Matchmaking Audiences to Content with Lucy Wang

•

0 gefällt mir•628 views

At BuzzFeed, it’s our mission to keep our social media audiences maximally engaged, by leveraging the hundreds of original articles, videos, and graphics we produce daily. With our vast network of social media distribution channels, we have a team of human curators who handpick the content that is most relevant and interesting to each channel’s audience. Of course, as our content production scales, curation becomes increasingly difficult — more and more content slips through the cracks and don’t reach their full potential audience. To this end, we’ve built a curation service, powered by natural language processing and deep learning techniques. It learns to: 1. Find the most relevant content for each channel 2. Identify and resurface content with evergreen appeal 3. Determine the optimal times in the day to post each content I’ll discuss the implications of this use case, how we developed our methods, and how the service cohabitates with existing human curation.

Daten & Analysen

Lucy X. Wang, BuzzFeed
Matchmaking Audiences
to Content
#AI8SAIS

BuzzFeed
2#AI8SAIS

3#AI8SAIS

A vast network of social channels
• 10+ platforms
• 400+ accounts
• 1200+ pieces of
content published a
week
4#AI8SAIS

But how do we scale our curation?
5#AI8SAIS

By mimicking human curation
6#AI8SAIS

3 use cases for AI
1. Surface relevant content for each channel
2. Surface evergreen content
3. Automate publishing
7#AI8SAIS

Case 1: How do we recommend
relevant content for each channel?
8#AI8SAIS

Content too often slips through the
cracks
9#AI8SAIS

Training on human curation data
10#AI8SAIS

A language-based relevance model
11#AI8SAIS

Predicting relevance from titles
12#AI8SAIS

Consolidated into one model
13#AI8SAIS

Examples
14#AI8SAIS

Impact
15#AI8SAIS
unlike rate remains stable

Case 2: How do we find evergreen
content to re-circulate?
16#AI8SAIS

Taking advantage of evergreen posts
17#AI8SAIS
2016
55K clicks
2018
36K clicks

A model based on content and traffic
performance
18#AI8SAIS

Not much human curation data
19#AI8SAIS

timely
evergreen
Our labelling proxy
20#AI8SAIS

timely?
Handling poorly-labeled data
21#AI8SAIS

Modeling with uncertainty
22#AI8SAIS
!"#$"%&'() =
!
+,(.()+
+
1
+
234 ,(.()+

Examples
23#AI8SAIS

Impact
median clicks per post doubled
(+108%) with addition of
evergreen recommendations
24#AI8SAIS
unlike rate remains stable

Case 3: How do we automate
publishing?
25#AI8SAIS

Mixing human and AI curation
26#AI8SAIS

Optimal scheduling model
27#AI8SAIS

Trying all scheduling times
28#AI8SAIS

How do we validate these models?
29#AI8SAIS

Limits of AI validation
30#AI8SAIS

The loop isn’t complete without
humans
31#AI8SAIS

Gathering user feedback
32#AI8SAIS

Adapting to feedback
33#AI8SAIS

Human decisions will always be key
34#AI8SAIS

35#AI8SAIS
Human decisions will always be key
19M views
800,000
simultaneous
viewers
33M views

Thank you!
36#AI8SAIS

Weitere ähnliche Inhalte

Mehr von Databricks

DW Migration Webinar-March 2022.pptx

DW Migration Webinar-March 2022.pptx

DW Migration Webinar-March 2022.pptx

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 1

Data Lakehouse Symposium | Day 1 | Part 1

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

Data Lakehouse Symposium | Day 1 | Part 2

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

Data Lakehouse Symposium | Day 2

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

Data Lakehouse Symposium | Day 4

Data Lakehouse Symposium | Day 4

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.

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

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

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

Democratizing Data Quality Through a Centralized Platform

Democratizing Data Quality Through a Centralized Platform

Democratizing Data Quality Through a Centralized Platform

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.

Learn to Use Databricks for Data Science

Learn to Use Databricks for Data Science

Learn to Use Databricks for Data Science

Application performance monitoring (APM) has become the cornerstone of software engineering allowing engineering teams to quickly identify and remedy production issues. However, as the world moves to intelligent software applications that are built using machine learning, traditional APM quickly becomes insufficient to identify and remedy production issues encountered in these modern software applications. As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored. In this talk, I draw upon my (and my team’s) experience building an ML Monitoring system from the ground up and deploying it on customer workloads running large-scale ML training with Spark as well as real-time inference systems. I will highlight how the key principles and architectural choices of APM don’t apply to ML monitoring. You’ll learn why, understand what ML Monitoring can successfully borrow from APM, and hear what is required to build a scalable, robust ML Monitoring architecture.

Why APM Is Not the Same As ML Monitoring

Why APM Is Not the Same As ML Monitoring

Why APM Is Not the Same As ML Monitoring

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.

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

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

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.

Stage Level Scheduling Improving Big Data and AI Integration

Stage Level Scheduling Improving Big Data and AI Integration

Stage Level Scheduling Improving Big Data and AI Integration

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.

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Simplify Data Conversion from Spark to TensorFlow and PyTorch

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 your Data Pipelines with Apache Spark on Kubernetes

Scaling your Data Pipelines with Apache Spark on Kubernetes

Scaling your Data Pipelines with Apache Spark on Kubernetes

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.

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

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.

Sawtooth Windows for Feature Aggregations

Sawtooth Windows for Feature Aggregations

Sawtooth Windows for Feature Aggregations

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

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

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.

Re-imagine Data Monitoring with whylogs and Spark

Re-imagine Data Monitoring with whylogs and Spark

Re-imagine Data Monitoring with whylogs and Spark

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.

Raven: End-to-end Optimization of ML Prediction Queries

Raven: End-to-end Optimization of ML Prediction Queries

Raven: End-to-end Optimization of ML Prediction Queries

Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis. Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them. Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy. This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.

Processing Large Datasets for ADAS Applications using Apache Spark

Processing Large Datasets for ADAS Applications using Apache Spark

Processing Large Datasets for ADAS Applications using Apache Spark

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!

Massive Data Processing in Adobe Using Delta Lake

Massive Data Processing in Adobe Using Delta Lake

Massive Data Processing in Adobe Using Delta Lake

Mehr von Databricks (20)

DW Migration Webinar-March 2022.pptx

DW Migration Webinar-March 2022.pptx

DW Migration Webinar-March 2022.pptx

Data Lakehouse Symposium | Day 1 | Part 1

Data Lakehouse Symposium | Day 1 | Part 1

Data Lakehouse Symposium | Day 1 | Part 1

Data Lakehouse Symposium | Day 1 | Part 2

Data Lakehouse Symposium | Day 1 | Part 2

Data Lakehouse Symposium | Day 1 | Part 2

Data Lakehouse Symposium | Day 2

Data Lakehouse Symposium | Day 2

Data Lakehouse Symposium | Day 2

Data Lakehouse Symposium | Day 4

Data Lakehouse Symposium | Day 4

Data Lakehouse Symposium | Day 4

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

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

Democratizing Data Quality Through a Centralized Platform

Learn to Use Databricks for Data Science

Learn to Use Databricks for Data Science

Learn to Use Databricks for Data Science

Why APM Is Not the Same As ML Monitoring

Why APM Is Not the Same As ML Monitoring

Why APM Is Not the Same As ML Monitoring

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

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

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

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 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

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Sawtooth Windows for Feature Aggregations

Sawtooth Windows for Feature Aggregations

Sawtooth Windows for Feature Aggregations

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Re-imagine Data Monitoring with whylogs and Spark

Re-imagine Data Monitoring with whylogs and Spark

Re-imagine Data Monitoring with whylogs and Spark

Raven: End-to-end Optimization of ML Prediction Queries

Raven: End-to-end Optimization of ML Prediction Queries

Raven: End-to-end Optimization of ML Prediction Queries

Processing Large Datasets for ADAS Applications using Apache Spark

Processing Large Datasets for ADAS Applications using Apache Spark

Processing Large Datasets for ADAS Applications using Apache Spark

Massive Data Processing in Adobe Using Delta Lake

Massive Data Processing in Adobe Using Delta Lake

Massive Data Processing in Adobe Using Delta Lake

Kürzlich hochgeladen

社内勉強会資料_Object Recognition as Next Token Prediction

社内勉強会資料_Object Recognition as Next Token Prediction

社内勉強会資料_Object Recognition as Next Token Prediction

NABLAS株式会社

SCI8-Q4-MOD11.pdfwrwujrrjfaajerjrajrrarj

SCI8-Q4-MOD11.pdfwrwujrrjfaajerjrajrrarj

SCI8-Q4-MOD11.pdfwrwujrrjfaajerjrajrrarj

adimosmejiaslendon

In today's digital world, credit card fraud is a growing concern. This project explores machine learning techniques for credit card fraud detection. We delve into building models that can identify suspicious transactions in real-time, protecting both consumers and financial institutions. for more detection and machine learning algorithm explore data science and analysis course: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Credit Card Fraud Detection: Safeguarding Transactions in the Digital Age

Credit Card Fraud Detection: Safeguarding Transactions in the Digital Age

Credit Card Fraud Detection: Safeguarding Transactions in the Digital Age

Boston Institute of Analytics

认证毕业证书【微信:95270640】【(UPenn毕业证）宾夕法尼亚大学毕业证】【微信:95270640】（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【微信:95270640】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:95270640】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！校名:学校英文全称授予学位：本部分将注明获得的具体学位名称。毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。办理UPenn毕业证书)微信:95270640宾夕法尼亚大学毕业证价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。综上所述，办理UPenn毕业证书)宾夕法尼亚大学毕业证微信:95270640是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

如何办理(UPenn毕业证书）宾夕法尼亚大学毕业证成绩单本科硕士学位证留信学历认证

如何办理(UPenn毕业证书）宾夕法尼亚大学毕业证成绩单本科硕士学位证留信学历认证

如何办理(UPenn毕业证书）宾夕法尼亚大学毕业证成绩单本科硕士学位证留信学历认证

5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed

5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed

5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed

原版定制【微信:153539019】《(UWO毕业证书）西安大略大学毕业证》【微信:153539019】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【微信153539019】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153539019】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。

原件一样(UWO毕业证书）西安大略大学毕业证成绩单留信学历认证

原件一样(UWO毕业证书）西安大略大学毕业证成绩单留信学历认证

原件一样(UWO毕业证书）西安大略大学毕业证成绩单留信学历认证

Context 1. Social Enterprise collected data on customers & wants to make insight-informed decisions. Objective 2. To identify customer segments to customised offers for each segment. Strategy 3. Explore & Clean data for analysis. 4. Perform K-Means Clustering, in Orange, to find possible segments in the customer data. 5. Tune the model to improve its performance. 6. Visualise the findings, share conclusions, and give insight-driven recommendations. Author: Anthony mok date: 18 Nov 2023 Email: xxiaohao@yahoo.com

Identify Customer Segments to Create Customer Offers for Each Segment - Appli...

Identify Customer Segments to Create Customer Offers for Each Segment - Appli...

Identify Customer Segments to Create Customer Offers for Each Segment - Appli...

ThinkInnovation

Yilin Xia (yilinx2@illinois.edu), Shawn Bowers (bowers@gonzaga.edu), Lan Li (lanl2@illinois.edu), and Bertram Ludäscher (ludaesch@illinois.edu) Presented at IDCC-2024 in Edinburg. ABSTRACT. We propose a new approach for modeling and reconciling conflicting data cleaning actions. Such conflicts arise naturally in collaborative data curation settings where multiple experts work independently and then aim to put their efforts together to improve and accelerate data cleaning. The key idea of our approach is to model conflicting updates as a formal argumentation framework (AF). Such argumentation frameworks can be automatically analyzed and solved by translating them to a logic program PAF whose declarative semantics yield a transparent solution with many desirable properties, e.g., uncontroversial updates are accepted, unjustified ones are rejected, and the remaining ambiguities are exposed and presented to users for further analysis. After motivating the problem, we introduce our approach and illustrate it with a detailed running example introducing both well-founded and stable semantics to help understand the AF solutions. We have begun to develop open source tools and Jupyter notebooks that demonstrate the practicality of our approach. In future work we plan to develop a toolkit for conflict resolution that can be used in conjunction with OpenRefine, a popular interactive data cleaning tool.

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Bertram Ludäscher

SAC 25 Final National, Regional & Local Angel Group Investing Insights 2024 0...

SAC 25 Final National, Regional & Local Angel Group Investing Insights 2024 0...

SAC 25 Final National, Regional & Local Angel Group Investing Insights 2024 0...

Elaine Werffeli

Cara Menggugurkan Kehamilan Usia Janin 1-8 Bulan dengan Aman Hub: 082134680322 Jual Obat Aborsi Asli Obat Penggugur Kandungan Alodokter Rekomendasi Cytotec 400 mcg Untuk Aborsi Terbaik. Obat Penggugur Kandungan Asli Resep Halodoc / Alodokter Hub: 082134680322 Merekomendasikan Dengan Pil Cytotec Misoprostol 400 mg Untuk Aborsi Terbaik dan Aman – Beli Obat Aborsi di apotik tanpa resep dokter Untuk Pemesanan dan Konsultasi Spesialis Dokter Kandungan (SpOG) Bisa Chat nomer Whatsapps Save Dahulu Nomer Kami. INFO KONSULTASI: 0821 3468 0322 Obat Penggugur Kandungan. Cara Menggugurkan Kandungan Yang biasanya menjadi pilihan utama apabila kehamilan masih berada di usia awal Obat Telat Datang Bulan Adalah Salah Satu Obat Berfungsi Untuk Melunturkan Janin Pada Seseorang Wanita Yang Paling Ampuh Dengan Usia 1, 2, 3, 4, 5 Bulan Dijamin Tidak Ada Efek Samping Dan Bersih Tuntas. Obat Telat Datang Bulan Misoprostol Cytotec Pfizer Adalah Obat Untuk Melancarkan Haid Atau Obat Aborsi Untuk Menggugurkan Kandungan Yang Paling Aman Dan Ampuh, Pil Telat Haid Usia 1, 2, 3, 4, 5 Bulan Dijamin 100% Tidak Ada Efek Samping Dan Bersih Dan Cepat Tuntas SELAMAT DATANG DI SITUS WEB KAMI Hubunggi: https://cytotec-store.com Hp / Wa : 0821 3468 0322

Jual Obat Aborsi Bandung (Asli No.1) Wa 082134680322 Klinik Obat Penggugur Ka...

Jual Obat Aborsi Bandung (Asli No.1) Wa 082134680322 Klinik Obat Penggugur Ka...

Jual Obat Aborsi Bandung (Asli No.1) Wa 082134680322 Klinik Obat Penggugur Ka...

Jual Obat Aborsi Cytotec di Tarakan 081336238223 Apotik Online 24 Jam ( 081336238223 ) Jual Obat Aborsi Cod Tarakan, Penjual Cytotec Di ambawara, Alamat Jual Obat Cytotec Di Tarakan, Toko Jual Cytotec Di Tarakan, Beli Obat Cytotec Di Tarakan, Penjual Obat Gastrul Tarakan, Misoprostol Asli Tarakan, Tempat Jual Obat Aborsi Daerah Tarakan, Obat Penggugur Kandungan Tarakan, Penjual Cytotec Wilayah Tarakan, Obat Aborsi Di Tarakan, Harga Cytotec Di Tarakan, Obat Aborsi Online Tarakan, Obat Penggugur Janin Tarakan, Alamat Toko Obat Aborsi Di Tarakan, Apotik Yang Menjual Cytotec Di Tarakan, Tempat Beli Cytotec Di Tarakan, Tempat Penjualan Cytotec Di Tarakan, Toko Obat Aborsi Tarakan, Tempat Aborsi Tarakan, Klinik Aborsi Tarakan, Tidak Menerima COD, Dengan harga yang bisa anda pilih sesuai usia kandungan anda. Obat Aborsi Cytotec ini mampu menggugurkan kandungan sampai usia 1 – 5 bulan dengan sangat efektif. Jual Obat Aborsi Penggugur Kandungan Cytotec Asli Terpercaya (Whatsapp) 081336238223 Di sini kami menjual berbagai macam obat aborsi cytotec misoprostol pfizer asli obat penggugur kandungan cytotec asli Apotik Penjual Obat Aborsi belakangan ini banyak sekali yang mengaku bidan yang bisa menggugurkan kandungan ( aborsi ) atau dokter, saya harap anda hati2 untuk bidan kuret, karena alat kuret hanya dokter yang mempunyai alat tersebut, kalau si bidan atau si dokter tersebut menawarkan obat aborsi cytotec, atau obat aborsi lainnya, pastikan dulu website atau blognya mempunyai bukti bahwa dia benar2 memiliki obat untuk aborsi janin dan kandungan tersebut, jadi kalau sebuah blog tidak punya bukti memiliki obat jangan coba2 membeli dari web atau blog tersebut, obat saja tidak punya lalu apa yang mau dikirim ? Bagi wanita di jika ingin menggugurkan kandungannya karena terbelit masalah yang sangat serius,misalnya, ada penyakit yang mengancam keselamatan sang ibu yang mengandungnya,masalah ekonomi, hasil pemerkosaan, maupun hasil hubungan gelap dan ternyata pasangannya tidak mau bertanggung jawab, oleh sebab itu jika anda tersandung masalah tersebut dan jika anda ingin melakukan tindakan aborsi dengan menggunkan obat, anda tidak salah tempat, karena disini menyediakan Obat Penggugur Kandungan yang bisa anda beli disini. Bagaimana cara mendapatkan obat di Apotik Penjual Obat Aborsi Cytotec ? kami akan memberikan kemudahan untuk anda tanpa harus mencari atau membawa resep dokter, Obat cytotec untuk aborsi merupakan tahap bagaimana seseorang dalam melakukan tindakan menggugurkan sebuah kehamilan atau kandungan dengan metode obat yang di dapat di kami dengan cara dan langkah-langkah yang diberikan oleh pihak kami yang ahli dalam proses pengguguran sebuah kandungan yang berusia 1 hingga 6 bulan lamanya obat aborsi yang tingkat keberhasilannya mencapai 100% karena obat inisudah sangat banyak digunakan bagi wanita yang tidak ingin meneruskan kandungan dikarenakan sesuatu hal tertentu, obat ini mempunyai nama Mifepristone ( juga dikenal dengan nama pil abortus, RU 486,

obat aborsi Tarakan wa 081336238223 jual obat aborsi cytotec asli di Tarakan9...

obat aborsi Tarakan wa 081336238223 jual obat aborsi cytotec asli di Tarakan9...

obat aborsi Tarakan wa 081336238223 jual obat aborsi cytotec asli di Tarakan9...

Harnessing the Power of GenAI for BI and Reporting.pptx

Harnessing the Power of GenAI for BI and Reporting.pptx

Harnessing the Power of GenAI for BI and Reporting.pptx

Displacement, Velocity, Acceleration, and Second Derivatives

Displacement, Velocity, Acceleration, and Second Derivatives

Displacement, Velocity, Acceleration, and Second Derivatives

DS Lecture-1 about discrete structure .ppt

DS Lecture-1 about discrete structure .ppt

DS Lecture-1 about discrete structure .ppt

TanveerAhmed817946

原版定制【微信:153539019】《澳洲拉筹伯大学毕业证（LaTrobe毕业证书）》【微信:153539019】（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【微信153539019】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153539019】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。

如何办理澳洲拉筹伯大学毕业证（LaTrobe毕业证书）成绩单原件一模一样

如何办理澳洲拉筹伯大学毕业证（LaTrobe毕业证书）成绩单原件一模一样

如何办理澳洲拉筹伯大学毕业证（LaTrobe毕业证书）成绩单原件一模一样

In my capstone project, I investigated the impact of COVID-19 on education. Using data analysis and statistical methods, I explored various aspects such as enrollment trends, access to resources, and socioeconomic disparities. I found a significant association between children missing classes and a lack of internet connection at home, as well as between household financial situations and children's enrollment in school. These findings highlight the importance of addressing disparities in internet access, household finances, and geographical location to ensure equal educational opportunities for all students during and beyond the pandemic.

Capstone in Interprofessional Informatic // IMPACT OF COVID 19 ON EDUCATION

Capstone in Interprofessional Informatic // IMPACT OF COVID 19 ON EDUCATION

Capstone in Interprofessional Informatic // IMPACT OF COVID 19 ON EDUCATION

LakpaYanziSherpa

ℂall Girls In Navi Mumbai Hire Me Neha 9910780858 Top Class ℂall Girl Serviℂe...

ℂall Girls In Navi Mumbai Hire Me Neha 9910780858 Top Class ℂall Girl Serviℂe...

ℂall Girls In Navi Mumbai Hire Me Neha 9910780858 Top Class ℂall Girl Serviℂe...

Amara arora$V15

DBMS UNIT 5 46 CONTAINS NOTES FOR THE STUDENTS

DBMS UNIT 5 46 CONTAINS NOTES FOR THE STUDENTS

DBMS UNIT 5 46 CONTAINS NOTES FOR THE STUDENTS

Digital Transformation Playbook by Graham Ware

Digital Transformation Playbook by Graham Ware

Digital Transformation Playbook by Graham Ware

Huawei Ransomware Protection Storage Solution Technical Overview Presentation...

Huawei Ransomware Protection Storage Solution Technical Overview Presentation...

Huawei Ransomware Protection Storage Solution Technical Overview Presentation...

Kürzlich hochgeladen (20)

社内勉強会資料_Object Recognition as Next Token Prediction

社内勉強会資料_Object Recognition as Next Token Prediction

社内勉強会資料_Object Recognition as Next Token Prediction

SCI8-Q4-MOD11.pdfwrwujrrjfaajerjrajrrarj

SCI8-Q4-MOD11.pdfwrwujrrjfaajerjrajrrarj

SCI8-Q4-MOD11.pdfwrwujrrjfaajerjrajrrarj

Credit Card Fraud Detection: Safeguarding Transactions in the Digital Age

Credit Card Fraud Detection: Safeguarding Transactions in the Digital Age

Credit Card Fraud Detection: Safeguarding Transactions in the Digital Age

如何办理(UPenn毕业证书）宾夕法尼亚大学毕业证成绩单本科硕士学位证留信学历认证

如何办理(UPenn毕业证书）宾夕法尼亚大学毕业证成绩单本科硕士学位证留信学历认证

如何办理(UPenn毕业证书）宾夕法尼亚大学毕业证成绩单本科硕士学位证留信学历认证

5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed

5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed

5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed

原件一样(UWO毕业证书）西安大略大学毕业证成绩单留信学历认证

原件一样(UWO毕业证书）西安大略大学毕业证成绩单留信学历认证

原件一样(UWO毕业证书）西安大略大学毕业证成绩单留信学历认证

Identify Customer Segments to Create Customer Offers for Each Segment - Appli...

Identify Customer Segments to Create Customer Offers for Each Segment - Appli...

Identify Customer Segments to Create Customer Offers for Each Segment - Appli...

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

SAC 25 Final National, Regional & Local Angel Group Investing Insights 2024 0...

SAC 25 Final National, Regional & Local Angel Group Investing Insights 2024 0...

SAC 25 Final National, Regional & Local Angel Group Investing Insights 2024 0...

Jual Obat Aborsi Bandung (Asli No.1) Wa 082134680322 Klinik Obat Penggugur Ka...

Jual Obat Aborsi Bandung (Asli No.1) Wa 082134680322 Klinik Obat Penggugur Ka...

Jual Obat Aborsi Bandung (Asli No.1) Wa 082134680322 Klinik Obat Penggugur Ka...

obat aborsi Tarakan wa 081336238223 jual obat aborsi cytotec asli di Tarakan9...

obat aborsi Tarakan wa 081336238223 jual obat aborsi cytotec asli di Tarakan9...

obat aborsi Tarakan wa 081336238223 jual obat aborsi cytotec asli di Tarakan9...

Harnessing the Power of GenAI for BI and Reporting.pptx

Harnessing the Power of GenAI for BI and Reporting.pptx

Harnessing the Power of GenAI for BI and Reporting.pptx

Displacement, Velocity, Acceleration, and Second Derivatives

Displacement, Velocity, Acceleration, and Second Derivatives

Displacement, Velocity, Acceleration, and Second Derivatives

DS Lecture-1 about discrete structure .ppt

DS Lecture-1 about discrete structure .ppt

DS Lecture-1 about discrete structure .ppt

如何办理澳洲拉筹伯大学毕业证（LaTrobe毕业证书）成绩单原件一模一样

如何办理澳洲拉筹伯大学毕业证（LaTrobe毕业证书）成绩单原件一模一样

如何办理澳洲拉筹伯大学毕业证（LaTrobe毕业证书）成绩单原件一模一样

Capstone in Interprofessional Informatic // IMPACT OF COVID 19 ON EDUCATION

Capstone in Interprofessional Informatic // IMPACT OF COVID 19 ON EDUCATION

Capstone in Interprofessional Informatic // IMPACT OF COVID 19 ON EDUCATION

ℂall Girls In Navi Mumbai Hire Me Neha 9910780858 Top Class ℂall Girl Serviℂe...

ℂall Girls In Navi Mumbai Hire Me Neha 9910780858 Top Class ℂall Girl Serviℂe...

ℂall Girls In Navi Mumbai Hire Me Neha 9910780858 Top Class ℂall Girl Serviℂe...

DBMS UNIT 5 46 CONTAINS NOTES FOR THE STUDENTS

DBMS UNIT 5 46 CONTAINS NOTES FOR THE STUDENTS

DBMS UNIT 5 46 CONTAINS NOTES FOR THE STUDENTS

Digital Transformation Playbook by Graham Ware

Digital Transformation Playbook by Graham Ware

Digital Transformation Playbook by Graham Ware

Huawei Ransomware Protection Storage Solution Technical Overview Presentation...

Huawei Ransomware Protection Storage Solution Technical Overview Presentation...

Huawei Ransomware Protection Storage Solution Technical Overview Presentation...

Matchmaking Audiences to Content with Lucy Wang

1. Lucy X. Wang, BuzzFeed Matchmaking Audiences to Content #AI8SAIS

2. BuzzFeed 2#AI8SAIS

4. A vast network of social channels • 10+ platforms • 400+ accounts • 1200+ pieces of content published a week 4#AI8SAIS

5. But how do we scale our curation? 5#AI8SAIS

6. By mimicking human curation 6#AI8SAIS

7. 3 use cases for AI 1. Surface relevant content for each channel 2. Surface evergreen content 3. Automate publishing 7#AI8SAIS

8. Case 1: How do we recommend relevant content for each channel? 8#AI8SAIS

9. Content too often slips through the cracks 9#AI8SAIS

10. Training on human curation data 10#AI8SAIS

11. A language-based relevance model 11#AI8SAIS

12. Predicting relevance from titles 12#AI8SAIS

13. Consolidated into one model 13#AI8SAIS

14. Examples 14#AI8SAIS

15. Impact 15#AI8SAIS unlike rate remains stable

16. Case 2: How do we find evergreen content to re-circulate? 16#AI8SAIS

17. Taking advantage of evergreen posts 17#AI8SAIS 2016 55K clicks 2018 36K clicks

18. A model based on content and traffic performance 18#AI8SAIS

19. Not much human curation data 19#AI8SAIS

20. timely evergreen Our labelling proxy 20#AI8SAIS

21. timely? Handling poorly-labeled data 21#AI8SAIS

22. Modeling with uncertainty 22#AI8SAIS !"#$"%&'() = ! +,(.()+ + 1 + 234 ,(.()+

23. Examples 23#AI8SAIS

24. Impact median clicks per post doubled (+108%) with addition of evergreen recommendations 24#AI8SAIS unlike rate remains stable

25. Case 3: How do we automate publishing? 25#AI8SAIS

26. Mixing human and AI curation 26#AI8SAIS

27. Optimal scheduling model 27#AI8SAIS

28. Trying all scheduling times 28#AI8SAIS

29. How do we validate these models? 29#AI8SAIS

30. Limits of AI validation 30#AI8SAIS

31. The loop isn’t complete without humans 31#AI8SAIS

32. Gathering user feedback 32#AI8SAIS

33. Adapting to feedback 33#AI8SAIS

34. Human decisions will always be key 34#AI8SAIS

35. 35#AI8SAIS Human decisions will always be key 19M views 800,000 simultaneous viewers 33M views

36. Thank you! 36#AI8SAIS