.
From Developer to Data Scientist - Gaines KergosienITCamp
ABSTRACT: Due to recent advances in technology, humanity is collecting vast amounts of data at an unprecedented rate, making the skills necessary to mine insights from this data increasingly valuable. So what does it take for a Developer to enter the world of data science?
Join me on a journey into the world of big data and machine learning where we will explore what the work actually looks like, identify which skills are most important, and design a road map for how you too can join this exciting and profitable industry.
What is graph all about, and why should you care? Graphs come in many shapes and forms, and can be used for different applications: Graph Analytics, Graph AI, Knowledge Graphs, and Graph Databases.
Talk by George Anadiotis. Connected Data London Meetup June 29th 2020.
Up until the beginning of the 2010s, the world was mostly running on spreadsheets and relational databases. To a large extent, it still does. But the NoSQL wave of databases has largely succeeded in instilling the “best tool for the job” mindset.
After relational, key-value, document, and columnar, the latest link in this evolutionary proliferation of data structures is graph. Graph analytics, Graph AI, Knowledge Graphs and Graph Databases have been making waves, included in hype cycles for the last couple of years.
The Year of the Graph marked the beginning of it all before the Gartners of the world got in the game. The Year of the Graph is a term coined to convey the fact that the time has come for this technology to flourish.
The eponymous article that set the tone was published in January 2018 on ZDNet by domain expert George Anadiotis. George has been working with, and keeping an eye on, all things Graph since the early 2000s. He was one of the first to note the continuing rise of Graph Databases, and to bring this technology in front of a mainstream audience.
The Year of the Graph has been going strong since 2018. In August 2018, Gartner started including Graph in its hype cycles. Ever since, Graph has been riding the upward slope of the Hype Cycle.
The need for knowledge on these technologies is constantly growing. To respond to that need, the Year of the Graph newsletter was released in April 2018. In addition, a constant flow of graph-related news and resources is being shared on social media.
To help people make educated choices, the Year of the Graph Database Report was released. The report has been hailed as the most comprehensive of its kind in the market, consistently helping people choose the most appropriate solution for their use case since 2018.
The report, articles, news stream, and the newsletter have been reaching thousands of people, helping them understand and navigate this landscape. We’ll talk about the Year of the Graph, the different shapes, forms, and applications for graphs, the latest news and trends, and wrap up with an ask me anything session.
Graph Analytics on Data from Meetup.comKarin Patenge
How to improve your Meetup experience by using Graph Analytics on data from Meetup.com. Slides from my session with "Women Who Code" group in Berlin on May 23, 2018.
Deploying Python Machine Learning Models with Apache Spark with Brandon Hamri...Databricks
Deploying machine learning models seems like it should be a relatively easy task. Take your model and pass it some features in production. The reality is that the code written during the prototyping phase of model development doesn’t always work when applied at scale or on “real” data. This talk will explore 1) common problems at the intersection of data science and data engineering 2) how you can structure your code so there is minimal friction between prototyping and production, and 3) how you can use Apache Spark to run predictions on your models in batch or streaming contexts.
You will take away how to address some of productionizing issues that data scientists and data engineers face while deploying machine learning models at scale and a better understanding of how to work collaboratively to minimize disparity between prototyping and productizing.
Taking Jupyter Notebooks and Apache Spark to the Next Level PixieDust with Da...Databricks
PixieDust is a new open source library that helps data scientists and developers working in Jupyter Notebooks and Apache Spark be more efficient. PixieDust speeds up data manipulation and display with features like: auto-visualization of Spark DataFrames, real-time Spark job progress monitoring, automated local install of Python and Scala kernels running with Spark, and much more.
Come along and learn how you can use this tool in your own projects to visualize and explore data effortlessly with no coding. Oh, and if you prefer working with a Scala Notebook, this session is also for you, as PixieDust can also run on a Scala Kernel. Imagine being able to visualize your favorite Python chart engines from a Scala Notebook!
We’ll finish the session with a demo combining Twitter, Watson Tone Analyzer, Spark Streaming, and some fun real-time visualizations–all running within a Notebook.
Data Science covers the complete workflow from defining a question, finding the most suitable data source, identifying the right tools and finally presenting the best possible answer in a clear, engaging manner. But it all starts with having access to the data. In these slides I will walk your through some examples of how to collect, store and access data in the Cloud with the use of different APIs.
Intelligent data summit: Self-Service Big Data and AI/ML: Reality or Myth?SnapLogic
Companies collect more data but struggle with how to glean the best insights. Use of Machine Learning also needs power data integration.
In this presentation, Janet Jaiswal, SnapLogic's VP of product marketing, reviews key strategies and technologies to deliver intelligent data via self-service ML models.
To learn more, visit https://www.snaplogic.com
Dynniq & GoDataDriven - Shaping the future of traffic with IoT and AIBigDataExpo
Dynniq is a high-tech, innovative company offering smart mobility solutions and services internationally. We will present advanced IoT use cases Dynniq is working on, and share how GoDataDriven helps set up an AI capability. We will share our learnings, and show what makes data science in the mobility domain unique.
Similar to FROM DATAFRAMES TO GRAPH Data Science with pyTigerGraph (20)
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.