The document describes Divolte Collector, a tool for collecting clickstream data from web servers and streaming it to Apache Hadoop and Kafka in a structured format. It parses web server log files and tags pages with JavaScript to collect data on user behavior. The data is mapped to Avro schemas for interoperability and enriched with information like geolocation before being sent to event transports. This allows for real-time analytics on user behavior as well as batch processing and training of machine learning models.
Walk through this hands-on workshop to expand your AWS technical skills. Gain credibility for your experience working with AWS by building proficiency with services and solutions in the areas of AWS Architecture Fundamentals.
AWS Fundamentals @Back2School by CloudZoneIdan Tohami
This class is all about the basics. Here you will learn about the services AWS has to offer in compute, storage, databases and various application services.
AWS 를 활용한 저지연 라이브 (Low Latency Live) 서비스 구현 - 류재춘 컨설턴트/에반젤리스트, GS Neot다 :: AW...Amazon Web Services Korea
라이브 방송의 성장과 더불어 최근 저지연 라이브 (Low Latency Live) 에 대한 관심이 높아지고 있습니다. 본 강연에서는 Low Latency Live 관련 기술적인 배경과 Latency를 줄이는 원리에 대한 설명을 하고, AWS 기반의 Low Latency Live 서비스를 구축하는 방법에 대해 소개합니다.
Empower Splunk and other SIEMs with the Databricks Lakehouse for CybersecurityDatabricks
Cloud, Cost, Complexity, and threat Coverage are top of mind for every security leader. The Lakehouse architecture has emerged in recent years to help address these concerns with a single unified architecture for all your threat data, analytics and AI in the cloud. In this talk, we will show how Lakehouse is essential for effective Cybersecurity and popular security use-cases. We will also share how Databricks empowers the security data scientist and analyst of the future and how this technology allows cyber data sets to be used to solve business problems.
How to Set Up a Cloud Cost Optimization Process for your EnterpriseRightScale
As cloud spend grows, enterprises need to set up internal processes to manage and optimize their cloud costs. This process will help organizations to accurately allocate and report on costs while minimizing wasted spend. In this webinar, experts from RightScale’s Cloud Cost Optimization team will share best practices in how to set up your own internal processes.
Walk through this hands-on workshop to expand your AWS technical skills. Gain credibility for your experience working with AWS by building proficiency with services and solutions in the areas of AWS Architecture Fundamentals.
AWS Fundamentals @Back2School by CloudZoneIdan Tohami
This class is all about the basics. Here you will learn about the services AWS has to offer in compute, storage, databases and various application services.
AWS 를 활용한 저지연 라이브 (Low Latency Live) 서비스 구현 - 류재춘 컨설턴트/에반젤리스트, GS Neot다 :: AW...Amazon Web Services Korea
라이브 방송의 성장과 더불어 최근 저지연 라이브 (Low Latency Live) 에 대한 관심이 높아지고 있습니다. 본 강연에서는 Low Latency Live 관련 기술적인 배경과 Latency를 줄이는 원리에 대한 설명을 하고, AWS 기반의 Low Latency Live 서비스를 구축하는 방법에 대해 소개합니다.
Empower Splunk and other SIEMs with the Databricks Lakehouse for CybersecurityDatabricks
Cloud, Cost, Complexity, and threat Coverage are top of mind for every security leader. The Lakehouse architecture has emerged in recent years to help address these concerns with a single unified architecture for all your threat data, analytics and AI in the cloud. In this talk, we will show how Lakehouse is essential for effective Cybersecurity and popular security use-cases. We will also share how Databricks empowers the security data scientist and analyst of the future and how this technology allows cyber data sets to be used to solve business problems.
How to Set Up a Cloud Cost Optimization Process for your EnterpriseRightScale
As cloud spend grows, enterprises need to set up internal processes to manage and optimize their cloud costs. This process will help organizations to accurately allocate and report on costs while minimizing wasted spend. In this webinar, experts from RightScale’s Cloud Cost Optimization team will share best practices in how to set up your own internal processes.
Architecting Snowflake for High Concurrency and High PerformanceSamanthaBerlant
Cloud Data Warehousing juggernaut Snowflake has raced out ahead of the pack to deliver a data management platform from which a wealth of new analytics can be run. Using Snowflake as a traditional data warehouse has some obvious cost advantages over a hardware solution. But the real value of Snowflake as a data platform lies in its ability to support a high-concurrency analytics platform using Kyligence Cloud, powered by Apache Kylin.
In this presentation, Senior Solutions Architect Robert Hardaway will describe a modern data service architecture using precomputation and distributed indexes to provide interactive analytics to hundreds or even thousands of users running against very large Snowflake datasets (TBs to PBs).
"Introduction to FinOps" – Greg VanderWel at Chicago AWS user groupAWS Chicago
Chicago's AWS user group
September 24th 2019
FinOps in AWS
"Introduction to FinOps" – Greg VanderWel Area Director, Apptio
FinOps in AWS - managing cost, spending, and budgets in AWS accounts
AWS Certified Cloud Practitioner Course S11-S17Neal Davis
This deck contains the slides from our AWS Certified Cloud Practitioner video course. It covers:
Section 11 Databases and Analytics
Section 12 Management and Governance
Section 13 AWS Cloud Security and Identity
Section 14 Architecting for the Cloud
Section 15 Accounts, Billing and Support
Section 16 Migration, Machine Learning and More
Section 17 Exam Preparation and Tips
Full course can be found here: https://digitalcloud.training/courses/aws-certified-cloud-practitioner-video-course/
Real-Time Anomaly Detection with Spark MLlib, Akka and CassandraNatalino Busa
We present a solution for streaming anomaly detection, named “Coral”, based on Spark, Akka and Cassandra. In the system presented, we run Spark to run the data analytics pipeline for anomaly detection. By running Spark on the latest events and data, we make sure that the model is always up-to-date and that the amount of false positives is kept low, even under changing trends and conditions. Our machine learning pipeline uses Spark decision tree ensembles and k-means clustering. Once the model is trained by Spark, the model’s parameters are pushed to the Streaming Event Processing Layer, implemented in Akka. The Akka layer will then score 1000s of event per seconds according to the last model provided by Spark. Spark and Akka communicate which each other using Cassandra as a low-latency data store. By doing so, we make sure that every element of this solution is resilient and distributed. Spark performs micro-batches to keep the model up-to-date while Akka detects the new anomalies by using the latest Spark-generated data model. The project is currently hosted on Github. Have a look at : http://coral-streaming.github.io
Strategic Approach To Data Migration Project PlanSlideTeam
Presenting this set of slides with name Strategic Approach To Data Migration Project Plan. This is a six stage process. The stages in this process are Plan, Develop, Validate, Migrate Stage, Test. This is a completely editable PowerPoint presentation and is available for immediate download. Download now and impress your audience. https://bit.ly/3CTswep
Driven by new business processes and regulation, the need for data exchange between organisations is heavily increasing. As 'Trading Partners', companies exchange data using electronic messages or 'events'. The automation of this B2B communication is very different compared to the use of Enterprise Service Buses within an organisation.
This presentation will show different insights in protocols and message formats which are being used in business, from file transfer and EDI until AS2 with different XML dialects. Also security and integration with 'endpoints' are explained.
Cloud Migration, Application Modernization, and Security Tom Laszewski
As AWS continues to expand, enterprise customers are looking to our partner ecosystem to assist in migrating their workloads to the cloud. This session describes the challenges, lessons learned and best practices for large scale application migrations. We will use real examples from our consulting partners and AWS Professional Services to illustrate how to move workloads to the cloud while modernizing the associated applications to take advantage of AWS’ unique benefits. We will also dive into how to use an array of AWS services and features to improve a customer’s security posture as they are migrating and once they are up and running in the cloud
Slides of QCon London 2016 talk. How stream processing is used within the Uber's Marketplace system to solve a wide range problems, including but not limited to realtime indexing and querying of geospatial time series, aggregation and computing of streaming data, and extracting patterns from data streams. In addition, it will touch upon various TimeSeries analysis and predictions. The underlying systems utilize many open source technologies such as Apache Kafka, Samza and Spark streaming.
Data Migration PowerPoint Presentation Slides SlideTeam
Presenting this set of slides with name - Data Migration PowerPoint Presentation Slides. The deck constituents are Data Migration, Data Transfers, Information Migration.
Manufacturers have an abundance of data, whether from connected sensors, plant systems, manufacturing systems, claims systems and external data from industry and government. Manufacturers face increased challenges from continually improving product quality, reducing warranty and recall costs to efficiently leveraging their supply chain. For example, giving the manufacturer a complete view of the product and customer information integrating manufacturing and plant floor data, with as built product configurations with sensor data from customer use to efficiently analyze warranty claim information to reduce detection to correction time, detect fraud and even become proactive around issues requires a capable enterprise data hub that integrates large volumes of both structured and unstructured information. Learn how an enterprise data hub built on Hadoop provides the tools to support analysis at every level in the manufacturing organization.
Training for AWS Solutions Architect at http://zekelabs.com/courses/amazon-web-services-training-bangalore/.Training for AWS Solutions Architect at http://zekelabs.com/courses/amazon-web-services-training-bangalore/. This slide describes about features of simple storage service, s3 buckets, s3-static web hosting, cross region replication, storage classes and comparison, glacier, transfer acceleration, life cycle management, security and encryption
___________________________________________________
zekeLabs is a Technology training platform. We provide instructor led corporate training and classroom training on Industry relevant Cutting Edge Technologies like Big Data, Machine Learning, Natural Language Processing, Artificial Intelligence, Data Science, Amazon Web Services, DevOps, Cloud Computing and Frameworks like Django,Spring, Ruby on Rails, Angular 2 and many more to Professionals.
Reach out to us at www.zekelabs.com or call us at +91 8095465880 or drop a mail at info@zekelabs.com
Migrating on premises and cloud contents to SharePoint Online at no cost with...Juan Carlos Gonzalez
Presentation delivered at M365 Philly virtual that took place on the 23rd of July 2020. In my session I talked about the migration tools provided by Microsoft to move On-Premises and Cloud contents to SharePoint Online and OneDrive For Business.
Visit http:aws.amazon.com/hpc for more information about HPC on AWS.
High Performance Computing (HPC) allows scientists and engineers to solve complex science, engineering, and business problems using applications that require high bandwidth, low latency networking, and very high compute capabilities. AWS allows you to increase the speed of research by running high performance computing in the cloud and to reduce costs by providing Cluster Compute or Cluster GPU servers on-demand without large capital investments. You have access to a full-bisection, high bandwidth network for tightly-coupled, IO-intensive workloads, which enables you to scale out across thousands of cores for throughput-oriented applications.
Architecting Snowflake for High Concurrency and High PerformanceSamanthaBerlant
Cloud Data Warehousing juggernaut Snowflake has raced out ahead of the pack to deliver a data management platform from which a wealth of new analytics can be run. Using Snowflake as a traditional data warehouse has some obvious cost advantages over a hardware solution. But the real value of Snowflake as a data platform lies in its ability to support a high-concurrency analytics platform using Kyligence Cloud, powered by Apache Kylin.
In this presentation, Senior Solutions Architect Robert Hardaway will describe a modern data service architecture using precomputation and distributed indexes to provide interactive analytics to hundreds or even thousands of users running against very large Snowflake datasets (TBs to PBs).
"Introduction to FinOps" – Greg VanderWel at Chicago AWS user groupAWS Chicago
Chicago's AWS user group
September 24th 2019
FinOps in AWS
"Introduction to FinOps" – Greg VanderWel Area Director, Apptio
FinOps in AWS - managing cost, spending, and budgets in AWS accounts
AWS Certified Cloud Practitioner Course S11-S17Neal Davis
This deck contains the slides from our AWS Certified Cloud Practitioner video course. It covers:
Section 11 Databases and Analytics
Section 12 Management and Governance
Section 13 AWS Cloud Security and Identity
Section 14 Architecting for the Cloud
Section 15 Accounts, Billing and Support
Section 16 Migration, Machine Learning and More
Section 17 Exam Preparation and Tips
Full course can be found here: https://digitalcloud.training/courses/aws-certified-cloud-practitioner-video-course/
Real-Time Anomaly Detection with Spark MLlib, Akka and CassandraNatalino Busa
We present a solution for streaming anomaly detection, named “Coral”, based on Spark, Akka and Cassandra. In the system presented, we run Spark to run the data analytics pipeline for anomaly detection. By running Spark on the latest events and data, we make sure that the model is always up-to-date and that the amount of false positives is kept low, even under changing trends and conditions. Our machine learning pipeline uses Spark decision tree ensembles and k-means clustering. Once the model is trained by Spark, the model’s parameters are pushed to the Streaming Event Processing Layer, implemented in Akka. The Akka layer will then score 1000s of event per seconds according to the last model provided by Spark. Spark and Akka communicate which each other using Cassandra as a low-latency data store. By doing so, we make sure that every element of this solution is resilient and distributed. Spark performs micro-batches to keep the model up-to-date while Akka detects the new anomalies by using the latest Spark-generated data model. The project is currently hosted on Github. Have a look at : http://coral-streaming.github.io
Strategic Approach To Data Migration Project PlanSlideTeam
Presenting this set of slides with name Strategic Approach To Data Migration Project Plan. This is a six stage process. The stages in this process are Plan, Develop, Validate, Migrate Stage, Test. This is a completely editable PowerPoint presentation and is available for immediate download. Download now and impress your audience. https://bit.ly/3CTswep
Driven by new business processes and regulation, the need for data exchange between organisations is heavily increasing. As 'Trading Partners', companies exchange data using electronic messages or 'events'. The automation of this B2B communication is very different compared to the use of Enterprise Service Buses within an organisation.
This presentation will show different insights in protocols and message formats which are being used in business, from file transfer and EDI until AS2 with different XML dialects. Also security and integration with 'endpoints' are explained.
Cloud Migration, Application Modernization, and Security Tom Laszewski
As AWS continues to expand, enterprise customers are looking to our partner ecosystem to assist in migrating their workloads to the cloud. This session describes the challenges, lessons learned and best practices for large scale application migrations. We will use real examples from our consulting partners and AWS Professional Services to illustrate how to move workloads to the cloud while modernizing the associated applications to take advantage of AWS’ unique benefits. We will also dive into how to use an array of AWS services and features to improve a customer’s security posture as they are migrating and once they are up and running in the cloud
Slides of QCon London 2016 talk. How stream processing is used within the Uber's Marketplace system to solve a wide range problems, including but not limited to realtime indexing and querying of geospatial time series, aggregation and computing of streaming data, and extracting patterns from data streams. In addition, it will touch upon various TimeSeries analysis and predictions. The underlying systems utilize many open source technologies such as Apache Kafka, Samza and Spark streaming.
Data Migration PowerPoint Presentation Slides SlideTeam
Presenting this set of slides with name - Data Migration PowerPoint Presentation Slides. The deck constituents are Data Migration, Data Transfers, Information Migration.
Manufacturers have an abundance of data, whether from connected sensors, plant systems, manufacturing systems, claims systems and external data from industry and government. Manufacturers face increased challenges from continually improving product quality, reducing warranty and recall costs to efficiently leveraging their supply chain. For example, giving the manufacturer a complete view of the product and customer information integrating manufacturing and plant floor data, with as built product configurations with sensor data from customer use to efficiently analyze warranty claim information to reduce detection to correction time, detect fraud and even become proactive around issues requires a capable enterprise data hub that integrates large volumes of both structured and unstructured information. Learn how an enterprise data hub built on Hadoop provides the tools to support analysis at every level in the manufacturing organization.
Training for AWS Solutions Architect at http://zekelabs.com/courses/amazon-web-services-training-bangalore/.Training for AWS Solutions Architect at http://zekelabs.com/courses/amazon-web-services-training-bangalore/. This slide describes about features of simple storage service, s3 buckets, s3-static web hosting, cross region replication, storage classes and comparison, glacier, transfer acceleration, life cycle management, security and encryption
___________________________________________________
zekeLabs is a Technology training platform. We provide instructor led corporate training and classroom training on Industry relevant Cutting Edge Technologies like Big Data, Machine Learning, Natural Language Processing, Artificial Intelligence, Data Science, Amazon Web Services, DevOps, Cloud Computing and Frameworks like Django,Spring, Ruby on Rails, Angular 2 and many more to Professionals.
Reach out to us at www.zekelabs.com or call us at +91 8095465880 or drop a mail at info@zekelabs.com
Migrating on premises and cloud contents to SharePoint Online at no cost with...Juan Carlos Gonzalez
Presentation delivered at M365 Philly virtual that took place on the 23rd of July 2020. In my session I talked about the migration tools provided by Microsoft to move On-Premises and Cloud contents to SharePoint Online and OneDrive For Business.
Visit http:aws.amazon.com/hpc for more information about HPC on AWS.
High Performance Computing (HPC) allows scientists and engineers to solve complex science, engineering, and business problems using applications that require high bandwidth, low latency networking, and very high compute capabilities. AWS allows you to increase the speed of research by running high performance computing in the cloud and to reduce costs by providing Cluster Compute or Cluster GPU servers on-demand without large capital investments. You have access to a full-bisection, high bandwidth network for tightly-coupled, IO-intensive workloads, which enables you to scale out across thousands of cores for throughput-oriented applications.
Contents and talk by Friso van Vollenhoven
Data is used in batches, ad hoc or streaming. The amount of data in (big) data clusters is enormous. Browsers use tagging to manage sessions on client side and to evaluate traffic. Divolte collects data in an effectively stateless environment. Use this data to predict, recommend and target based on behavior.
From zero to hero - Easy log centralization with Logstash and ElasticsearchRafał Kuć
Presentation I gave during DevOps Days Warsaw 2014 about combining Elasticsearch, Logstash and Kibana together or use our Logsene solution instead of Elasticsearch.
From Zero to Hero - Centralized Logging with Logstash & ElasticsearchSematext Group, Inc.
Originally presented at DevOpsDays Warsaw 2014. How to set up centralized logging either using ELK stack - Logstash, Elasticsearch, and Kibana or using Logsene.
(WEB301) Operational Web Log Analysis | AWS re:Invent 2014Amazon Web Services
Log data contains some of the most valuable raw information you can gather and analyze about your infrastructure and applications. Amid the mess of confusing lines of seemingly random text can be hints about performance, security, flaws in code, user access patterns, and other operational data. Without the proper tools, finding insights in these logs can be like searching for a hay-colored needle in a haystack. In this session you learn what practices and patterns you can easily implement that can help you better understand your log files. You see how you can customize web logs to add more information to them, how to digest logs from around your infrastructure, and how to analyze your log files in near real time.
Orchestrate Event-Driven Infrastructure with SaltStackLove Nyberg
Saltstack is by it's design a event driven configuration management tool. In talk will do a deep dive into salt reactor, runners and beacon systems. Talk will also cover a demo of event driven application releases process.
OSDC 2015: Pere Urbon | Scaling Logstash: A Collection of War StoriesNETWAYS
In this talk, we will cover several strategies for successfully scaling Logstash. Through the lens of several real-life war stories, you willl learn how to make Logstash sing alongside RabbitMQ, Redis, ZeroMQ, Kafka and much more. If you are ready to grow at scale and make your infrastructure more resilient, this talk is for you.
Discusses how new approaches to managing business risk and software services (like Dev Ops and Platform Engineering/Management) can draw from their forefather concepts: Operations Management and Decision Science.
Derek Pearcy - Reading Users' Minds For Fun And Profitbolt peters
What users say will generally be different from what they do -- this is true, but what's a good strategy when you can't get to enough of your users? What if you could answer some really big questions by performing simple research on ALL of your users? This is the same style of approach taken by companies like Google and Zynga, to target user research efforts which have made them what they are today. Log analysis, done well, can seem like mind-reading. If you haven't done it before: there's nothing to fear.
How to create a Devcontainer for your Python projectGoDataDriven
Prevent mis-aligned environments between developers, onboard new-joiners faster, and reduce the time it takes to take your project to production. Sounds interesting? Devcontainers can help you with this. Devcontainers allow you to connect your IDE to a running Docker container and develop inside it. This gives you all the benefits of reproducibility that Docker is known for. In this talk, I will walk you through what Devcontainers are, why they might be useful for you, and how to create one for your Python project using VSCode.
Using Graph Neural Networks To Embrace The Dependency In Your Data by Usman Z...GoDataDriven
Many machine learning models we use today have the core assumption that our data needs to be tabular, but how often is this truly the case? What if our data points are not independent? By ignoring the potential interrelatedness of our data, do we lose meaningful information that our models cannot leverage? In this talk, we shall explore graph neural networks and highlight how they can solve interesting problems in a way that is intractable when limiting ourselves to using tabular data. We will look at the limitations of common algorithms and highlight how some clever linear algebra enables us to incorporate more meaningful information into our models. Social network data is a popular example of where relationships are relevant but relationships exist in many types of data where it may not be so obvious. Whether it's e-commerce, logistics or molecular data, relationships within your data likely exist and making use of them can be incredibly powerful. This talk will hopefully spark your curiosity and provide you with a way of looking at problems from a new angle. It is intended for anyone with an interest in machine learning and will only lightly touch on some technical details.
Common Issues With Time Series by Vadim Nelidov - GoDataFest 2022GoDataDriven
Time-series data is all around us: from logistics to digital marketing, from pricing to stock markets - it’s hard to imagine a modern business that has no time series data to forecast. However, mastering such forecasting is not an easy task. For this talk, we have collected a list of common time series issues that digital fortune tellers commonly run into. You will learn how to identify, understand and resolve them better. This will include stabilising divergent time series, handling outliers without anomaly propagation, reducing the impact of noise and more.
MLOps CodeBreakfast on AWS - GoDataFest 2022GoDataDriven
During the MLOps CodeBreakfast, we will be giving an introduction to MLOps. After this introduction, we will go into more detail on how to implement and deploy a Machine Learning pipeline on both Azure and AWS.
MLOps CodeBreakfast on Azure - GoDataFest 2022GoDataDriven
During the MLOps CodeBreakfast, we will be giving an introduction to MLOps. After this introduction, we will go into more detail on how to implement and deploy a Machine Learning pipeline on both Azure and AWS.
Tableau vs. Power BI by Juan Manuel Perafan - GoDataFest 2022GoDataDriven
In this talk, we will compare the most widely used BI tools in the market from the perspective of a mature data organization. The focus of this talk WON’T be on flashy features nor superficial sales talk. We will compare both tools in terms of how well they fit in with DataOps best practices. How do they rank in terms of speed of delivery, governance, robustness, and analytical capabilities.
Deploying a Modern Data Stack by Lasse Benninga - GoDataFest 2022GoDataDriven
Deploy your own modern data stack using open source components usingTerraform cloud-agnostic tooling. By leveraging open-source components you can deploy a state-of-the-art modern data platform in a day. What are the pro's and con's of “build-it-yourself" in the data+analytics space?
AWS Well-Architected Webinar Security - Ben de HaanGoDataDriven
The security pillar encompasses the ability to protect information, systems, and assets while delivering business value through risk assessments and mitigation strategies. This presentation will provide in-depth, best-practice guidance for architecting secure systems on AWS.
The 7 Habits of Effective Data Driven CompaniesGoDataDriven
1. Start searching use cases with value & impact: without use cases, nobody will want to draft a data strategy
Where do you want to go? Draft a clear Customer Experience that you want to create and think about the organization & data strategy to get there!
2. Get Tech (data scientists and engineers) and Business (Product Management & Commercial) on the same table: create a solid foundation.
3. Start with communities of practice to learn & experiment together and build the capability.
4. Stop talking about data. Start experimenting and doing.
5. Product Management needs to get real about data. (start training these capabilities)
DevOps for Data Science on Azure - Marcel de Vries (Xpirit) and Niels Zeilema...GoDataDriven
The typical organizational model is that teams are in constant flux, are created for work, are only responsible for the change and are not empowered, or lack trust, to run products. A high performance organization model allows teams to take full responsibility for cost, compliance and security, and lets them own their own incidents. This improves quality, change failure rates, lower costs and leads to more happy employees. DevOps is about creating with the end in mind, cross-functional autonomous teams and end-tn-end responsibility. You build it, you run it. You break it, you fix it. This means you want to automate everything in a CI/CD pipeline. Roll-forward, don't roll-back. DevOps principles play an important role in a data-driven maturity model. Continuous prototyping and a data mindset and skills for everybody. In a Data Science Workflow combining input data and deriving the model features usually requires the most of the work, and lots of iterations before its done. Implement features one-by-one. So, start with a baseline model and compare this against more complex models, to see if additional complexity is worth the performance gain. The result of a data scientist is a trained model. Such a model contains 4 components: input data, derived features, chosen model type and hyperparameters. A trained model is always the combination of data and the code. So where do you run this trained model? Model management is versioning code but not the data. A model management server stores hyperparameters, performance metrics, metadata, trained models. IN a data science pipeline, we have two components for deployment: the application and the trained model. So we split the pipeline into parts: a build pipeline, a train pipeline and a deploy pipeline. A complete pipeline mapped to azure components would look largely like this: An Azure DevOps Build pipeline, an Azure ML Training pipeline and an Azure DevOps Release pipeline.
Artificial intelligence in actions: delivering a new experience to Formula 1 ...GoDataDriven
At GoDataFest 2019, Guy Kfir presented how AI delivers a new experience to Formula 1 fans across the world. AWS fuels the analytics through machine learning. Did you know a Formula 1 race car contains 120 sensors and generated 3 GB of data every race at 1,500 data points per second? AWS developed several applications, including overtake possibility, pitstop advantage. How important is it for your company to invest in Machine Learning and AI? There are three scenario's for AI/ML success: Automation, Enrichment and Invention. So, what are you waiting for: create the loop, advance your data strategy and organize for succes. To get started identify AI/ML use cases, educate yourself, start with AI services and move to Amazon Sagemaker, engage with AWS, consider the partner eco system (like GoDataDriven or Binx).
Smart application on Azure at Vattenfall - Rens Weijers & Peter van 't HofGoDataDriven
During GoDataFest 2019, Rens Weijers, manager data & strategy and Peter van ' t Hof, data engineer, share the story of how Vattenfall develops smart applications on Azure. Vattenfall has the ambition to transition to fossil-free living within one generation. But what about decentral energy solutions in the Customers & Solutions business unit? Data is key to help customers to reduce their CO2 footprint. Azure enables Vattenfall to be personal and relevant towards customers.
Democratizing AI/ML with GCP - Abishay Rao (Google) at GoDataFest 2019GoDataDriven
Every company today is talking about AI/ML, but when most companies talk about AI/ML in their transformation journey, you hear terms like Proof of Concept, Feasibility Study, Pilot, A/B Test. We are at the peak of AI's hype, but only 12% of enterprises have deployed AI in production. Google aims to make big data processing available for everyone, the possiblities of Big Query ML are endless: Marketing, retail, industrial and IoT, media, gaming, and so fort.
Discover the innovative and creative projects that highlight my journey throu...dylandmeas
Discover the innovative and creative projects that highlight my journey through Full Sail University. Below, you’ll find a collection of my work showcasing my skills and expertise in digital marketing, event planning, and media production.
What is the TDS Return Filing Due Date for FY 2024-25.pdfseoforlegalpillers
It is crucial for the taxpayers to understand about the TDS Return Filing Due Date, so that they can fulfill your TDS obligations efficiently. Taxpayers can avoid penalties by sticking to the deadlines and by accurate filing of TDS. Timely filing of TDS will make sure about the availability of tax credits. You can also seek the professional guidance of experts like Legal Pillers for timely filing of the TDS Return.
Memorandum Of Association Constitution of Company.pptseri bangash
www.seribangash.com
A Memorandum of Association (MOA) is a legal document that outlines the fundamental principles and objectives upon which a company operates. It serves as the company's charter or constitution and defines the scope of its activities. Here's a detailed note on the MOA:
Contents of Memorandum of Association:
Name Clause: This clause states the name of the company, which should end with words like "Limited" or "Ltd." for a public limited company and "Private Limited" or "Pvt. Ltd." for a private limited company.
https://seribangash.com/article-of-association-is-legal-doc-of-company/
Registered Office Clause: It specifies the location where the company's registered office is situated. This office is where all official communications and notices are sent.
Objective Clause: This clause delineates the main objectives for which the company is formed. It's important to define these objectives clearly, as the company cannot undertake activities beyond those mentioned in this clause.
www.seribangash.com
Liability Clause: It outlines the extent of liability of the company's members. In the case of companies limited by shares, the liability of members is limited to the amount unpaid on their shares. For companies limited by guarantee, members' liability is limited to the amount they undertake to contribute if the company is wound up.
https://seribangash.com/promotors-is-person-conceived-formation-company/
Capital Clause: This clause specifies the authorized capital of the company, i.e., the maximum amount of share capital the company is authorized to issue. It also mentions the division of this capital into shares and their respective nominal value.
Association Clause: It simply states that the subscribers wish to form a company and agree to become members of it, in accordance with the terms of the MOA.
Importance of Memorandum of Association:
Legal Requirement: The MOA is a legal requirement for the formation of a company. It must be filed with the Registrar of Companies during the incorporation process.
Constitutional Document: It serves as the company's constitutional document, defining its scope, powers, and limitations.
Protection of Members: It protects the interests of the company's members by clearly defining the objectives and limiting their liability.
External Communication: It provides clarity to external parties, such as investors, creditors, and regulatory authorities, regarding the company's objectives and powers.
https://seribangash.com/difference-public-and-private-company-law/
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While the MOA lays down the company's fundamental principles, it is not entirely immutable. It can be amended, but only under specific circumstances and in compliance with legal procedures. Amendments typically require shareholder
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LEARNING OBJECTIVES
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2. Explore the sustainability implementation model, focusing on effective measures and reporting strategies to track and communicate sustainability efforts.
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CONTENTS
1. Introduction and Key Concepts of Sustainability
2. Principles and Practices of Sustainability
3. Measures and Reporting in Sustainability
4. Sustainability Implementation & Best Practices
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The Parable of the Pipeline a book every new businessman or business student ...
Divolte collector overview
1. GoDataDriven
PROUDLY PART OF THE XEBIA GROUP
@asnare / @fzk / @godatadriven
signal@godatadriven.com
Divolte Collector
Andrew Snare / Friso van Vollenhoven
Because life’s too short for log file parsing
3. How do we use our data?
•Ad hoc
•Batch
•Streaming
4. USER
HTTP request:
/org/apache/hadoop/io/IOUtils.html
log transport
service
log event:
2012-07-01T06:00:02.500Z /org/apache/hadoop/io/IOUtils.html
transport logs to
compute cluster
off line analytics /
model training
batch update
model state
serve model result
(e.g. recommendations) streaming log
processing
streaming update
model state
Typical web optimization architecture
6. How did it get there?
Option 1: parse HTTP server logs
•Ship log files on a schedule
•Parse using MapReduce jobs
•Batch analytics jobs feed online systems
7. HTTP server log parsing
•Inherently batch oriented
•Schema-less (URL format is the schema)
•Initial job to parse logs into structured format
•Usually multiple versions of parsers required
•Requires sessionizing
•Logs usually have more than you ask for (bots,
image requests, spiders, health check, etc.)
8. Stream HTTP server logs
access.log
Message Queue or Event Transport
(Kafka, Flume, etc.)
EVENTS
tail -F
EVENTS
OTHER
CONSUMERS
9. How did it get there?
Option 2: stream HTTP server logs
•tail -F logfiles
•Use a queue for transport (e.g. Flume or Kafka)
•Parse logs on the fly
•Or write semi-schema’d logs, like JSON
•Parse again for batch work load
10. Stream HTTP server logs
•Allows for near real-time event handling when
consuming from queues
•Sessionizing? Duplicates? Bots?
•Still requires parser logic
•No schema
12. How did it get there?
Option 3: tagging
•Instrument pages with special ‘tag’, i.e. special
JavaScript or image just for logging the request
•Create special endpoint that handles the tag
request in a structured way
•Tag endpoint handles logging the events
13. Tagging
•Not a new idea (Google Analytics, Omniture,
etc.)
•Less garbage traffic, because a browser is
required to evaluate the tag
•Event logging is asynchronous
•Easier to do inflight processing (apply a schema,
add enrichments, etc.)
•Allows for custom events (other than page view)
14. Also…
•Manage session through cookies on the client
side
•Incoming data is already sessionized
•Extract additional information from clients
•Screen resolution
•Viewport size
•Timezone
18. Divolte Collector:Vision
•Focus purely on collection
•Processing is a separate concern
•Minimal on the fly enrichment
•The Hadoop tools ecosystem evolves too fast to compete
(SQL solutions, streaming, machine learning, etc.)
•Just provide data
•Data source for custom data science solutions
•Not a web analytics solution per se; descriptive web
analytics is a side effect
•Use cases will vary, try not too many assumptions about
users’ needs
19. Divolte Collector:Vision
•Solve the web specific tricky parts
•ID generation on client side (JavaScript)
•In-stream duplicate detection
•Schema!
•Data will be written in a schema-evolution-
friendly open format (Apache Avro)
•No arbitrary (JSON) objects
20. Javascript based tag
<body>
<!--
Your page content here.
-->
<!--
Include Divolte Collector
just before the closing
body tag
-->
<script src="//example.com/divolte.js"
defer async>
</script>
</body>
26. Useful performance
Requests per second: 14010.80 [#/sec] (mean)
Time per request: 0.571 [ms] (mean)
Time per request: 0.071 [ms] (mean, across all concurrent requests)
Transfer rate: 4516.55 [Kbytes/sec] received
Connection Times (ms)
min mean[+/-sd] median max
Connect: 0 0 0.1 0 1
Processing: 0 0 0.2 0 3
Waiting: 0 0 0.2 0 3
Total: 0 1 0.2 1 3
Percentage of the requests served within a certain time (ms)
50% 1
66% 1
75% 1
80% 1
90% 1
95% 1
98% 1
99% 1
100% 3 (longest request)
27. Custom events
divolte.signal('addToBasket', {
productId: 309125,
count: 1
})
In the page (Javascript)
map eventParameter('productId') onto 'basketProductId'
map eventParameter('count') onto 'basketNumProducts'
In the mapping (Groovy)
36. Approach
1. Pick n images randomly
2. Optimise displayed image using bandit optimisation
3. After X iterations:
•Pick n / 2 new images randomly
•Select n / 2 images from existing set using learned
distribution
•Construct new set of images using half of existing
set and newly selected random images
4. Goto 2
37. Bayesian Bandits
•For each image, keep track of:
•Number of impressions
•Number of clicks
•When serving an image:
•Draw a random number from a Beta
distribution with parameters alpha = # of clicks,
beta = # of impressions, for each image
•Show image where sample value is largest
39. Prototype UI
class HomepageHandler(ShopHandler):
@coroutine
def get(self):
# Hard-coded ID for a pretty flower.
# Later this ID will be decided by the bandit optmization.
winner = '15442023790'
# Grab the item details from our catalog service.
top_item = yield self._get_json('catalog/item/%s' % winner)
# Render the homepage
self.render(
'index.html',
top_item=top_item)
40. Prototype UI
<div class="col-md-6">
<h4>Top pick:</h4>
<p>
<!-- Link to the product page with a source identifier for tracking -->
<a href="/product/{{ top_item['id'] }}/#/?source=top_pick">
<img class="img-responsive img-rounded" src="{{ top_item['variants']['Medium']['img_source'] }}">
<!-- Signal that we served an impression of this image -->
<script>divolte.signal('impression', { source: 'top_pick', productId: '{{ top_item['id'] }}'})</script>
</a>
</p>
<p>
Photo by {{ top_item['owner']['real_name'] or top_item['owner']['user_name']}}
</p>
</div>
41. Data collection in Divolte Collector
{
"name": "source",
"type": ["null", "string"],
"default": null
}
def locationUri = parse location() to uri
when eventType().equalTo('pageView') apply {
def fragmentUri = parse locationUri.rawFragment() to uri
map fragmentUri.query().value('source') onto 'source'
}
when eventType().equalTo('impression') apply {
map eventParameters().value('productId') onto 'productId'
map eventParameters().value('source') onto 'source'
}
43. Consuming Kafka in Python
def start_consumer(args):
# Load the Avro schema used for serialization.
schema = avro.schema.Parse(open(args.schema).read())
# Create a Kafka consumer and Avro reader. Note that
# it is trivially possible to create a multi process
# consumer.
consumer = KafkaConsumer(args.topic,
client_id=args.client,
group_id=args.group,
metadata_broker_list=args.brokers)
reader = avro.io.DatumReader(schema)
# Consume messages.
for message in consumer:
handle_event(message, reader)
44. Consuming Kafka in Python
def handle_event(message, reader):
# Decode Avro bytes into a Python dictionary.
message_bytes = io.BytesIO(message.value)
decoder = avro.io.BinaryDecoder(message_bytes)
event = reader.read(decoder)
# Event logic.
if 'top_pick' == event['source'] and 'pageView' == event['eventType']:
# Register a click.
redis_client.hincrby(
ITEM_HASH_KEY,
CLICK_KEY_PREFIX + ascii_bytes(event['productId']),
1)
elif 'top_pick' == event['source'] and 'impression' == event['eventType']:
# Register an impression and increment experiment count.
p = redis_client.pipeline()
p.incr(EXPERIMENT_COUNT_KEY)
p.hincrby(
ITEM_HASH_KEY,
IMPRESSION_KEY_PREFIX + ascii_bytes(event['productId']),
1)
experiment_count, ingnored = p.execute()
if experiment_count == REFRESH_INTERVAL:
refresh_items()
45. def refresh_items():
# Fetch current model state. We convert everything to str.
current_item_dict = redis_client.hgetall(ITEM_HASH_KEY)
current_items = numpy.unique([k[2:] for k in current_item_dict.keys()])
# Fetch random items from ElasticSearch. Note we fetch more than we need,
# but we filter out items already present in the current set and truncate
# the list to the desired size afterwards.
random_items = [
ascii_bytes(item)
for item in random_item_set(NUM_ITEMS + NUM_ITEMS - len(current_items) // 2)
if not item in current_items][:NUM_ITEMS - len(current_items) // 2]
# Draw random samples.
samples = [
numpy.random.beta(
int(current_item_dict[CLICK_KEY_PREFIX + item]),
int(current_item_dict[IMPRESSION_KEY_PREFIX + item]))
for item in current_items]
# Select top half by sample values. current_items is conveniently
# a Numpy array here.
survivors = current_items[numpy.argsort(samples)[len(current_items) // 2:]]
# New item set is survivors plus the random ones.
new_items = numpy.concatenate([survivors, random_items])
# Update model state to reflect new item set. This operation is atomic
# in Redis.
p = redis_client.pipeline(transaction=True)
p.set(EXPERIMENT_COUNT_KEY, 1)
p.delete(ITEM_HASH_KEY)
for item in new_items:
p.hincrby(ITEM_HASH_KEY, CLICK_KEY_PREFIX + item, 1)
p.hincrby(ITEM_HASH_KEY, IMPRESSION_KEY_PREFIX + item, 1)
p.execute()
46. Serving a recommendation
class BanditHandler(web.RequestHandler):
redis_client = None
def initialize(self, redis_client):
self.redis_client = redis_client
@gen.coroutine
def get(self):
# Fetch model state.
item_dict = yield gen.Task(self.redis_client.hgetall, ITEM_HASH_KEY)
items = numpy.unique([k[2:] for k in item_dict.keys()])
# Draw random samples.
samples = [
numpy.random.beta(
int(item_dict[CLICK_KEY_PREFIX + item]),
int(item_dict[IMPRESSION_KEY_PREFIX + item]))
for item in items]
# Select item with largest sample value.
winner = items[numpy.argmax(samples)]
self.write(winner)
49. Server side - short term
•Allow multiple sources / sink channels
•With different input → schema mappings
•Server side events
•Support for server side event logging (JSON
endpoint)
•Enabler for mobile SDKs
•Trivial to add pixel based end-point (server
managed cookies)
50. Client side
•Specific browser related bug fixes (IE9)
•Allow for setting session scoped parameters
•JavaScript Data Layer
51. Collector next steps
•Integrate with Planout (https://facebook.github.io/
planout/)
•Allow definition of online experiments in one
place
•All event logging automatically includes random
parameters generated for experiment selection
•Single solution for data collection for online
experimentation / optimization