Jeeves Grows Up: An AI Chatbot for Performance and Quality
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Sarah: CEO-Finance-Report pipeline seems to be slow today. Why Jeeves: SparkSQL query dbt_fin_model in CEO-Finance-Report is running 53% slower on 2/28/2021. Data skew issue detected. Issue has not been seen in last 90 days. Jeeves: Adding 5 more nodes to cluster recommended for CEO-Finance-Report to finish in its 99th percentile time of 5.2 hours. Who is Jeeves? An experienced Spark developer? A seasoned administrator? No, Jeeves is a chatbot created to simplify data operations management for enterprise Spark clusters. This chatbot is powered by advanced AI algorithms and an intuitive conversational interface that together provide answers to get users in and out of problems quickly. Instead of being stuck to screens displaying logs and metrics, users can now have a more refreshing experience via a two-way conversation with their own personal Spark expert. We presented Jeeves at Spark Summit 2019. In the two years since, Jeeves has grown up a lot. Jeeves can now learn continuously as telemetry information streams in from more and more applications, especially SQL queries. Jeeves now “knows” about data pipelines that have many components. Jeeves can also answer questions about data quality in addition to performance, cost, failures, and SLAs. For example: Tom: I am not seeing any data for today in my Campaign Metrics Dashboard. Jeeves: 3/5 validations failed on the cmp_kpis table on 2/28/2021. Run of pipeline cmp_incremental_daily failed on 2/28/2021. This talk will give an overview of the newer capabilities of the chatbot, and how it now fits in a modern data stack with the emergence of new data roles like analytics engineers and machine learning engineers. You will learn how to build chatbots that tackle your complex data operations challenges.
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Improving Power Grid Reliability Using IoT Analytics
Society depends on reliable utility services to ensure the health and safety of our communities. Electrical grid failures have impact and consequences that can range from daily inconveniences to catastrophic events. Ensuring grid reliability means that data is fully leveraged to understand and forecast demand, predict and mitigate unplanned interruptions to power supply, and efficiently restore power when needed. Neudesic, a Systems Integrator, and DTE Energy, a large electric and natural gas utility serving 2.2 million customers in southeast Michigan, partnered to use large IoT datasets to identify the sources and causes of reliability issues across DTE’s power distribution network. In this session, we will demonstrate how we ingest hundreds of millions of quality measures each day from DTE’s network of smart electric meters. This data is then further processed in Databricks to detect anomalies, apply graph analytics and spatially cluster these anomalies into “hot spots”. Engineers and Work Management Experts use a dashboard to explore, plan and prioritize diverse actions to remediate the hot spots. This allows DTE to prioritize work orders and dispatch crews based on impact to grid reliability. Because of this and other efforts, DTE has improved reliability by 25% year over year. We will demonstrate our notebooks and machine learning models along with our dashboard. We will also discuss Spark streaming, Pandas UDF’s, anomaly detection and DBSCAN clustering. By the end of our presentation, you should understand our approach to infer hidden insights from our IoT data, and potentially apply similar techniques to your own data.
What’s New with Databricks Machine Learning
In this session, the Databricks product team provides a deeper dive into the machine learning announcements. Join us for a detailed demo that gives you insights into the latest innovations that simplify the ML lifecycle — from preparing data, discovering features, and training and managing models in production.
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...
Getting machine learning models to production is notoriously difficult: it involves multiple teams (data scientists, data and machine learning engineers, operations, …), who often does not speak to each other very well; the model can be trained in one environment but then productionalized in completely different environment; it is not just about the code, but also about the data (features) and the model itself… At DataSentics, as a machine learning and cloud engineering studio, we see this struggle firsthand – on our internal projects and client’s projects as well.
Learn to Use Databricks for Data Science
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
Feature drift monitoring as a service for machine learning models at scale
In this talk, you’ll learn about techniques used to build a feature drift detection as a service capability for your enterprise and beyond. Feature drift monitoring is a way to check volatility of machine learning model inputs. It can trigger investigations for potential model degradation as well as explain why models have shifted.
Learn to Use Databricks for the Full ML Lifecycle
Machine learning development brings many new complexities beyond the traditional software development lifecycle. Unlike traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. In this talk, learn how to operationalize ML across the full lifecycle with Databricks Machine Learning.
Saving Energy in Homes with a Unified Approach to Data and AI
Energy wastage by residential buildings is a significant contributor to total worldwide energy consumption. Quby, an Amsterdam based technology company, offers solutions to empower homeowners to stay in control of their electricity, gas and water usage.
DevOps for DataScience
A talk for SF big analytics meetup. Building, testing, deploying, monitoring and maintaining big data analytics services. http://hydrosphere.io/
FlorenceAI: Reinventing Data Science at Humana
Humana strives to help the communities we serve and our individual members achieve their best health – no small task in the past year! We had the opportunity to rethink our existing operations and reimagine what a collaborative ML platform for hundreds of data scientists might look like. The primary goal of our ML Platform, named FlorenceAI, is to automate and accelerate the delivery lifecycle of data science solutions at scale. In this presentation, we will walk through an end-to-end example of how to build a model at scale on FlorenceAI and deploy it to production. Tools highlighted include Azure Databricks, MLFlow, AppInsights, and Azure Data Factory.
We will employ slides, notebooks and code snippets covering problem framing and design, initial feature selection, model design and experimentation, and a framework of centralized production code to streamline implementation. Hundreds of data scientists now use our feature store that has tens of thousands of features refreshed in daily and monthly cadences across several years of historical data. We already have dozens of models in production and also daily provide fresh insights for our Enterprise Clinical Operating Model. Each day, billions of rows of data are generated to give us timely information.
We already have examples of teams operating orders of magnitude faster and at a scale not within reach using fixed on-premise resources. Given rapid adoption from a dozen pilot users to over 100 MAU in the first 5 months, we will also share some anecodotes about key early wins created by the platform. We want FlorenceAI to enable Humana’s data scientists to focus their efforts where they add the most value so we can continue to deliver high-quality solutions that remain fresh, relevant and fair in an ever changing world.
FrugalML: Using ML APIs More Accurately and Cheaply
Offering prediction APIs for fee is a fast growing industry and is an important aspect of machine learning as a service. While many such services are available, the heterogeneity in their price and performance makes it challenging for users to decide which API or combination of APIs to use for their own data and budget. We take a first step towards addressing this challenge by proposing FrugalML, a principled framework that jointly learns the strength and weakness of each API on different data, and performs an efficient optimization to automatically identify the best sequential strategy to adaptively use the available APIs within a budget constraint. Our theoretical analysis shows that natural sparsity in the formulation can be leveraged to make FrugalML efficient. We conduct systematic experiments using ML APIs from Google, Microsoft, Amazon, IBM, Baidu and other providers for tasks including facial emotion recognition, sentiment analysis and speech recognition. Across various tasks, FrugalML can achieve up to 90% cost reduction while matching the accuracy of the best single API, or up to 5% better accuracy while matching the best API’s cost.
Scaling AutoML-Driven Anomaly Detection With Luminaire
Organizations rely heavily on time series metrics to measure and model key aspects of operational and business performance. The ability to reliably detect issues with these metrics is imperative to identifying early indicators of major problems before they become pervasive. This is a difficult machine learning and systems problem because temporal patterns are complex, ever changing, and often very noisy, traditionally requiring significant manual configuration and model maintenance.
At Zillow, we have built an orchestration framework around Luminaire, our open-source python library for hands-off time-series Anomaly Detection. Luminaire provides a suite of models and built-in AutoML capabilities which we process with Spark for distributed training and scoring of thousands of metrics. In this talk, we will cover the architecture of this framework and performance of the Luminaire package across detection and prediction accuracy as well as runtime efficiency.
Building Robust Production Data Pipelines with Databricks Delta
"Most data practitioners grapple with data quality issues and data pipeline complexities—it's the bane of their existence. Data engineers, in particular, strive to design and deploy robust data pipelines that serve reliable data in a performant manner so that their organizations can make the most of their valuable corporate data assets.
Databricks Delta, part of Databricks Runtime, is a next-generation unified analytics engine built on top of Apache Spark. Built on open standards, Delta employs co-designed compute and storage and is compatible with Spark API’s. It powers high data reliability and query performance to support big data use cases, from batch and streaming ingests, fast interactive queries to machine learning. In this tutorial we will discuss the requirements of modern data pipelines, the challenges data engineers face when it comes to data reliability and performance and how Delta can help. Through presentation, code examples and notebooks, we will explain pipeline challenges and the use of Delta to address them. You will walk away with an understanding of how you can apply this innovation to your data architecture and the benefits you can gain.
This tutorial will be both instructor-led and hands-on interactive session. Instructions in how to get tutorial materials will be covered in class. WHAT
YOU’LL LEARN:
– Understand the key data reliability and performance data pipelines challenges
– How Databricks Delta helps build robust pipelines at scale
– Understand how Delta fits within an Apache Spark™ environment – How to use Delta to realize data reliability improvements
– How to deliver performance gains using Delta
PREREQUISITES:
– A fully-charged laptop (8-16GB memory) with Chrome or Firefox
– Pre-register for Databricks Community Edition"
Speakers: Steven Yu, Burak Yavuz
Re-imagine Data Monitoring with whylogs and Spark
In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data.
In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.
Building Real-Time Data Pipeline for Diabetes Medication Recommender System U...
American Diabetic association states that 29.1 million Americans and 300+ million people all over the world have diabetes. Diabetic medication management is always challenging. Based on doctor’s prescription, patients take insulin dosage one hour before breakfast, lunch or dinner. But the real world scenario insulin intake can be changed based on the blood glucose level, calorie intake on a specific day, etc.
This talk explains how a real-time Big Data pipeline recommendation engine can be used to suggest insulin intake for diabetic patients in near real time. Based on calorie intake and blood glucose level from patients as well as generated dataset, insulin dosage can be recommended which will help patients to avoid over/under dosage. Designing medication recommender system is a need for the Healthcare industry. There is a growing trend for the applications to help doctors by recommending medication based on patient’s historic data. This also helps facilitate a doctor friendly and hospital free atmosphere for all users all over the world.
This talk would delve into a Diabetes medication recommender system using Databricks and Spark. Databricks supports HIPAA compliant deployment for processing PHI data. This talk would cover building a secure pipeline with encrypted data and the end-to-end recommendation system using Structured streaming and IoT data flowing from sensor.
Why do the majority of Data Science projects never make it to production?
María de la Fuente (Solutions Architect Manager for IMEA) @ Databricks
While most companies understand the value creation of leveraging data and are taking on board an AI strategy, only 13% of the data science projects make it to production successfully.
Besides the well-known skills gap in the market, we need to level up our end-to-end approach and cover all aspects involved when working with AI.
In this session, we will discuss the main obstacles to overcome and how we can avoid the major pitfalls to ensure our data science journey becomes successful.
Machine Learning at Scale with MLflow and Apache Spark
Societe Generale is one of the major banks in France and has many data science teams across the globe. After years of explorations and prototyping, it is time for the company to really deploy machine learning projects at scale to the production environment.
To achieve that goal, we have been working hard to define a standard process of collaboration between data engineers and data scientists. And we also designed and deployed an infrastructure for productionizing machine learning.
During this presentation, you will be looking at the following points of our adventure:
1. Difficulties that we had for putting ML applications into production, such as lack of model registry; hard to deploy ML libraries to our Hadoop cluster; collaboration between data scientists and data engineers etc. ?
2. How did we deploy MLflow as a key technical component to our production hadoop environment given different security constraints.
3. How did we build a CI/CD pipeline to deploy ML applications automatically. MLflow plays an important role in this piepline.
4. A first and concrete production project developed on top of this infrastructure with MLflow, Spark streaming, Sklearn and CI/CD.
The key takeaways of this presentation would be:
1. To productionize machine learning in a big structure like Société Générale, a process of collaboration should be clearly defined.
2. A ML model registry is key to ML productionization. MLflow is the best solution we found.
3. A CI/CD pipeline is essential to the success of a machine learning application.
The Future of Data Science and Machine Learning at Scale: A Look at MLflow, D...
Many had dubbed 2020 as the decade of data. This is indeed an era of data zeitgeist.
From code-centric software development 1.0, we are entering software development 2.0, a data-centric and data-driven approach, where data plays a central theme in our everyday lives.
As the volume and variety of data garnered from myriad data sources continue to grow at an astronomical scale and as cloud computing offers cheap computing and data storage resources at scale, the data platforms have to match in their abilities to process, analyze, and visualize at scale and speed and with ease — this involves data paradigm shifts in processing and storing and in providing programming frameworks to developers to access and work with these data platforms.
In this talk, we will survey some emerging technologies that address the challenges of data at scale, how these tools help data scientists and machine learning developers with their data tasks, why they scale, and how they facilitate the future data scientists to start quickly.
In particular, we will examine in detail two open-source tools MLflow (for machine learning life cycle development) and Delta Lake (for reliable storage for structured and unstructured data).
Other emerging tools such as Koalas help data scientists to do exploratory data analysis at scale in a language and framework they are familiar with as well as emerging data + AI trends in 2021.
You will understand the challenges of machine learning model development at scale, why you need reliable and scalable storage, and what other open source tools are at your disposal to do data science and machine learning at scale.
Phar Data Platform: From the Lakehouse Paradigm to the Reality
Despite the increased availability of ready-to-use generic tools, more and more enterprises are deciding to build in-house data platforms. This practice, common for some time in research labs and digital native companies, is now making its waves across large enterprises that traditionally used proprietary solutions and outsourced most of their IT. The availability of large volumes of data, coupled with more and more complex analytical use cases driven by innovations in data science have yielded these traditional and on premise architectures to become obsolete in favor of cloud architectures powered by open source technologies.
The idea of building an in-house platform at a larger enterprise comes with many challenges of its own: Build an Architecture that combines the best elements of data lakes and data warehouses to accommodate all kinds from BI to ML use cases. The need to interoperate with all the company’s data and technology, including legacy systems. Cultural transformation, including a commitment to adopt agile processes and data driven approaches.
This presentation describes a success story on building a Lakehouse in an enterprise such as LIDL, a successful chain of grocery stores operating in 32 countries worldwide. We will dive into the cloud-based architecture for batch and streaming workloads based on many different source systems of the enterprise and how we applied security on architecture and data. We will detail the creation of a curated Data Lake comprising several layers from a raw ingesting layer up to a layer that presents cleansed and enriched data to the business units as a kind of Data Marketplace.
A lot of focus and effort went into building a semantic Data Lake as a sustainable and easy to use basis for the Lakehouse as opposed to just dumping source data into it. The first use case being applied to the Lakehouse is the Lidl Plus Loyalty Program. It is already deployed to production in 26 countries with more than 30 millions of customers’ data being analyzed on a daily basis. In parallel to productionizing the Lakehouse, a cultural and organizational change process was undertaken to get all involved units to buy into the new data driven approach.
Big Data Day LA 2016/ Use Case Driven track - Shaping the Role of Data Scienc...
At IRIS.TV, our business builds algorithmic solutions for video recommendation with the end goal to deliver a great user experience as evidenced by users viewing more video content. This talk outlines our reasons for expanding from a descriptive/predictive approach to data analytics toward a philosophy that features more prescriptive analytics, driven by our data science team.
What's hot (20)
Improving Power Grid Reliability Using IoT Analytics
Improving Power Grid Reliability Using IoT Analytics
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...
Feature drift monitoring as a service for machine learning models at scale
Feature drift monitoring as a service for machine learning models at scale
Saving Energy in Homes with a Unified Approach to Data and AI
Saving Energy in Homes with a Unified Approach to Data and AI
FrugalML: Using ML APIs More Accurately and Cheaply
FrugalML: Using ML APIs More Accurately and Cheaply
Scaling AutoML-Driven Anomaly Detection With Luminaire
Scaling AutoML-Driven Anomaly Detection With Luminaire
Building Robust Production Data Pipelines with Databricks Delta
Building Robust Production Data Pipelines with Databricks Delta
Building Real-Time Data Pipeline for Diabetes Medication Recommender System U...
Building Real-Time Data Pipeline for Diabetes Medication Recommender System U...
Why do the majority of Data Science projects never make it to production?
Why do the majority of Data Science projects never make it to production?
Machine Learning at Scale with MLflow and Apache Spark
Machine Learning at Scale with MLflow and Apache Spark
The Future of Data Science and Machine Learning at Scale: A Look at MLflow, D...
The Future of Data Science and Machine Learning at Scale: A Look at MLflow, D...
Phar Data Platform: From the Lakehouse Paradigm to the Reality
Phar Data Platform: From the Lakehouse Paradigm to the Reality
Big Data Day LA 2016/ Use Case Driven track - Shaping the Role of Data Scienc...
Big Data Day LA 2016/ Use Case Driven track - Shaping the Role of Data Scienc...
Similar to Jeeves Grows Up: An AI Chatbot for Performance and Quality
Alten calsoft labs analytics service offerings
Capturing Data, Pre-processing, Analysis, Post Processing
Business Intelligence solutions using Excel 2013 and Power BI
Presented for the "Colegio de Ingenieros y Agrimensores de Puerto Rico (CIAPR) - Ponce" (Feb 8, 2014). Presented in Spanish.
Big Data Architecture
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
Time's Up! Getting Value from Big Data Now
The Briefing Room with Dr. Robin Bloor and CASK
We all know the promise of big data, but who gets the value? There are plenty of success stories already, and most of them involve one key ingredient: facilitated access to important data sets. Most research studies suggest that the Pareto principle applies: 80 percent goes to data integration, and only 20 to analysis. Inverting that balance is the Holy Grail.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor explain why the time has finally come for turning the tables on the status quo in analytics. He'll be briefed by CASK CEO Jonathan Gray, who will showcase his company's big data integration platform, CDAP, which was specifically designed to expedite time-to-value for big data.
Take Action: The New Reality of Data-Driven Business
The Briefing Room with Dr. Robin Bloor and WebAction
Live Webcast on July 23, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=360d371d3a49ad256942f55350aa0a8b
The waiting used to be the hardest part, but not anymore. Today’s cutting-edge enterprises can seize opportunities faster than ever, thanks to an array of technologies that enable real-time responsiveness across the spectrum of business processes. Early adopters are solving critical business challenges by enabling the rapid-fire design, development and production of very specific applications. Functionality can range from improved customer engagement to dynamic machine-to-machine interactions.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor, who will tout a new era in data-driven organizations, and why a data flow architecture will soon be critical for industry leaders. He’ll be briefed by Sami Akbay of WebAction, who will showcase his company’s real-time data management platform, which combines all the component parts needed to access, process and leverage data big and small. He’ll explain how this new approach can provide game-changing power to organizations of all types and sizes.
Visit InsideAnlaysis.com for more information.
Initiate Edinburgh 2019 - Big Data Meets AI
Over 90% of today’s data has been generated in the last two years, and growth rates continue to climb. In this session, we’ll step through challenges and best practices with data capturing, how to derive meaningful insights to help predict the future, and common pitfalls in data analysis.
Come discover how integrated solutions involving Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon Machine Learning/Deep Learning result in effective data systems for data scientists and business users, alike.
Analytic Excellence - Saying Goodbye to Old Constraints
The Briefing Room with Dr. Robin Bloor and Actian
Live Webcast August 6, 2013
http://www.insideanalysis.com
With all the innovations in compute power these days, one of the hardest hurdles to overcome is the tendency to think in old ways. By and large, the processing constraints of yesterday no longer apply. The new constraints revolve around the strategic management of data, and the effective use of business analytics. How can your organization take the helm in this new era of analysis?
Register for this episode of The Briefing Room to find out! Veteran Analyst Wayne Eckerson of The BI Leadership Forum, will explain how a handful of key innovations has significantly changed the game for data processing and analytics. He'll be briefed by John Santaferraro of Actian, who will tout his company's unique position in "scale-up and scale-out" for analyzing data.
Bring Your SAP and Enterprise Data to Hadoop, Kafka, and the Cloud
The world’s largest enterprises run their infrastructure on Oracle, DB2 and SQL and their critical business operations on SAP applications. Organisations need this data to be available in real-time to conduct necessary analytics. However, delivering this heterogeneous data at the speed it’s required can be a huge challenge because of the complex underlying data models and structures and legacy manual processes which are prone to errors and delays.
Unlock these silos of data and enable the new advanced analytics platforms by attending this session.
Find out how to:
• To overcome common challenges faced by enterprises trying to access their SAP data
• You can integrate SAP data in real-time with change data capture (CDC) technology
• Organisations are using Attunity Replicate for SAP to stream SAP data in to Kafka
Speakers:
John Hol, Regional Director, Attunity
Mike Hollobon, Director Business Development, IBT
Smart Enterprise Big Data Bus for the Modern Responsive Enterprise- StreamAna...
Smart Enterprise Big Data Bus for the Modern Responsive Enterprise- StreamAna...Impetus Technologies
On- demand webcast ‘Smart Enterprise Big Data Bus for the Modern Responsive Enterprise’ available at http://lf1.me/6Ob/Hadoop and the Relational Database: The Best of Both Worlds
The Briefing Room with Dr. Robin Bloor and Splice Machine
Live Webcast on August 5, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=71551d669454741c8bd56f2349bdf140
As the pressure of Big Data collides with the reality of daily operations, many organizations are trying to solve the challenge of meeting new requirements without disrupting the flow of business. One solution focuses on the data layer itself, by combining the well known functionality of relational database technology with the scale-out capabilities of Hadoop.
Register for this episode of The Briefing Room to hear from veteran Analyst Dr. Robin Bloor as he outlines the critical components of a business-ready data layer. He’ll be briefed by John Leach and Rich Reimer of Splice Machine who will explain how their solution delivers the best of both data worlds: the trusted capabilities of relational with the infinite scalability of Hadoop. They will also discuss how Hadoop has transformed from a batch-oriented workhorse into a scale-out layer capable of supporting real-time applications and operational analytics using traditional SQL.
Visit InsideAnlaysis.com for more information.
Data Democracy: Journey to User-Facing Analytics - Pulsar Summit SF 2022
There is an increasing need to unleash analytical capabilities directly to the end-users to democratize decision-making. User-Facing Analytics is a new frontier that will shape the products of tomorrow and push the limits of existing technology. It demands a solution that will scale to millions of users to provide fast, real-time insights. In this session, Xiang will talk about his journey to build Apache Pinot to tackle the analytics problem space with the architectural changes and technology inventions made over the past decade. He will also talk about how other big data companies such as LinkedIn, Uber, and Stripe power their user-facing analytical applications.
Data Visualization and the Art of Self-Reliance
The Briefing Room with Robin Bloor and Tableau Software
Live Webcast April 23, 2013
Classic author Ralph Waldo Emerson inspired a generation with his essays on self-reliance. The wisdom of his message still rings true today, and provides a vision for the modern information worker: trust in yourself and your ideas. Then, pull together the right data, and the right visualization of that data, to tell a compelling story. Rely on your instincts, and use information assets to your advantage.
Check out the slides from this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor provide a roadmap for enabling self-reliant knowledge workers. He'll be briefed by Suzanne Hoffman of Tableau Software, who will demonstrate how the latest version of Tableau's data visualization platform can empower information professionals from all segments of the enterprise. She'll demonstrate how anyone from a casual business user to a Data Scientist can quickly create, modify and share visualizations.
Visit: http://www.insideanalysis.com
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
In our exclusive webinar, you'll learn why event-driven architecture is the key to unlocking cost efficiency, operational effectiveness, and profitability. Gain insights on how this approach differs from API-driven methods and why it's essential for your organization's success.
Streaming analytics
Stream computing is a processing paradigm that brings the analytics to the data, rather than storing the data first.
SQL Saturday Redmond The Power Platform
The slide deck for the Power Platform Presentation in SQL Saturday Redmond 2019. We have reviewed the power Platform Components, why is it better together and how to make it happen. During the demo all the options of implementation between the Power Apps and PowerBI were demonstrated. Including the data visualization changes with new data feed. Use some of the following ideas in your organization and POC's for more complex implementations.
The State of Streaming Analytics: The Need for Speed and Scale
Peter Vescuso, CMO, VoltDB and Mike Gualtieri Principal Analyst, Forrester Research talk about the latest on the streaming analytics market, in-memory database technology, and the emergence of ‘translytics’. Also hear how the latest release of VoltDB, the in-memory operational database, simplifies the distributed system complexity of building high-speed data pipelines with rapid ingestion of data, real-time analytics, Apache Kafka support, and Elasticsearch support.
Colin Carter - LSPs and APIs
SCONUL Conference 20-21 June 2013, Dublin
SCONUL Fringe session - LSPs and APIs: Integration and the next generation of library management systems, with Colin Carter, Sales Account Manager for the UK and Northern Europe, Innovative Interfaces Inc.
Similar to Jeeves Grows Up: An AI Chatbot for Performance and Quality (20)
Business Intelligence solutions using Excel 2013 and Power BI
Business Intelligence solutions using Excel 2013 and Power BI
Take Action: The New Reality of Data-Driven Business
Take Action: The New Reality of Data-Driven Business
Analytic Excellence - Saying Goodbye to Old Constraints
Analytic Excellence - Saying Goodbye to Old Constraints
Enabling Self Service Business Intelligence using Excel
Enabling Self Service Business Intelligence using Excel
Bring Your SAP and Enterprise Data to Hadoop, Kafka, and the Cloud
Bring Your SAP and Enterprise Data to Hadoop, Kafka, and the Cloud
Smart Enterprise Big Data Bus for the Modern Responsive Enterprise- StreamAna...
Smart Enterprise Big Data Bus for the Modern Responsive Enterprise- StreamAna...
Hadoop and the Relational Database: The Best of Both Worlds
Hadoop and the Relational Database: The Best of Both Worlds
Data Democracy: Journey to User-Facing Analytics - Pulsar Summit SF 2022
Data Democracy: Journey to User-Facing Analytics - Pulsar Summit SF 2022
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
Disrupting Big Data with Apache Spark in the Cloud
Disrupting Big Data with Apache Spark in the Cloud
The State of Streaming Analytics: The Need for Speed and Scale
The State of Streaming Analytics: The Need for Speed and Scale
More from Databricks
Data Lakehouse Symposium | Day 1 | Part 1
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse Symposium | Day 1 | Part 2
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse Symposium | Day 2
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse Symposium | Day 4
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.
Democratizing Data Quality Through a Centralized Platform
Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how do we ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Investing in shared, flexible components and practices for monitoring data health is crucial for a complex data organization to rapidly and effectively scale.
At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including:
Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal
Performing data quality validations using libraries built to work with spark
Dynamically generating pipelines that can be abstracted away from users
Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers
Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. We enable data scientists to deploy and operate their models independently, with minimal need for handoffs or gatekeeping. By writing a simple function and calling out to an intuitive API, data scientists can harness a suite of platform-provided tooling meant to make ML operations easy. In this talk, we will dive into the abstractions the Data Platform team has built to enable this. We will go over the interface data scientists use to specify a model and what that hooks into, including online deployment, batch execution on Spark, and metrics tracking and visualization.
Stage Level Scheduling Improving Big Data and AI Integration
In this talk, I will dive into the stage level scheduling feature added to Apache Spark 3.1. Stage level scheduling extends upon Project Hydrogen by improving big data ETL and AI integration and also enables multiple other use cases. It is beneficial any time the user wants to change container resources between stages in a single Apache Spark application, whether those resources are CPU, Memory or GPUs. One of the most popular use cases is enabling end-to-end scalable Deep Learning and AI to efficiently use GPU resources. In this type of use case, users read from a distributed file system, do data manipulation and filtering to get the data into a format that the Deep Learning algorithm needs for training or inference and then sends the data into a Deep Learning algorithm. Using stage level scheduling combined with accelerator aware scheduling enables users to seamlessly go from ETL to Deep Learning running on the GPU by adjusting the container requirements for different stages in Spark within the same application. This makes writing these applications easier and can help with hardware utilization and costs.
There are other ETL use cases where users want to change CPU and memory resources between stages, for instance there is data skew or perhaps the data size is much larger in certain stages of the application. In this talk, I will go over the feature details, cluster requirements, the API and use cases. I will demo how the stage level scheduling API can be used by Horovod to seamlessly go from data preparation to training using the Tensorflow Keras API using GPUs.
The talk will also touch on other new Apache Spark 3.1 functionality, such as pluggable caching, which can be used to enable faster dataframe access when operating from GPUs.
Simplify Data Conversion from Spark to TensorFlow and PyTorch
In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks.
Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model?
The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity.
The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters.
In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.
Scaling your Data Pipelines with Apache Spark on Kubernetes
There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal.
In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark.
Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations.
Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.
Sawtooth Windows for Feature Aggregations
In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark.
Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue
· Why?
o Custom queries on top a table; We load the data once and query N times
· Why not Structured Streaming
· Working Solution using Redis
Niche 2 : Distributed Counters
· Problems with Spark Accumulators
· Utilize Redis Hashes as distributed counters
· Precautions for retries and speculative execution
· Pipelining to improve performance
Raven: End-to-end Optimization of ML Prediction Queries
Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components.
We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure.
This allows us to introduce optimization rules that
(i) reduce unnecessary computations by passing information between the data processing and ML operators
(ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and
(iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator.
We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.
Massive Data Processing in Adobe Using Delta Lake
At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences.
What are we storing?
Multi Source – Multi Channel Problem
Data Representation and Nested Schema Evolution
Performance Trade Offs with Various formats
Go over anti-patterns used
(String FTW)
Data Manipulation using UDFs
Writer Worries and How to Wipe them Away
Staging Tables FTW
Datalake Replication Lag Tracking
Performance Time!
Machine Learning CI/CD for Email Attack Detection
Detecting advanced email attacks at scale is a challenging ML problem, particularly due to the rarity of attacks, adversarial nature of the problem, and scale of data. In order to move quickly and adapt to the newest threat we needed to build a Continuous Integration / Continuous Delivery pipeline for the entire ML detection stack. Our goal is to enable detection engineers and data scientists to make changes to any part of the stack including joined datasets for hydration, feature extraction code, detection logic, and develop/train ML models.
In this talk, we discuss why we decided to build this pipeline, how it is used to accelerate development and ensure quality, and dive into the nitty-gritty details of building such a system on top of an Apache Spark + Databricks stack.
Intuitive & Scalable Hyperparameter Tuning with Apache Spark + Fugue
Hyperparameter tuning is critical in model development. And its general form: parameter tuning with an objective function is also widely used in industry. On the other hand, Apache Spark can handle massive parallelism, and Apache Spark ML is a solid machine learning solution.
But we have not seen a general and intuitive distributed parameter tuning solution based on Apache Spark, why?
Not every tuning problem is on Apache Spark ML models. How can Apache Spark handle general models?
Not every tuning problem is a parallelizable grid or random search. Bayesian optimization is sequential, how can Apache Spark help in this case?
Not every tuning problem is single epoch, deep learning is not. How to fit algos such as hyperband and ASHA into Apache Spark?
Not every tuning problem is a machine learning problem, for example simulation + tuning is also common. How to generalize?
In this talk, we are going to show how using Fugue-Tune and Apache Spark together can eliminate these painpoints
Fugue-Tune like Fugue, is a “super framework” – an absraction layer unifying existing solutions such as Hyperopt and Optuna
It firstly models the general tuning problems, independent from machine learning
It is designed for both small and large scale problems. It can always fully parallelize the distributable part of a tuning problem
It works for both classical and deep learning models. With Fugue, running hyperband and ASHA becomes possible on Apache Spark.
In the demo, you will see how to do any type of tuning in a consistent, intuitive, scalable and minimal way. And you will see a live demo of the amazing performance.
Infrastructure Agnostic Machine Learning Workload Deployment
When it comes to Large Scale data processing and Machine Learning, Apache Spark is no doubt one of the top battle-tested frameworks out there for handling batched or streaming workloads. The ease of use, built-in Machine Learning modules, and multi-language support makes it a very attractive choice for data wonks. However bootstrapping and getting off the ground could be difficult for most teams without leveraging a Spark cluster that is already pre-provisioned and provided as a managed service in the Cloud, while this is a very attractive choice to get going, in the long run, it could be a very expensive option if it’s not well managed.
As an alternative to this approach, our team has been exploring and working a lot with running Spark and all our Machine Learning workloads and pipelines as containerized Docker packages on Kubernetes. This provides an infrastructure-agnostic abstraction layer for us, and as a result, it improves our operational efficiency and reduces our overall compute cost. Most importantly, we can easily target our Spark workload deployment to run on any major Cloud or On-prem infrastructure (with Kubernetes as the common denominator) by just modifying a few configurations.
In this talk, we will walk you through the process our team follows to make it easy for us to run a production deployment of our Machine Learning workloads and pipelines on Kubernetes which seamlessly allows us to port our implementation from a local Kubernetes set up on the laptop during development to either an On-prem or Cloud Kubernetes environment
Improving Apache Spark for Dynamic Allocation and Spot Instances
This presentation will explore the new work in Spark 3.1 adding the concept of graceful decommissioning and how we can use this to improve Spark’s performance in both dynamic allocation and spot/preemptable instances. Together we’ll explore how Spark’s dynamic allocation has evolved over time, and why the different changes have been needed. We’ll also look at the multi-company collaboration that resulted in being able to deliver this feature and I’ll end with encouraging pointers on how to get more involved in Spark’s development.
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5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
Democratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized Platform
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
Stage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI Integration
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Raven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction Queries
Intuitive & Scalable Hyperparameter Tuning with Apache Spark + Fugue
Intuitive & Scalable Hyperparameter Tuning with Apache Spark + Fugue
Infrastructure Agnostic Machine Learning Workload Deployment
Infrastructure Agnostic Machine Learning Workload Deployment
Improving Apache Spark for Dynamic Allocation and Spot Instances
Improving Apache Spark for Dynamic Allocation and Spot Instances
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三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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#SQL #Views #Privacy #Compliance #DataLake
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
The affect of service quality and online reviews on customer loyalty in the E...
Journal of Management and Creative Business.published book./
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Object oriented system analysis and design
Learn SQL from basic queries to Advance queries
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Adjusting OpenMP PageRank : SHORT REPORT / NOTES
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Adjusting primitives for graph : SHORT REPORT / NOTES
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness in Kansas City, MO
To: Tom Tresser
From: Roger Warren
做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样
原版定制【Q微信:741003700】《(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书》【Q微信:741003700】成绩单 、雅思、外壳、留信学历认证永久存档查询，采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【Q微信741003700】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信741003700】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
一比一原版(Dalhousie毕业证书)达尔豪斯大学毕业证如何办理
原版定制【微信:41543339】【(Dalhousie毕业证书)达尔豪斯大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
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Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
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Jeeves Grows Up: An AI Chatbot for Performance and Quality
- 1. 1 Jeeves Grows Up: An AI Chatbot for Performance and Quality Shivnath Babu CTO/Cofounder @ Unravel Adjunct Professor @ Duke University TRUSTED BY
- 2. 2 About the speaker Shivnath Babu Cofounder/CTO at Unravel Adjunct Professor of Computer Science at Duke University Focusing on manageability of data pipelines and the modern data stack Recipient of US National Science Foundation CAREER Award, IBM Faculty Award, HP Labs Innovation Research Award
- 3. 3 Unravel radically simplifies DataOps & has strong adoption across platforms & industries • Brings together information about all your apps, clusters, resource utilization, users, & datasets in a single place • Creates end-to-end view of data pipelines to easily track & understand issues • Tracks & reports on usage across environments • Checks for & alerts on anomalous behavior • Uses AI/ML to troubleshoot & optimize apps to meet desired performance & cost needs • Spots & fixes inefficient usage • Ensures efficiency, quality, & performance of all apps in development & production
- 4. 4 4 Chatbot A program that conducts a conversation via text or voice
- 5. 5 5 #UnifiedAnalytics #SparkAISummit The happy Spark user
- 6. 6 6 #UnifiedAnalytics #SparkAISummit “I have no clue which cloud instance type to pick for my workload” “My cloud costs are getting out of control. Help!” “I have no idea why my app is slow” “My app failed and I don’t know why!” The UNhappy Spark user
- 7. 7 • Many levels of dependent stack traces • Identifying the root cause is hard and time consuming 7 Typical app failure in Spark #UnifiedAnalytics #SparkAISummit
- 8. 8 8 #UnifiedAnalytics #SparkAISummit “My app failed and I don’t know why!” Chatbot “I know that sucks! Let me take a look here …” “I see the problem. Executors are running out of memory” “Setting spark.executor.memory to 12g fixes the problem. I have verified it. See this run here” “Wow. Thanks. You are awesome!” Spark User
- 9. 9 9 Let us see it in action
- 10. 10 10 Fast forward to 2021
- 11. 11 Now every company is a data company Powered by Data, ML and AI
- 12. 12 Most companies have 10+ mission-critical Data Pipelines Data Pipelines Now every company is a data company
- 14. 14 Most companies have 10+ mission-critical Data Pipelines Data Pipelines Data Stack for these pipelines is multi-system & complex Data Stack Now every company is a data company
- 20. 20 Most companies have 10+ mission-critical Data Pipelines Data Pipelines Data Stack for these pipelines is multi-system & complex Data Stack 33% & growing # of data teams follow a DataOps practice DataOps Now every company is a data company
- 21. 21 SLA misses are creating problems We asked 200+ companies how they manage their data pipelines We only detect the fire after it starts! Our pipeline schedules are all messed up! We need CI/CD for our pipelines Fixing problems takes weeks Users are always complaining I am wasting most of my time with bad data Do devs ever #!$ test their pipelines? Two failed attempts to migrate to cloud Cost reduction is our #1 priority
- 22. 22 Effective DataOps practice is required to solve these problems with data pipelines SLA misses are creating problems We only detect the fire after it starts! Our pipeline schedules are all messed up! We need CI/CD for our pipelines Fixing problems takes weeks Users are always complaining I am wasting most of my time with bad data Do devs ever #!$ test their pipelines? Two failed attempts to migrate to cloud Cost reduction is our #1 priority
- 23. 23 We created Unravel’s Pipeline Observer to simplify DataOps Real-time Store Root Cause Analysis Service Baselining Service Pipeline Observer UI/API Correlation Services Logs Metrics Traces Metadata Conf Events Chatbot SLA Tracking UI Pipeline Capacity Planning Proactive Alerting Usage / Cost Chargeback UI
- 24. 24 Modern Data Stack composed of: 1. Databricks (Advanced Analytics with Spark) 2. Azure Data Lake Storage (Data Lake) 3. Airflow (Orchestration) 4. dbt (Data Transformation) 5. Great Expectations (Data Quality/Validation) 6. Slack (Chatbot, Team Comm., & Alerting) 7. Unravel (End-to-end Observability) Demo Stack
- 25. 25 1. Pipeline in danger of missing performance SLA 2. Pipeline in danger of cost overrun 3. Pipeline in danger of breaking due to data quality problems Demo Scenarios
- 26. 26 26 Let us see it in action
- 27. 27 In summary AI-driven DataOps to manage Data Pipelines for the New Data Stack • Develop & manage data pipelines with ease • Save time & money 27 Sign up for a free trial, we value your feedback! https://unraveldata.com/saas-free-trial We are hiring shivnath@unraveldata.com
- 28. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.