Operationalizing Machine Learning at Scale at StarbucksDatabricks
As ML-driven innovations are propelled by the Self-Service capabilities in the Enterprise Data and Analytics Platform, teams face a significant entry barrier and productivity issues in moving from POCs to Operating ML-powered apps at scale in production.
PyCaret is an open-source, low-code machine learning library in Python that allows you to go from preparing your data to deploying your model within minutes in your choice of environment. This talk is a practical demo on how to use PyCaret in your existing workflows and supercharge your data science team’s productivity.
Azure Machine Learning Studio is a data science technique and advanced analytics solution that allows for collaborative and drag-and-drop creation of machine learning models in a fully managed cloud platform. It has components such as the Azure Machine Learning Workbench, Experimentation Service, and Model Management Service that allow users to import and visualize data, create workflows, perform statistics, and publish models as web services or BI visualizations.
Herding Cats: Migrating Dozens of Oddball Analytics Systems to Apache Spark w...Databricks
HP ships millions of PCs, printers and other devices every year to customers in all market segments. Many of these systems have had various generations of data collection and reporting, going
back as many as 16 years. That has led to a significant sprawl of custom data formats, specialized code and numerous brittle legacy systems collecting, analyzing and reporting data.
This session will focus on samples of HP’s journey to find, catalog and ultimately eliminate these systems by migrating to Apache Spark with Databricks in the cloud. Hear about HP’s challenges dealing with legacy systems (some even located under engineers desks) and how the power of AWS, Spark, and visualization tools has significantly simplified their migrations. You’ll also learn how the success of this endeavor is not just in counting the number of systems deprecated, but also how the process is evolving into building companywide shared Spark libraries, notebooks and web services that are accelerating future migrations and analysis using Spark.
This document describes an autonomous analytics platform that allows users to analyze streaming data. The platform uses a unified big data technology stack including Spark, Cassandra, Hadoop, Kafka and Elasticsearch. It has a cloud-agnostic architecture and supports multiple machine learning frameworks. The platform includes a Domain Specific Language (DSL) that allows power users to create full data pipelines and analytics workflows with a few lines of code. It also includes a DSL Workbench for interactively building, editing and publishing analytical pipelines. Additionally, the document introduces "Auto Curious", which harnesses user interactions to autonomously discover insights and compose DSL commands through a question graph interface.
This document provides an overview of Azure Machine Learning including an introduction to the service, differences between Azure ML and SSAS Data Mining, demos of building and consuming ML models, and a quick introduction to other relevant Azure tools like Azure Stream Analytics, Azure Data Factory, and Azure Intelligent Systems Service. The presenter has experience with SQL Server BI, .NET, and is a BI developer but not a data scientist.
Operationalizing Machine Learning at Scale at StarbucksDatabricks
As ML-driven innovations are propelled by the Self-Service capabilities in the Enterprise Data and Analytics Platform, teams face a significant entry barrier and productivity issues in moving from POCs to Operating ML-powered apps at scale in production.
PyCaret is an open-source, low-code machine learning library in Python that allows you to go from preparing your data to deploying your model within minutes in your choice of environment. This talk is a practical demo on how to use PyCaret in your existing workflows and supercharge your data science team’s productivity.
Azure Machine Learning Studio is a data science technique and advanced analytics solution that allows for collaborative and drag-and-drop creation of machine learning models in a fully managed cloud platform. It has components such as the Azure Machine Learning Workbench, Experimentation Service, and Model Management Service that allow users to import and visualize data, create workflows, perform statistics, and publish models as web services or BI visualizations.
Herding Cats: Migrating Dozens of Oddball Analytics Systems to Apache Spark w...Databricks
HP ships millions of PCs, printers and other devices every year to customers in all market segments. Many of these systems have had various generations of data collection and reporting, going
back as many as 16 years. That has led to a significant sprawl of custom data formats, specialized code and numerous brittle legacy systems collecting, analyzing and reporting data.
This session will focus on samples of HP’s journey to find, catalog and ultimately eliminate these systems by migrating to Apache Spark with Databricks in the cloud. Hear about HP’s challenges dealing with legacy systems (some even located under engineers desks) and how the power of AWS, Spark, and visualization tools has significantly simplified their migrations. You’ll also learn how the success of this endeavor is not just in counting the number of systems deprecated, but also how the process is evolving into building companywide shared Spark libraries, notebooks and web services that are accelerating future migrations and analysis using Spark.
This document describes an autonomous analytics platform that allows users to analyze streaming data. The platform uses a unified big data technology stack including Spark, Cassandra, Hadoop, Kafka and Elasticsearch. It has a cloud-agnostic architecture and supports multiple machine learning frameworks. The platform includes a Domain Specific Language (DSL) that allows power users to create full data pipelines and analytics workflows with a few lines of code. It also includes a DSL Workbench for interactively building, editing and publishing analytical pipelines. Additionally, the document introduces "Auto Curious", which harnesses user interactions to autonomously discover insights and compose DSL commands through a question graph interface.
This document provides an overview of Azure Machine Learning including an introduction to the service, differences between Azure ML and SSAS Data Mining, demos of building and consuming ML models, and a quick introduction to other relevant Azure tools like Azure Stream Analytics, Azure Data Factory, and Azure Intelligent Systems Service. The presenter has experience with SQL Server BI, .NET, and is a BI developer but not a data scientist.
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.
Data cleansing and data prep with synapse data flowsMark Kromer
This document contains links to resources about using Azure Synapse Analytics for data cleansing and preparation with Data Flows. It includes links to videos and documentation about removing null values, saving data profiler summary statistics, and using metadata functions in Azure Data Factory data flows.
The document summarizes the typical evolution of data processing at a startup company and provides details about data engineering at Udemy. It describes how companies initially struggle with data before establishing scalable data infrastructure and workflows. At Udemy, they use AWS Redshift as their data warehouse, ingest data from various sources using Python ETL pipelines scheduled through Pinball, and use Hadoop/EMR for batch processing and AWS Kinesis for real-time processing. Lessons learned include starting with batch processing, considering the type of data, and storing data in a log format for debugging.
Model Experiments Tracking and Registration using MLflow on DatabricksDatabricks
Machine learning models are only as good as the quality of data and the size of datasets used to train the models. Data has shown that data scientists spend around 80% of their time on preparing and managing data for analysis and 57% of the data scientists regard cleaning and organizing data as the least enjoyable part of their work. This further validates the idea of MLOps and the need for collaboration between data scientists and data engineers.
Larry will discuss what data science means in general, and more specifically at Udemy. He will describe some key data science frameworks, and what it means for them to be agile. He will also discuss ideally what it would mean to be a data scientist at Udemy.
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/nZzHFwaoMpU
In this presentation, we will demonstrate the integration of H2O Driverless.ai with NetApp Cloud Volumes Service. In addition, we’ll describe key considerations for the development of Deep Learning environments and the solutions that enable seamless data management across edge environments, on-premises data centers, and the cloud. This presentation is targeted for data scientists, data engineers, and line of business leaders.
Vinod comes with over 10 years of Marketing & Data Science experience in multiple startups. He was the founding employee for his previous startup, Activehours, where he helped build the product and bootstrap the user acquisition with growth hacking. He has seen the user base for his companies grow from scratch to millions of customers. He’s built models to score leads, reduce churn, increase conversion, prevent fraud and many more use cases. He brings a strong analytical side and an metrics driven approach to marketing.
This document summarizes lessons learned from a large enterprise implementation of Esri ArcGIS. It discusses challenges faced including the unique security responsibilities of the federal government. Key lessons included discouraging hybrid ArcGIS designs across multiple networks, allowing hours to move across contract years, using Agile over Waterfall for expedited life cycles, and increasing visibility of fixes from vendors. Prototyping with infrastructure as a service and emphasizing minimum viable products is also advised.
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).
Automated machine learning (AutoML) can automate time-consuming tasks in the machine learning lifecycle like data preprocessing, model training, and tuning. This allows data scientists to focus on higher-level work. The presentation demonstrated AutoML on the Titanic dataset in Microsoft Azure Machine Learning service. It showed how AutoML can iterate through various algorithms and hyperparameters, measure model performance, enable model interpretability, facilitate model hosting and drift detection, and support code-based MLOps workflows. AutoML aims to make machine learning more accessible and productive.
DPGD Microsoft Hyderabad 22nd Sept 2018Pramod Singla
Introduction to machine learning on Azure data bricks environment gives you fair idea to kick start ML with the best tools and libraries available. It Covers:
Why Machine learning
Life Cycle
Why Azure Databricks
Available ML options in Azure Databricks
MLFlow
H20.ai
This session is continuation of “Automated Production Ready ML at Scale” in last Spark AI Summit at Europe. In this session you will learn about how H&M evolves reference architecture covering entire MLOps stack addressing a few common challenges in AI and Machine learning product, like development efficiency, end to end traceability, speed to production, etc.
These are slides presented at MLconf in San Francisco, November 14, 2014. I share the approach to real-time machine learning for recommender systems developed at if(we). We achieve rapid iterative cycles by adhering to a strict approach to structuring and accessing our data, as well as to building the online features that comprise our models. These developments support teams of data scientist and data engineers, who work together to solve complex recommendation problems. We also introduce the Antelope Realtime Events framework, an open source demonstration application which derives from our scalable proprietary software stack.
In this session, we will explore options in PowerBI to stream real-time data to the service.
Differences between pushing, streaming and PubNub streaming will be explained and we will dive deep into each of the three methods.
Join this session to learn how to get live data into your PowerBI service.
The session will be covering basic entry to best practices.
You did great job finishing this web app on time and budget. Design patterns, good code coverage, cutting edge frameworks and best CI ever. It goes to production and boom, clients complain it's too slow. They don't really care, if it's best engineering ever, if each view loads 4 seconds. My presentation will give you hints on how to look for bottlenecks. I will also share simple tricks to make the app work faster, or at least seem to work faster.
This document introduces Apache Superset, an open source data exploration and visualization tool. Superset allows users to easily slice, dice and visualize data without coding knowledge. It was originally developed by engineers at Airbnb and is now maintained under the Apache license. Some key features include supporting multiple data sources, interactivity without coding, and being free to use. While still developing, Superset provides an open alternative to paid business intelligence tools.
Spark Summit East Keynote by Anjul BhambhriJen Aman
Apache Spark is a framework for large-scale data processing. IBM fully supports Spark and is building it into many of its products and services. Spark can handle both batch and streaming analytics efficiently using techniques like the Lambda architecture. IBM discusses several use cases for Spark including weather data analytics, healthcare data lakes, and customer experience analysis in telecom.
Big Data Advanced Analytics on Microsoft Azure 201904Mark Tabladillo
This talk summarizes key points for big data advanced analytics on Microsoft Azure. First, there is a review of the major technologies. Second, there is a series of technology demos (focusing on VMs, Databricks and Azure ML Service). Third, there is some advice on using the Team Data Science Process to help plan projects. The deck has web resources recommended. This presentation was delivered at the Global Azure Bootcamp 2019, Atlanta GA location (Alpharetta Avalon).
Building a Real-Time Security Application Using Log Data and Machine Learning...Sri Ambati
Building a Real-Time Security Application Using Log Data and Machine Learning- Karthik Aaravabhoomi
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Praveen Nair is a program director at Adfolks LLC and formerly held roles at Orion Business Innovation and PIT Solutions. He is a Microsoft MVP and certified in various Microsoft, PMP, and CSPO programs. Azure Monitor is a monitoring solution that collects, analyzes, and acts on telemetry data from Azure and on-premises environments. It helps maximize application performance and availability and proactively identify problems. Azure Monitor provides a unified view of applications, infrastructure, and networks using collected metrics and logs analyzed with Kusto query language.
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.
Data cleansing and data prep with synapse data flowsMark Kromer
This document contains links to resources about using Azure Synapse Analytics for data cleansing and preparation with Data Flows. It includes links to videos and documentation about removing null values, saving data profiler summary statistics, and using metadata functions in Azure Data Factory data flows.
The document summarizes the typical evolution of data processing at a startup company and provides details about data engineering at Udemy. It describes how companies initially struggle with data before establishing scalable data infrastructure and workflows. At Udemy, they use AWS Redshift as their data warehouse, ingest data from various sources using Python ETL pipelines scheduled through Pinball, and use Hadoop/EMR for batch processing and AWS Kinesis for real-time processing. Lessons learned include starting with batch processing, considering the type of data, and storing data in a log format for debugging.
Model Experiments Tracking and Registration using MLflow on DatabricksDatabricks
Machine learning models are only as good as the quality of data and the size of datasets used to train the models. Data has shown that data scientists spend around 80% of their time on preparing and managing data for analysis and 57% of the data scientists regard cleaning and organizing data as the least enjoyable part of their work. This further validates the idea of MLOps and the need for collaboration between data scientists and data engineers.
Larry will discuss what data science means in general, and more specifically at Udemy. He will describe some key data science frameworks, and what it means for them to be agile. He will also discuss ideally what it would mean to be a data scientist at Udemy.
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/nZzHFwaoMpU
In this presentation, we will demonstrate the integration of H2O Driverless.ai with NetApp Cloud Volumes Service. In addition, we’ll describe key considerations for the development of Deep Learning environments and the solutions that enable seamless data management across edge environments, on-premises data centers, and the cloud. This presentation is targeted for data scientists, data engineers, and line of business leaders.
Vinod comes with over 10 years of Marketing & Data Science experience in multiple startups. He was the founding employee for his previous startup, Activehours, where he helped build the product and bootstrap the user acquisition with growth hacking. He has seen the user base for his companies grow from scratch to millions of customers. He’s built models to score leads, reduce churn, increase conversion, prevent fraud and many more use cases. He brings a strong analytical side and an metrics driven approach to marketing.
This document summarizes lessons learned from a large enterprise implementation of Esri ArcGIS. It discusses challenges faced including the unique security responsibilities of the federal government. Key lessons included discouraging hybrid ArcGIS designs across multiple networks, allowing hours to move across contract years, using Agile over Waterfall for expedited life cycles, and increasing visibility of fixes from vendors. Prototyping with infrastructure as a service and emphasizing minimum viable products is also advised.
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).
Automated machine learning (AutoML) can automate time-consuming tasks in the machine learning lifecycle like data preprocessing, model training, and tuning. This allows data scientists to focus on higher-level work. The presentation demonstrated AutoML on the Titanic dataset in Microsoft Azure Machine Learning service. It showed how AutoML can iterate through various algorithms and hyperparameters, measure model performance, enable model interpretability, facilitate model hosting and drift detection, and support code-based MLOps workflows. AutoML aims to make machine learning more accessible and productive.
DPGD Microsoft Hyderabad 22nd Sept 2018Pramod Singla
Introduction to machine learning on Azure data bricks environment gives you fair idea to kick start ML with the best tools and libraries available. It Covers:
Why Machine learning
Life Cycle
Why Azure Databricks
Available ML options in Azure Databricks
MLFlow
H20.ai
This session is continuation of “Automated Production Ready ML at Scale” in last Spark AI Summit at Europe. In this session you will learn about how H&M evolves reference architecture covering entire MLOps stack addressing a few common challenges in AI and Machine learning product, like development efficiency, end to end traceability, speed to production, etc.
These are slides presented at MLconf in San Francisco, November 14, 2014. I share the approach to real-time machine learning for recommender systems developed at if(we). We achieve rapid iterative cycles by adhering to a strict approach to structuring and accessing our data, as well as to building the online features that comprise our models. These developments support teams of data scientist and data engineers, who work together to solve complex recommendation problems. We also introduce the Antelope Realtime Events framework, an open source demonstration application which derives from our scalable proprietary software stack.
In this session, we will explore options in PowerBI to stream real-time data to the service.
Differences between pushing, streaming and PubNub streaming will be explained and we will dive deep into each of the three methods.
Join this session to learn how to get live data into your PowerBI service.
The session will be covering basic entry to best practices.
You did great job finishing this web app on time and budget. Design patterns, good code coverage, cutting edge frameworks and best CI ever. It goes to production and boom, clients complain it's too slow. They don't really care, if it's best engineering ever, if each view loads 4 seconds. My presentation will give you hints on how to look for bottlenecks. I will also share simple tricks to make the app work faster, or at least seem to work faster.
This document introduces Apache Superset, an open source data exploration and visualization tool. Superset allows users to easily slice, dice and visualize data without coding knowledge. It was originally developed by engineers at Airbnb and is now maintained under the Apache license. Some key features include supporting multiple data sources, interactivity without coding, and being free to use. While still developing, Superset provides an open alternative to paid business intelligence tools.
Spark Summit East Keynote by Anjul BhambhriJen Aman
Apache Spark is a framework for large-scale data processing. IBM fully supports Spark and is building it into many of its products and services. Spark can handle both batch and streaming analytics efficiently using techniques like the Lambda architecture. IBM discusses several use cases for Spark including weather data analytics, healthcare data lakes, and customer experience analysis in telecom.
Big Data Advanced Analytics on Microsoft Azure 201904Mark Tabladillo
This talk summarizes key points for big data advanced analytics on Microsoft Azure. First, there is a review of the major technologies. Second, there is a series of technology demos (focusing on VMs, Databricks and Azure ML Service). Third, there is some advice on using the Team Data Science Process to help plan projects. The deck has web resources recommended. This presentation was delivered at the Global Azure Bootcamp 2019, Atlanta GA location (Alpharetta Avalon).
Building a Real-Time Security Application Using Log Data and Machine Learning...Sri Ambati
Building a Real-Time Security Application Using Log Data and Machine Learning- Karthik Aaravabhoomi
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Praveen Nair is a program director at Adfolks LLC and formerly held roles at Orion Business Innovation and PIT Solutions. He is a Microsoft MVP and certified in various Microsoft, PMP, and CSPO programs. Azure Monitor is a monitoring solution that collects, analyzes, and acts on telemetry data from Azure and on-premises environments. It helps maximize application performance and availability and proactively identify problems. Azure Monitor provides a unified view of applications, infrastructure, and networks using collected metrics and logs analyzed with Kusto query language.
Serverless data and analytics on AWS for operations CloudHesive
The document discusses using serverless data and analytics on AWS for operations. It describes ingesting data from various sources into an AWS data lake for storage, processing, analysis and visualization. This allows operational data to be combined and analyzed to improve response by leveraging serverless AWS services like S3, Glue, Athena and QuickSight in a cost-effective way. A demo shows how different data sources can be ingested and analyzed using a data lake approach.
Sarine's Big Data Journey by Rostislav AaronovIdan Tohami
This document discusses how Sarine, a company that provides technology for the diamond industry, uses Elasticsearch. It notes that Sarine uses Elasticsearch to store over 400 million documents totaling 1 terabyte of data across 125 indices. Sarine uses Elasticsearch for logging application requests, monitoring system activity, collecting statistics, and visualizing and reporting on data. The document recommends how to best implement and use Elasticsearch, such as using at least three nodes, carefully designing index mappings, educating teams, and using partners for consulting.
This document discusses how Sarine, a company that provides technology for the diamond industry, uses Elasticsearch. It notes that Sarine uses Elasticsearch to store over 400 million documents totaling 1 terabyte of data across 125 indices. Sarine uses Elasticsearch for logging application requests, monitoring system activity, collecting statistics, and visualizing and reporting on data. The document recommends how to best implement and use Elasticsearch, such as using at least three nodes, carefully designing mappings, and getting partner support.
최근 데이터의 폭증과 이를 기반한 빅데이터 분석이 기업 비지니스 성패에 큰 영향을 끼치고 있습니다. 다양한 기업의 데이터 기반 의사 결정을 위한 요구를 수용하는 분석 플랫폼과 인공 지능 기술의 도입은 큰 화두입니다. 본 세션에서는 기업의 비지니스 전략 및 기획을 담당하시는 분들을 위해 클라우드 기반 데이터 분석 플랫폼을 쉽게 접근하고 사용할 수 있는 방법을 사례 위주로 소개합니다.국내외 주요 기업들이 어떻게 AWS기반 데이터 분석 및 기계 학습 서비스로 비지니스 혁신에 활용하고 있는지 알아보시기 바랍니다.
다시보기 링크: https://youtu.be/24YgdrJ9r-A
10 Key Considerations for AI/ML Model GovernanceQuantUniversity
This document is a summary of a presentation by Sri Krishnamurthy on key considerations for AI/ML model governance. The presentation covered 10 best practices for an effective model risk management program, including adopting a framework-driven approach, customizing the program to the organization, defining roles and responsibilities, integrating model risk management into the model lifecycle, and monitoring model health. It also provided a case study on sentiment analysis of earnings calls using various APIs and building an internal model. The presentation emphasized challenges in moving models from development to production and the need for fairness, explainability and tracking of models.
The document discusses the role and responsibilities of a data architect. It provides information on the high demand and salaries for data architects, which can be over $200,000 at companies like Microsoft. The summary also outlines some of the key technical skills required for the role, including strong data modeling abilities, knowledge of databases, ETL tools, analytics dashboards, and programming languages like SQL, Python and R. Business skills like communication and presenting complex concepts are also important.
Data Lake allows an organisation to store all of their data, structured and unstructured, in one, centralised repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand. In this session we will explore the architecture of a Data Lake on AWS and cover topics such as storage, processing and security.
Alex mang patterns for scalability in microsoft azure applicationCodecamp Romania
The document discusses patterns for scalability in Microsoft Azure applications. It covers queue-based load leveling, competing consumers, and priority queue patterns for handling application load and message processing. It also discusses materialized view and sharding patterns for scaling databases, where materialized views optimize queries and sharding partitions data horizontally across multiple servers. The talk includes demos of priority queue and sharding patterns to illustrate their implementations.
Your Roadmap for An Enterprise Graph StrategyNeo4j
Speaker: Michael Moore, Ph.D., Executive Director, Knowledge Graphs + AI, EY National Advisory
Abstract: Knowledge graphs have enormous potential for delivering superior customer experiences, advanced analytics and efficient data management.
Learn valuable tips from a leading practitioner on how to position, organize and implement your first enterprise graph project.
Slides from my talk at Big Data Conference 2018 in Vilnius
Doing data science today is far more difficult than it will be in the next 5-10 years. Sharing, collaborating on data science workflows in painful, pushing models into production is challenging.
Let’s explore what Azure provides to ease Data Scientists’ pains. What tools and services can we choose based on a problem definition, skillset or infrastructure requirements?
In this talk, you will learn about Azure Machine Learning Studio, Azure Databricks, Data Science Virtual Machines and Cognitive Services, with all the perks and limitations.
Building a data warehouse with Amazon Redshift … and a quick look at Amazon ...Julien SIMON
This document provides a summary of a presentation about building data warehouses with Amazon Redshift and using Amazon Machine Learning. The presentation discusses how Amazon Redshift can be used to build a petabyte-scale data warehouse with SQL and no system administration. Case studies are presented showing companies saving on total cost of ownership by migrating to Amazon Redshift. It also briefly introduces Amazon Machine Learning for building predictive models with managed services. Demo examples are shown of loading data into Redshift and using ML to train a regression model and create a real-time prediction API.
This document provides an introduction to a course on big data and analytics. It outlines the following key points:
- The instructor and TA contact information and course homepage.
- The course will cover foundational data analytics, Hadoop/MapReduce programming, graph databases, and other big data topics.
- Big data is defined as data that is too large or complex for traditional database tools to process. It is characterized by high volume, velocity, and variety.
- Examples of big data sources and the exponential growth of data volumes are provided. Real-time analytics and fast data processing are also discussed.
This document provides an introduction to a course on big data. It outlines the instructor and TA contact information. The topics that will be covered include data analytics, Hadoop/MapReduce programming, graph databases and analytics. Big data is defined as data sets that are too large and complex for traditional database tools to handle. The challenges of big data include capturing, storing, analyzing and visualizing large, complex data from many sources. Key aspects of big data are the volume, variety and velocity of data. Cloud computing, virtualization, and service-oriented architectures are important enabling technologies for big data. The course will use Hadoop and related tools for distributed data processing and analytics. Assessment will include homework, a group project, and class
How to Build TOGAF Architectures With System Architect (2).pptStevenShing
This document provides an agenda and overview for a TOGAF workshop on building enterprise architectures with System Architect. The agenda covers introducing TOGAF preliminary stages, business architecture, the business service layer, information systems architecture, application portfolio management, and analysis. It discusses modeling functions, processes, services, and applications. It also describes leveraging reference models, integrating with tools like Visio and Blueworks Live, and using the FEA Services Reference Model and TMForum models. The labs guide attending building out the different architecture components in System Architect.
This document contains a presentation on using graph databases for recommendations. It begins with an introduction to graphs and graph theory, then discusses what graph databases are and how they are different from relational databases. It explains how graphs are well-suited for complex querying and representing connected data. The presentation describes how recommendation systems work and how graph algorithms and storing recommendation data in a graph structure provide benefits like real-time recommendations, navigating relationships between items, and efficient operations. It concludes with a demonstration, examples, and discussing future events.
This document summarizes a presentation on performance optimization on a budget. It discusses measuring and improving performance at the front-end through asset optimization, latency reduction, and client-side rendering. It also discusses measuring and optimizing performance at the backend through caching, databases, and server-side architecture. The document lists several free and paid tools for profiling, testing, and analyzing performance. It concludes with best practices for performance including establishing goals, architecture, testing, and an SDLC approach.
Similar to Metrics for Web Applications - Netcamp 2012 (20)
The Evolving Landscape of Data EngineeringAndrei Savu
The document discusses the evolving landscape of data engineering. It provides context on the past, present, and future of data engineering. Specifically, it notes that in the past, data engineering was driven by open source communities and the early histories of AWS and Google Cloud. It describes common present-day patterns like serverless architectures and data locality. Finally, it outlines a future wish list, including data catalogs, monitoring systems, and more intelligent data infrastructure. The document concludes by offering recommendations on where to start with technologies, Google Cloud courses, and developing domain knowledge.
The Evolving Landscape of Data EngineeringAndrei Savu
Data Engineering is a relatively new, but fast evolving discipline that spans multiple environments and technologies, from traditional data centers to hyper-scale cloud providers, a discipline that combines closed-source, homegrown and open source software to create scalable data pipelines and power incredible new product features.
In this presentation, we will go over the last 5-10 years of technology trends and advancements and bring all of that together in a story about modern day Data Engineering and the magic behind it.
Recap on AWS Lambda after re:Invent 2015Andrei Savu
A quick presentation on what AWS Lambda is about and what was announced at AWS re:Invent 2015 Las Vegas. In see Lambda as a easy to define event handles that glue different AWS services together at a surprising scale.
One Hadoop, Multiple Clouds - NYC Big Data MeetupAndrei Savu
The slide deck I presented at NYC Big Data Meetup just before Strata + Hadoop World 2015. It goes into details on what's different about running Hadoop in the cloud, main use case and some lessons learned from working with customers.
Introducing Cloudera Director at Big Data BashAndrei Savu
My slide deck for Big Data Bash. This is a quick introduction on Cloudera Director and it ends with a list of open questions around some interesting future problems we are planning to work on.
APIs & Underlying Protocols #APICraftSFAndrei Savu
My slides from a talk about APIs and their relationship to various network protocols, older and new ones and how that defines some of the characteristics that describe high quality implementation.
Challenges for running Hadoop on AWS - AdvancedAWS MeetupAndrei Savu
Nowadays we've got all the tools we need to spin-up and tear-down clusters with hundreds of nodes in minutes and this puts more pressure on the tools we use to configure and monitor our applications. This challenge is even more interesting when we have to deal with long running distributed data storage and processing systems like Hadoop. In this talk we will look into some of the challenges we need to deal with when creating and managing Hadoop clusters in AWS, we will discuss improvement opportunities in monitoring (e.g. detecting and dealing with instance failure, resource contention & noisy neighbors) and a bit about the future and how we should go about disconnecting workload dispatch from cluster lifecycle.
My slides on how to use cloud as a data platform at BigDataWeek 2013 Romania
http://www.eurocloud.ro/en/events/all-there-is-to-know-about-big-data/#.UXZFaUDvlVI
Apache Provisionr (incubating) - Bucharest JUG 10Andrei Savu
My slides on Apache Provisionr (incubating) - a service that can be used to create and manage pools of virtual machines on multiple clouds.
http://provisionr.incubator.apache.org/
Creating pools of Virtual Machines - ApacheCon NA 2013Andrei Savu
My slides on creating pools of virtual machines for ApacheCon NA 2013 in Portland.
Provisionr Source code:
https://github.com/axemblr/axemblr-provisionr
Apache Incubator proposal:
https://github.com/axemblr/axemblr-provisionr/wiki/Provisionr-Proposal
This document provides an overview of the data science process and tools for a data science project. It discusses identifying important business questions to answer with data, extracting relevant data from sources, cleaning and sampling the data, analyzing samples to create models and check hypotheses, applying results to full data sets, visualizing findings, automating and deploying solutions, and continuously learning and improving through an iterative process. Key tools mentioned include Hadoop, R, Python, Excel, and various data wrangling, analysis, and visualization tools.
Simple Service for Managing Pools of 10s or 100s of Virtual Machines
With Provisionr we want to solve the problem of cloud portability by hiding completely the API and only focusing on building a cluster that matches the same set of assumptions on all clouds, assumptions like: running a specific operating system (e.g. Ubuntu LTS), having the same set of pre-installed packages and binaries, sane dns settings (forward & reverse ip resolution - as needed for Hadoop), ntp settings, networking settings, ssh admin access, vpn access etc.
The document summarizes the Bucharest JUG (Java User Group) meetings from May 2012 to November 2012. It provides details on the monthly meetings including average attendance of 25-80 people, topics covered such as Guava, Maven, JavaScript UI for REST, and thanks to speakers and sponsors. It outlines future plans such as live streaming international speakers, a donations/job board, and potential future topics around alternative build systems, deployment options, and monitoring tools. Contact details are provided.
Counters with Riak on Amazon EC2 at HackoverAndrei Savu
The document discusses using distributed counters with Riak on Amazon EC2. It introduces Riak as a distributed key-value database focused on availability, fault tolerance, simplicity and scalability. It describes using Riak features like consistent hashing, replication and automatic load balancing to implement a REST API for counters with eventual consistency. The demo shows implementing and accessing counters across local machines and EC2 regions to demonstrate the architecture.
This document provides an overview of using Dropwizard, an open-source Java framework, to build RESTful web services. It discusses REST concepts like resources and representations, REST verbs like GET and POST, and architectures for REST APIs. It then introduces Dropwizard and its components for building HTTP services with features like Jetty, Jersey, Jackson, and metrics support. The document demonstrates a sample Dropwizard TODO list application with REST endpoints and resources and discusses considerations for development, testing, and deployment.
Guava Overview Part 2 Bucharest JUG #2 Andrei Savu
This document provides an overview of Guava and discusses caches and services. Guava is Google's core Java library that contains utilities like caches, primitives, collections, and concurrency libraries. Caches can improve performance by storing values to avoid expensive re-computation. Services in Guava define lifecycles for objects with operational state and allow asynchronous starting and stopping. The document describes cache eviction strategies, service implementations, and where to find more information on Guava features like functional idioms and concurrency.
Guava Overview. Part 1 @ Bucharest JUG #1 Andrei Savu
Guava is a Java library developed by Google that includes common libraries such as collections, caching, primitives support, concurrency libraries, and generalized utility classes. The talk covered the basic utilities in Guava including using and avoiding null, preconditions, common object utilities, ordering, and primitive array utilities. It also discussed the collections in Guava including immutable collections, new collection types like multisets and multimaps, collection utilities, and ways to extend the collections framework with decorators.
Polyglot Persistence & Big Data in the CloudAndrei Savu
This document discusses polyglot persistence and deploying big data technologies in the cloud. It introduces databases like HBase, Cassandra, MongoDB, CouchDB, and Riak for storing large amounts of data. Technologies like Elasticsearch and Solr are presented for search. Hadoop is described as a framework for distributed processing of large datasets. The document concludes by discussing how the Apache Whirr project can be used to deploy these technologies on cloud infrastructure.
Apache Whirr is a set of libraries for running cloud services on-demand in a cloud-neutral way. It provides common APIs and defaults for deploying clusters running Hadoop, Cassandra, HBase, and Zookeeper on EC2 and Rackspace. Whirr configurations allow deploying typical clusters with a single command. It is being developed further to support private clouds and new services, and to integrate with Hudson for testing fault injection scenarios on small test clusters.
An introduction to the cryptocurrency investment platform Binance Savings.Any kyc Account
Learn how to use Binance Savings to expand your bitcoin holdings. Discover how to maximize your earnings on one of the most reliable cryptocurrency exchange platforms, as well as how to earn interest on your cryptocurrency holdings and the various savings choices available.
Best practices for project execution and deliveryCLIVE MINCHIN
A select set of project management best practices to keep your project on-track, on-cost and aligned to scope. Many firms have don't have the necessary skills, diligence, methods and oversight of their projects; this leads to slippage, higher costs and longer timeframes. Often firms have a history of projects that simply failed to move the needle. These best practices will help your firm avoid these pitfalls but they require fortitude to apply.
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FIA officials brutally tortured innocent and snatched 200 Bitcoins of worth 4...jamalseoexpert1978
Farman Ayaz Khattak and Ehtesham Matloob are government officials in CTW Counter terrorism wing Islamabad, in Federal Investigation Agency FIA Headquarters. CTW and FIA kidnapped crypto currency owner from Islamabad and snatched 200 Bitcoins those worth of 4 billion rupees in Pakistan currency. There is not Cryptocurrency Regulations in Pakistan & CTW is official dacoit and stealing digital assets from the innocent crypto holders and making fake cases of terrorism to keep them silent.
Recruiting in the Digital Age: A Social Media MasterclassLuanWise
In this masterclass, presented at the Global HR Summit on 5th June 2024, Luan Wise explored the essential features of social media platforms that support talent acquisition, including LinkedIn, Facebook, Instagram, X (formerly Twitter) and TikTok.
At Techbox Square, in Singapore, we're not just creative web designers and developers, we're the driving force behind your brand identity. Contact us today.
IMPACT Silver is a pure silver zinc producer with over $260 million in revenue since 2008 and a large 100% owned 210km Mexico land package - 2024 catalysts includes new 14% grade zinc Plomosas mine and 20,000m of fully funded exploration drilling.
Part 2 Deep Dive: Navigating the 2024 Slowdownjeffkluth1
Introduction
The global retail industry has weathered numerous storms, with the financial crisis of 2008 serving as a poignant reminder of the sector's resilience and adaptability. However, as we navigate the complex landscape of 2024, retailers face a unique set of challenges that demand innovative strategies and a fundamental shift in mindset. This white paper contrasts the impact of the 2008 recession on the retail sector with the current headwinds retailers are grappling with, while offering a comprehensive roadmap for success in this new paradigm.
Building Your Employer Brand with Social MediaLuanWise
Presented at The Global HR Summit, 6th June 2024
In this keynote, Luan Wise will provide invaluable insights to elevate your employer brand on social media platforms including LinkedIn, Facebook, Instagram, X (formerly Twitter) and TikTok. You'll learn how compelling content can authentically showcase your company culture, values, and employee experiences to support your talent acquisition and retention objectives. Additionally, you'll understand the power of employee advocacy to amplify reach and engagement – helping to position your organization as an employer of choice in today's competitive talent landscape.
How to Implement a Real Estate CRM SoftwareSalesTown
To implement a CRM for real estate, set clear goals, choose a CRM with key real estate features, and customize it to your needs. Migrate your data, train your team, and use automation to save time. Monitor performance, ensure data security, and use the CRM to enhance marketing. Regularly check its effectiveness to improve your business.
Implicitly or explicitly all competing businesses employ a strategy to select a mix
of marketing resources. Formulating such competitive strategies fundamentally
involves recognizing relationships between elements of the marketing mix (e.g.,
price and product quality), as well as assessing competitive and market conditions
(i.e., industry structure in the language of economics).
The 10 Most Influential Leaders Guiding Corporate Evolution, 2024.pdfthesiliconleaders
In the recent edition, The 10 Most Influential Leaders Guiding Corporate Evolution, 2024, The Silicon Leaders magazine gladly features Dejan Štancer, President of the Global Chamber of Business Leaders (GCBL), along with other leaders.
5. Network Bandwidth
CPU
Disk Usage
Infrastructure Metrics
RAID System Load
IOPS
Sessions Memory Usage
6. Infrastructure Metrics
• If it moves it should have a Graph
• Valuable for historical reasons
• Think “flight deck”
• You can know in advance when
bad things happen (e.g. Black Friday)