Data science team, a practice to setup
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This document outlines roles and processes for building an effective data science team. It recommends that teams include people with skills in machine learning, data engineering, analytics, mathematics, and visualization. The document describes the CRISP-DM process for data science projects and a lambda architecture for continuous analysis of big data using a pipeline of jobs, messaging queues, and data processing applications. It emphasizes building cross-disciplinary teams that can work together to ask important questions and create useful data products.
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Pay no attention to the man behind the curtain - the unseen work behind data ...
Data science vs. Data scientist by Jothi Periasamy
Data science vs. Data scientist by Jothi Periasamy
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From Config Management Sucks to #cfgmgmtlove
This document summarizes Kris Buytaert's talk on the evolution of config management from the 1990s to present day. It discusses early approaches like manual installations and system imaging tools. It then covers the rise of infrastructure as code using tools like Puppet, Chef, and Docker. The talk addresses challenges like getting operations teams to adopt new methods and complexities that can arise from dependencies and modules. It promotes treating infrastructure like code with development practices for versioning, testing, and continuous integration/deployment.
Rootconf
LinkedIn uses Kafka for log aggregation and monitoring. It ingests over 500,000 metrics per minute from applications into Kafka, which then writes to storage clusters. It also uses Kafka to transport log data to ELK (Elasticsearch, Logstash, Kibana) for real-time search, analysis and visualization of metrics and logs. Kafka provides benefits like horizontal scalability, multiple consumer groups, and no overhead on client applications.
Mesoscon 2015
This document discusses Mesos implementation at Bloomberg. It notes that Bloomberg runs one of the largest private networks and was an early adopter of cloud computing and software as a service. It describes how Mesos is used to provide elastic data processing and analytics across Bloomberg's 3000+ developers. Key parts of the Mesos implementation include using Marathon for application deployment, Kafka for processing topologies, and ELK/InfluxDB/Grafana for centralized monitoring. The document also discusses lessons learned around access control, Zookeeper protection, and cleaning up sandbox data.
Transform your Analytics Practice into Insights Practice
- The document discusses transforming an analytics practice into an insights practice to better serve leadership and stakeholders with actionable insights.
- Currently, different groups like analytics, user research, and A/B testing answer different questions from leadership but are siloed, creating issues.
- The proposed Insights 2.0 framework aims to solve this by taking an outcome-focused strategy, enabling better data instrumentation and management, providing first level insights from vetted data, and opening an analytics platform for end users.
- Key considerations for success include strong executive support, business unit buy-in, clearly defined objectives and success criteria, and deep stakeholder involvement throughout the process.
Building Product from ground up using Open Source Technologies
This is a presentation given by Amit Goel, Founder & CEO, Patterbuzz at Open Source India 2014 conference in Bangalore. . He spoke about the story of Patterbuzz and its journey of 2 years since its beginning to explain the product development with the pace of a start up. It covered all the necessary evils and building the technology form scratch using open source softwares and available technologies and how to leverage its advantage.
Patterbuzz builds on the core philosophy of unbundling content from the publishing industry and making it available to consumers in a device reading friendly format with pay as you go model. For example: users can buy single articles if they like rather than purchasing the complete magazine.
Currently, Patterbuzz enables magazines from India / Indian publishing industry. Most popular magazines available are Popular Science, Mother & Baby, Electronics for you, open source for you, computer active, Gfiles, Power Politics, Hard News, Car India, Bike India etc...
Send that (damn) elevator down !
This document contains notes from a leadership summit talk given by Ekta Grover. The main topics discussed include Maslow's hierarchy of needs, strategically creating opportunities, focusing on execution over ideas, growing strong teams, managing priorities, having a global perspective, building social capital, failing forward with resilience, and cultivating confidence through competence.
Introducing ELK
This document introduces the (B)ELK stack, which consists of Beats, Elasticsearch, Logstash, and Kibana. It describes each component and how they work together. Beats are lightweight data shippers that collect data from logs and systems. Logstash processes and transforms data from inputs like Beats. Elasticsearch stores and indexes the data. Kibana provides visualization and analytics capabilities. The document provides examples of using each tool and tips for working with the ELK stack.
Experiences in ELK with D3.js for Large Log Analysis and Visualization
This document discusses experiences using the ELK stack (Elasticsearch, Logstash, Kibana) and D3.js for large log analysis and visualization. It begins with an overview of network traffic logging at Kasetsart University, which generates over 30 terabytes of log data per day. It then demonstrates setting up an ELK testbed to index these logs in real-time for fast search and exploration in Kibana. Finally, it shows how D3.js can be used to create dynamic, real-time visualizations of the logged data.
Monitoring with ElasticSearch
This document discusses using the ELK stack (Elasticsearch, Logstash, Kibana) for log collection, aggregation, and visualization. It describes how to use Logstash to collect logs from various sources, apply filters like Grok to parse logs into structured data, and output to Elasticsearch for analysis and long-term storage. Metrics can also be sent to Graphite for long-term storage and visualization. The ELK stack provides a way for both developers and operations teams to analyze logs.
Elastic Stackにハマった話
This document summarizes Kazuhiro Kosaka's talk about being stuck using the Elastic Stack. Some key points included being stuck with data loss between Filebeat and Logstash due to Logstash's lack of a persistent queue in versions below 5.1. Kosaka was also stuck with Elasticsearch writing issues due to JVM memory pressure on a single m3.medium instance that was resolved by upgrading to 3 m3.large instances. Restrictions of the Amazon Elasticsearch Service were also discussed, as well as next steps of migrating to Elastic Stack 5.x and considering Elastic Cloud or a private cluster.
Monitoring using Open source technologies
In this presentation, we have talked about building an end to end solution for monitoring using open source technologies such as Graphite, Grafana, Apache Spark, Apache Kafka, OpenTSDB, Prometheus, etc.
Launching A Management Consulting Practice (2009)
A description of the nature of the management consulting profession and the requirements for launching a successful consulting practice. Includes client service, marketing and selling and practice management, as well as a discussion of the Management Consulting Competency Framework.
The Rise of Real Time
Multi-tenancy allows a single instance of software to serve multiple customers. It is an architecture where a single instance of the software runs on a server and serves multiple tenants. This allows for efficient use of computing resources and reduces maintenance costs as updates only need to be applied to a single code base.
Keystone - Leverage Big Data 2016
The document discusses Netflix's real-time data infrastructure called Keystone, which processes over 1 trillion events per day. It describes how Netflix built Keystone over 1 year to handle the massive scale of data from its growing user base and content catalog. Key points include:
- Keystone uses Kafka as its main data backbone to ingest, process and move data across clusters. It has over 3000 instances across 24 Kafka clusters.
- Keystone processes an average of 700 billion unique events per day and 1 trillion total events daily across 1.4 petabytes of data.
- The system was designed based on Netflix's culture of freedom and responsibility and best practices of full automation, self-healing and minimizing single points
How Did BuzzFeed Harvest One Million Email Subscribers?
How exactly did Buzzfeed managed to accumulate 1 million subscribers in 2014 alone? We have unpacked 20 steps the media news company took to reach this staggering number which looks set to be far higher in 2015.
Real-Time Log Analysis with Apache Mesos, Kafka and Cassandra
Slides for our solution we developed for using Mesos, Docker, Kafka, Spark, Cassandra and Solr (DataStax Enterprise Edition) all developed in Go for doing realtime log analysis at scale. Many organizations either need or want log analysis in real time where you can see within a second what is happening within your entire infrastructure. Today, with the hardware available and software systems we have in place, you can develop, build and use as a service these solutions.
Kafka + Uber- The World’s Realtime Transit Infrastructure, Aaron Schildkrout
Kafka is Uber's real-time data infrastructure that powers many of its core systems and products. It processes both real-time and batch data from many different sources and consumers across Uber's distributed systems. Over time, Uber has improved Kafka to handle larger volumes of data across more data centers and languages. Looking forward, Uber envisions Kafka enabling even more dynamic and real-time systems through continued innovation.
Scaling an ELK stack at bol.com
A presentation about the deployment of an ELK stack at bol.com
At bol.com we use Elasticsearch, Logstash and Kibana in a logsearch system that allows our developers and operations people to easilly access and search thru logevents coming from all layers of its infrastructure.
The presentations explains the initial design and its failures. It continues with explaining the latest design (mid 2014). Its improvements. And finally a set of tips are giving regarding Logstash and Elasticsearch scaling.
These slides were first presented at the Elasticsearch NL meetup on September 22nd 2014 at the Utrecht bol.com HQ.
Building an IoT Kafka Pipeline in Under 5 Minutes
This document discusses building an IoT Kafka pipeline using MemSQL in under 5 minutes. It begins with an overview of IoT, Kafka, and operational data warehouses. It then discusses MemSQL and how it functions as an operational data warehouse by continuously loading and querying data in real-time. The document demonstrates launching a MemSQL cluster, creating schemas and pipelines to ingest, transform, persist and analyze IoT data from Kafka. It emphasizes MemSQL's ability to handle different data types and scales from IoT at high throughput with low latency.
Building a Modern Data Architecture on AWS - Webinar
Amazon Web Services gives you fast access to flexible and low cost IT resources, so you can rapidly scale and build virtually any big data application including data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity, and variety of data.
https://aws.amazon.com/webinars/anz-webinar-series/
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How Did BuzzFeed Harvest One Million Email Subscribers?
How Did BuzzFeed Harvest One Million Email Subscribers?
Real-Time Log Analysis with Apache Mesos, Kafka and Cassandra
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Building a Modern Data Architecture on AWS - Webinar
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Similar to Data science team, a practice to setup
DataOps - Big Data and AI World London - March 2020 - Harvinder Atwal
Title
DataOps, the secret weapon for delivering AI, data science, and business intelligence value at speed.
Synopsis
● According to recent research, just 7.3% of organisations say the state of their data and analytics is excellent, and only 22% of companies are currently seeing a significant return from data science expenditure.
● Poor returns on data & analytics investment are often the result of applying 20th-century thinking to 21st-century challenges and opportunities.
● Modern data science and analytics require secure, efficient processes to turn raw data from multiple sources and in numerous formats into useful inputs to a data product.
● Developing, orchestrating and iterating modern data pipelines is an extremely complex process requiring multiple technologies and skills.
● Other domains have to successfully overcome the challenge of delivering high-quality products at speed in complex environments. DataOps applies proven agile principles, lean thinking and DevOps practices to the development of data products.
● A DataOps approach aligns data producers, analytical data consumers, processes and technology with the rest of the organisation and its goals.
Ch1IntroductiontoDataScience.pptx
Data Science involves extracting insights from vast amounts of data using scientific methods and algorithms. It includes concepts like Statistics, Visualization, Machine Learning, and Deep Learning. The Data Science process goes through steps like Discovery, Preparation, Modeling, and Communication. Important roles include Data Scientist, Engineer, Analyst, and Statistician. Tools include R, SQL, Python, and SAS. Applications are in search, recommendations, recognition, gaming, and pricing. The main challenge is the variety of information and data required.
Data engineering design patterns
There are patterns for things such as domain-driven design, enterprise architectures, continuous delivery, microservices, and many others.
But where are the data science and data engineering patterns?
Sometimes, data engineering reminds me of cowboy coding - many workarounds, immature technologies and lack of market best practices.
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...
2017 StrataHadoop SJC conference talk. https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/56047
Description:
So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it.
As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop.
Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #DataScienceHappiness.
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...
So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it.
As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop.
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Just ask Watson Seminar
This document discusses cognitive computing and analytics technologies. It provides examples of how cognitive systems can be applied, such as a toy that learns from child interactions. The document outlines a cognitive strategy and foundation that includes collecting and analyzing both structured and unstructured data. It also discusses the importance of cloud services, infrastructure, and security for cognitive systems. Finally, the document describes some of the cognitive computing APIs available from IBM Watson and how the set of APIs has expanded over time.
1.-DE-LECTURE-1-INTRO-TO-DATA-ENGG.pptx
Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale. It allows organizations to collect massive amounts of data and ensure the data is highly usable by data scientists and analysts. As data volumes continue to grow exponentially, data engineers are needed to process and channel data to enable fields like machine learning and deep learning.
Architecting Data For The Modern Enterprise - Data Summit 2017, Closing Keynote
The “Big Data era” has ushered in an avalanche of new technologies and approaches for delivering information and insights to business users. What is the role of the cloud in your analytical environment? How can you make your migration as seamless as possible? This closing keynote, delivered by Joe Caserta, a prominent consultant who has helped many global enterprises adopt Big Data, provided the audience with the inside scoop needed to supplement data warehousing environments with data intelligence—the amalgamation of Big Data and business intelligence.
This presentation was given as the closing keynote at DBTA's annual Data Summit in NYC.
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Presentation run@GSE UK, executive stream, analytics to AI, from an information architecture point of view, and role of IBM Z.
Meetup Data-science OVH
This document discusses data organization and big data architecture. It covers data organization topics like line of business, data office, data maturity levels, and a data maturity matrix. It then discusses big data architecture, including the roles of data scientists, analysts, engineers and more. Tooling and infrastructure are also summarized, like Hadoop, Spark, Kafka and data lakes. The document aims to provide an overview of how to structure and analyze large volumes of organizational data at multiple levels of maturity.
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The document proposes a data engineering solution called ManhattanDB to help Homerunner address challenges around integrating data from multiple sources, talent shortage, and limited productivity. ManhattanDB is a no-code platform that allows users to build data pipelines to ingest, transform, and analyze data. It promises to democratize access to data science and machine learning by unifying data engineering processes. Current clients are using ManhattanDB to build end-to-end data workflows for tasks like customer segmentation, transaction monitoring, and medical data transformation.
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BI & Analytics with Ms Power BI.pptx
Cecilia Brusatori is the founder and CEO of Magna Data Inc, which provides data analytics solutions and corporate training using Microsoft Power BI. She has a graduate degree in computer science and engineering and is a Microsoft Certified Trainer. In this presentation, she discusses Microsoft technologies for business intelligence and analytics like Power BI, SQL Server, and Azure. She demonstrates the content creation lifecycle in Power BI Desktop and how to share reports through workspaces, apps, and embedding. Finally, she reviews Power BI licensing options including free, pro, premium, and premium per user.
Data Science as a Service: Intersection of Cloud Computing and Data Science
Dr. Pouria Amirian explains data science, steps in a data science workflow and show some experiments in AzureML. He also mentions about big data issues in a data science project and solutions to them.
Data Science as a Service: Intersection of Cloud Computing and Data Science
Dr. Pouria Amirian from the University of Oxford explains Data Science and its relationship with Big Data and Cloud Computing. Then he illustrates using AzureML to perform a simple data science analytics.
Visionet Business Intelligence Solutions - Is your Business Intelligence real...
Visionet Business Intelligence Solutions - Is your Business Intelligence real...Visionet Systems, Inc.
Business intelligence systems promise to present one version of the truth by pulling data from multiple internal and external sources. However, getting accurate insights is challenging because data is often disparate and inconsistent. Visionet helps companies address this issue through enterprise data management practices like data standards and governance. They use a comprehensive methodology involving extraction, transformation, loading of data as well as application and metadata repository development to help organizations make informed decisions based on clean unified information.Qiagram
The document describes a business intelligence software called Qiagram that allows non-technical domain experts to easily explore and query complex datasets through a visual drag-and-drop interface without SQL or programming knowledge. It provides centralized data management, integration with various data sources, and self-service visual querying capabilities to help researchers gain insights from their data.
Big Data Meetup: Analytical Systems Evolution
Maxim Tereschenko (BI/BigData Lead, Provectus)
Topic: Analytical Systems Evolution - From Excel to Big Data Platforms and Data Lakes.
Tips and Tricks to be an Effective Data Scientist
Data Science is an evolving field, that requires a diverse skill set. From Analytical Techniques to Career Advice, this talk is full of practical tips that you can apply immediately to your job.
Analyst Webinar: The Role of a Logical Architecture in Modern Data and Analytics
Watch full webinar here: https://bit.ly/3Sl3BqC
Modern data and analytics requires empowerment of business users who possess domain knowledge and are best suited to have decentralized data ownership. But the current data landscape, which has a heavy dependency on data warehouse, is limited in terms of flexibility, extensibility and comprehensibility.
In this session, we will reflect on the findings from the recent BARC report “The Future of Data Architecture - Has the data warehouse had its day?” with one of its authors, Jacqueline Bloemen, Senior Analyst at BARC. Jacqueline will provide us with insights pulled from the global survey findings that led to the conclusions presented in the report.
We will also look into how implementing a logical data fabric (LDF, an extension of LDW), powered by data virtualization, helps organizations extend the life of their data warehouse implementations, whilst embracing the need to manage and eliminate data silos. You will take away an understanding of how a LDF helps you integrate, manage and deliver your data for data science and analytics programs to further your business goals.
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DataOps - Big Data and AI World London - March 2020 - Harvinder Atwal
DataOps - Big Data and AI World London - March 2020 - Harvinder Atwal
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...
Architecting Data For The Modern Enterprise - Data Summit 2017, Closing Keynote
Architecting Data For The Modern Enterprise - Data Summit 2017, Closing Keynote
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Data Science as a Service: Intersection of Cloud Computing and Data Science
Data Science as a Service: Intersection of Cloud Computing and Data Science
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Data Science as a Service: Intersection of Cloud Computing and Data Science
Visionet Business Intelligence Solutions - Is your Business Intelligence real...
Visionet Business Intelligence Solutions - Is your Business Intelligence real...
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Privacy in Computer Vision
This document discusses privacy concerns in computer vision and machine learning. It argues that privacy is a human right, but current centralized data practices and lack of transparency pose privacy risks. The document proposes solutions like federated learning and differential privacy to preserve privacy during data collection and training. It also discusses decentralized and anonymized approaches to processing, access control, and notifications to enforce privacy while deriving insights from computer vision systems. The document uses an example product from NATIX to illustrate how these techniques can be applied in applications like occupancy monitoring, parking management, and crowd density analysis.
The journey to Private AI, where Privacy-Preserving ML meets DLT
While the advances of AI and machine learning can benefit immensely people and societies, more and more concerns regarding privacy violations and data breaches are addressed. In this talk, first Privacy-Preserving Machine Learning including its aspects and potentials will be explored. Afterward, what Distributed Ledger Technologies (DLTs) can offer for a practical environment to enable private computation is discussed.
Blockchain, a disappointing tech talk
Blockchain has received an exceptional amount of attention by the media and large swaths of the population, due to its links to cryptocurrencies such as Bitcoin. Although many people in industry and academia believe that the technology is likely to be the next disruptor, the lack of deep understanding of the technology, its benefits and the reasoning behind the use cases can be a true barrier to reach such future.
In this talk, we try to comprehend the usage of such technology via categorising its applications in different layers. We discuss the limitations of blockchain technology and how the next generations of this technology aim to address those. Furthermore, we will picture the potential impacts and possible future field of activities of Distributed Ledger Technologies (including blockchain) through defining major artifacts.
How big is big data?
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Distributed File System and Why It Matters.
Thinking of a fast, resilient and scalable Internet unavoidably brings up the topic of re-architecting the entire web where eliminating the need of having a central server for file delivery is one of the key factors in this process. In this talk, the necessity of DFS will be discussed as well as some of the projects in this area.
Hadoop essential setup
This document provides an overview of Hadoop and essential setup. It discusses the origins of Hadoop stemming from Google research papers. It also mentions MapReduce and the Hadoop filesystem (HDFS). Additionally, it briefly introduces Hadoop ecosystem components like Pig, Hive, Spark, HBase and the lambda architecture for handling both fast and big data. It concludes by asking the reader about their own ideas.
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A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
06-18-2024-Princeton Meetup-Introduction to Milvus
06-18-2024-Princeton Meetup-Introduction to Milvus
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://github.com/milvus-io/milvus
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/142-17June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
Expand LLMs' knowledge by incorporating external data sources into LLMs and your AI applications.
Do People Really Know Their Fertility Intentions? Correspondence between Sel...
Fertility intention data from surveys often serve as a crucial component in modeling fertility behaviors. Yet, the persistent gap between stated intentions and actual fertility decisions, coupled with the prevalence of uncertain responses, has cast doubt on the overall utility of intentions and sparked controversies about their nature. In this study, we use survey data from a representative sample of Dutch women. With the help of open-ended questions (OEQs) on fertility and Natural Language Processing (NLP) methods, we are able to conduct an in-depth analysis of fertility narratives. Specifically, we annotate the (expert) perceived fertility intentions of respondents and compare them to their self-reported intentions from the survey. Through this analysis, we aim to reveal the disparities between self-reported intentions and the narratives. Furthermore, by applying neural topic modeling methods, we could uncover which topics and characteristics are more prevalent among respondents who exhibit a significant discrepancy between their stated intentions and their probable future behavior, as reflected in their narratives.
Telemetry Solution for Gaming (AWS Summit'24)
Discover the cutting-edge telemetry solution implemented for Alan Wake 2 by Remedy Entertainment in collaboration with AWS. This comprehensive presentation dives into our objectives, detailing how we utilized advanced analytics to drive gameplay improvements and player engagement.
Key highlights include:
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Tech Stack: Leveraging AWS services such as EKS for hosting, WAF for security, Karpenter for instance optimization, S3 for data storage, and OpenTelemetry Collector for data collection. EventBridge and Lambda were used for data compression, while Glue ETL and Athena facilitated data transformation and preparation.
Data Utilization: Transforming raw data into actionable insights with technologies like Glue ETL (PySpark scripts), Glue Crawler, and Athena, culminating in detailed visualizations with Tableau.
Achievements: Successfully managing 700 million to 1 billion events per month at a cost-effective rate, with significant savings compared to commercial solutions. This approach has enabled simplified scaling and substantial improvements in game design, reducing player churn through targeted adjustments.
Community Engagement: Enhanced ability to engage with player communities by leveraging precise data insights, despite having a small community management team.
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一比一原版(uom毕业证书)曼彻斯特大学毕业证如何办理
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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(uom毕业证书)曼彻斯特大学毕业证【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(uom毕业证书)曼彻斯特大学毕业证【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(uom毕业证书)曼彻斯特大学毕业证【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(uom毕业证书)曼彻斯特大学毕业证【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。
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Essential Skills for Family Assessment - Marital and Family Therapy and Couns...
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
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。
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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。
Health care analysis using sentimental analysis
This presentation is about health care analysis using sentiment analysis .
*this is very useful to students who are doing project on sentiment analysis
*
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
Tech Talk: Unstructured Data and Vector Databases
Speaker: Tim Spann (Zilliz)
Abstract: In this session, I will discuss the unstructured data and the world of vector databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture. Wrapping up with an overview of Milvus.
Introduction
Unstructured data, vector databases, traditional databases, similarity search
Vectors
Where, What, How, Why Vectors? We’ll cover a Vector Database Architecture
Introducing Milvus
What drives Milvus' Emergence as the most widely adopted vector database
Hi Unstructured Data Friends!
I hope this video had all the unstructured data processing, AI and Vector Database demo you needed for now. If not, there’s a ton more linked below.
My source code is available here
https://github.com/tspannhw/
Let me know in the comments if you liked what you saw, how I can improve and what should I show next? Thanks, hope to see you soon at a Meetup in Princeton, Philadelphia, New York City or here in the Youtube Matrix.
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/141-10June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
https://www.meetup.com/unstructured-data-meetup-new-york/events/301383476/?slug=unstructured-data-meetup-new-york&eventId=301383476
https://www.aicamp.ai/event/eventdetails/W2024062014
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...
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Data science team, a practice to setup
- 1. Data Science Team A practice to set up Omid Mogharian V0.2.1 - 06.02.2017
- 2. … it’s a mistake to treat data science teams like any old product group ... To build teams that create great data products, you have to find people with the skills and the curiosity to ask the big questions. You have build cross-disciplinary groups with people who are comfortable creating together… DJ Patil, U.S. Chief Data Scientist at White House Office of Science and Technology Policy
- 3. Roles Machine Learning Engineer Data Engineer/Architect Data Analyst Math & Statistics Interpretation/ Visualisation Modeling & ML Math & Statistics Machine Learning Developing Developing Infrastructure Design Operation Data Scientist Core Team Skills
- 4. Relations Data Science Team Operations/ System Administration Sales PO/ Customer relation BI App Development To make it real
- 5. Method CRISP-DM The Data Science Process Communications with Customer
- 6. How to? lambdaBig data and Fast data
- 7. Big Data Pipeline Job repository Scheduler/ Runner Incremental Runner Message QueueData Pipe Agent A practice for continuous analyse Severing layer
- 8. Application Environments Source Simulator Stage Production Sample Data Source Connector Big Data Continuous Analyse Application* Continuous Analyse Application* To bring accuracy * Whole software with several components which are explained in previous slide
- 9. References ● https://dzone.com/articles/lambda-architecture-with-apache-spark ● https://en.wikipedia.org/wiki/Lambda_architecture ● https://www.mapr.com/developercentral/lambda-architecture ● http://www.kdnuggets.com/2015/11/different-data-science-roles-industry.html ● http://www.kdnuggets.com/2016/03/data-science-process-rediscovered.html ● http://www.datascienceassn.org/sites/default/files/Building%20Data%20Scienc e%20Teams.pdf