Re-imagine Data Monitoring with whylogs and Spark
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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.
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![Data Logs
Model Metadata
Pipeline Metadata
i.e. data profiling
Data profiling refers to the analysis of information [...] in order to clarify the
structure, content, relationships, and derivation rules of the data [Wikipedia]
6
Data monitoring starts with logging](https://image.slidesharecdn.com/297andydang-210616155309/85/Re-imagine-Data-Monitoring-with-whylogs-and-Spark-6-320.jpg)
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Drifting Away: Testing ML Models in ProductionDatabricks Deploying machine learning models has become a relatively frictionless process. However, properly deploying a model with a robust testing and monitoring framework is a vastly more complex task. There is no one-size-fits-all solution when it comes to productionizing ML models, oftentimes requiring custom implementations utilising multiple libraries and tools. There are however, a set of core statistical tests and metrics one should have in place to detect phenomena such as data and concept drift to prevent models from becoming unknowingly stale and detrimental to the business.
Combining our experiences from working with Databricks customers, we do a deep dive on how to test your ML models in production using open source tools such as MLflow, SciPy and statsmodels. You will come away from this talk armed with knowledge of the key tenets for testing both model and data validity in production, along with a generalizable demo which uses MLflow to assist with the reproducibility of this process.
Data Mesh Part 4 Monolith to Mesh
Data Mesh Part 4 Monolith to MeshJeffrey T. Pollock This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
Data Lakehouse, Data Mesh, and Data Fabric (r2)
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse A...
Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse A...Cathrine Wilhelmsen Pipelines and Data Flows: Introduction to Data Integration in Azure Synapse Analytics (Presented at Global Azure Norway on April 16th, 2021)
A Practical Enterprise Feature Store on Delta Lake
A Practical Enterprise Feature Store on Delta LakeDatabricks The feature store is a data architecture concept used to accelerate data science experimentation and harden production ML deployments. Nate Buesgens and Bryan Christian describe a practical approach to building a feature store on Delta Lake at a large financial organization. This implementation has reduced feature engineering “wrangling” time by 75% and has increased the rate of production model delivery by 15x. The approach described focuses on practicality. It is informed by innovative approaches such as Feast, but our primary goal is evolutionary extensions of existing patterns that can be applied to any Delta Lake architecture.
Key Takeaways:
– Understand the key use cases that motivate the feature store from both a data science and engineering perspective.
– Consider edge cases where there may be opportunities for simplification such as “online” predictions.
– Review a typical logical data model for a feature store and how that can be applied to your business domain.
– Consider options for physical storage of the feature store in the Delta Lake.
– Understand common access patterns including metadata-based feature discovery.
Similar to Re-imagine Data Monitoring with whylogs and Spark (20)
MLOps and Data Quality: Deploying Reliable ML Models in Production
MLOps and Data Quality: Deploying Reliable ML Models in ProductionProvectus Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure.
For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).
Agenda
- Data Quality and why it matters
- Challenges and solutions of Data Testing
- Challenges and solutions of Model Testing
- MLOps pipelines and why they matter
- How to expand validation pipelines for Data Quality
SFScon 22 - Fiete Lüer - Heading towards reproducible machine learning resear...
SFScon 22 - Fiete Lüer - Heading towards reproducible machine learning resear...South Tyrol Free Software Conference How 'publish or perish' has failed us and how we can improve the current publication process
While it is crucial that machine learning models are reproducible, the issue is not sufficiently addressed in current research. Models can be irreproducible due to a variety of non-intentional and intentional reasons ranging from time pressure to deliberate fraud. To assure validity of published models, some venues demand public availability of source code or trained models. However, these approaches have shown to be insufficient, for example, because there are often no adequate quality checks on source code and functionality during the publication process.
We discuss the need as well as the challenges of open source research on machine learning. We address frequently occurring problems and offer (also technical) solutions.
These solutions are integrated in a novel open source framework. The applicability of this framework is discussed in the context of data generation and anomaly detection in electrocardiographic data and its specific requirements concerning data samples and model validity. Finally, we present how such frameworks enable more reliable progress in research.
Moving from BI to AI : For decision makers
Moving from BI to AI : For decision makerszekeLabs Technologies This document discusses moving from traditional business intelligence (BI) tools to adopting machine learning. It begins with an overview of common BI workflows and their limitations. It then provides introductions to machine learning, deep learning, and artificial intelligence. The machine learning pipeline is explained along with examples of adopting machine learning in products. Challenges of adopting machine learning are discussed as well as cost optimization strategies. Real world use cases are presented and open source options are mentioned.
C2_W1---.pdf
C2_W1---.pdfHumayun Kabir This document discusses copyright and distribution terms for slides from DeepLearning.AI. Key points:
- The slides are distributed under a Creative Commons license for educational purposes.
- DeepLearning.AI makes the slides available for non-commercial educational use, as long as DeepLearning.AI is cited as the source.
- For full details of the license terms, see the URL provided.
Obfuscating LinkedIn Member Data
Obfuscating LinkedIn Member DataDataWorks Summit Krishnan Raman presented on LinkedIn's data obfuscation pipeline. The pipeline aims to analyze LinkedIn data to improve machine learning models, discover data quickly for analysis, and access data efficiently while complying with privacy regulations. It determines which files contain personally identifiable information (PII) to obfuscate, handles schema evolution, and preserves file names and types. WhereHows is used to track dataset lineage and locations. Obfuscated data is emitted with metrics on job progress captured in timeseries for monitoring the data pipeline. Challenges include unclean data, complex schemas, balancing failures vs dropped rows, and accounting for changing data and schemas. Auditing data, metadata, robust monitoring systems, and re-ob
Model selection and tuning at scale
Model selection and tuning at scaleOwen Zhang This document discusses model selection and tuning at scale using large datasets. It describes using different percentages of a 1TB Criteo click-through dataset to test and tune gradient boosted trees (GBTs) and other models. Testing on small slices found GBT performed best. Tuning GBT on larger slices up to 10% of the data showed tree depth should increase logarithmically with data size. Online learning with VW was also efficient, needing minimal tuning. The document cautions that true model selection and tuning at scale involves starting with larger data samples than GBs to avoid extrapolating from small data.
vodQA Pune (2019) - Testing AI,ML applications
vodQA Pune (2019) - Testing AI,ML applicationsvodQA 1) The document discusses testing machine learning and AI applications. It covers topics like what AI and ML are, how technology is shifting towards them, and where quality assurance fits in.
2) Hands-on activities are described for creating and testing a basic wine-beer classifier and an image classifier using mobile networks.
3) Challenges of testing AI and ML applications include generating training and test data, knowing thresholds, and dealing with data quality issues like overfitting and underfitting. Metrics like accuracy, precision, recall, and error rates are discussed.
AI hype or reality
AI hype or realityAwantik Das The document provides an overview of machine learning and artificial intelligence concepts. It discusses:
1. The machine learning pipeline, including data collection, preprocessing, model training and validation, and deployment. Common machine learning algorithms like decision trees, neural networks, and clustering are also introduced.
2. How artificial intelligence has been adopted across different business domains to automate tasks, gain insights from data, and improve customer experiences. Some challenges to AI adoption are also outlined.
3. The impact of AI on society and the workplace. While AI is predicted to help humans solve problems, some people remain wary of technologies like home health diagnostics or AI-powered education. Responsible development of explainable AI is important.
Reproducibility and experiments management in Machine Learning 
Reproducibility and experiments management in Machine Learning Mikhail Rozhkov Machine Learning becomes more and more common practice in many companies. ML teams size is growing and collaboration goes out of office and personal laptops. The complexity of ML projects leads to adopting distributed team collaboration, cloud based infrastructure and distributed machine learning. Well defined and manageable process for ML experiments becomes a central issue. Practices to apply automated pipelines, models and data set versioning helps to establish a good manageable process in project and provide reproducible results.
This speech helps to start with handling models and datasets versioning using open source tools: DVC, mlflow, Luigi, etc.
Importance of ML Reproducibility & Applications with MLfLow
Importance of ML Reproducibility & Applications with MLfLowDatabricks With data as a valuable currency and the architecture of reliable, scalable Data Lakes and Lakehouses continuing to mature, it is crucial that machine learning training and deployment techniques keep up to realize value. Reproducibility, efficiency, and governance in training and production environments rest on the shoulders of both point in time snapshots of the data and a governing mechanism to regulate, track, and make best use of associated metadata.
This talk will outline the challenges and importance of building and maintaining reproducible, efficient, and governed machine learning solutions as well as posing solutions built on open source technologies – namely Delta Lake for data versioning and MLflow for efficiency and governance.
MLlib and Machine Learning on Spark
MLlib and Machine Learning on SparkPetr Zapletal This document provides an overview of Apache Spark's MLlib machine learning library. It discusses machine learning concepts and terminology, the types of machine learning techniques supported by MLlib like classification, regression, clustering, collaborative filtering and dimensionality reduction. It covers MLlib's algorithms, data types, feature extraction and preprocessing capabilities. It also provides tips for using MLlib such as preparing features, configuring algorithms, caching data, and avoiding overfitting. Finally, it introduces ML Pipelines for constructing machine learning workflows in Spark.
MLOps.pptx
MLOps.pptxsundharakumarkb1 MLOps is the process of taking machine learning models into production and maintaining and monitoring them. It addresses issues like lack of reproducibility, inability to identify new trends, and lack of scalability that can occur without proper processes. The machine learning lifecycle includes scoping a project, collecting and preparing data, developing and evaluating models, deploying models into production, and ongoing monitoring. MLOps aims to operationalize this lifecycle to ensure models can be deployed and updated efficiently and reliably at scale.
Data analytcis-first-steps
Data analytcis-first-stepsShesha R 1) Data analytics involves treating available digital data as a "gold mine" to obtain tangible outputs that can improve business efficiency when applied. Machine learning uses algorithms to correlate parameters in data and improve relationships.
2) The document provides an overview of getting started in data science, covering business objectives, statistical analysis, programming tools like R and Python, and problem-solving approaches like supervised and unsupervised learning.
3) It describes the iterative "rule of seven" process for data science projects, including collecting/preparing data, exploring/analyzing it, transforming features, applying models, evaluating performance, and visualizing results.
20240515 - Chicago PUG - Clustering in PostgreSQL: Because one database serve...
20240515 - Chicago PUG - Clustering in PostgreSQL: Because one database serve...Umair Shahid In the world of database management, high availability and disaster recovery are key considerations for any organization that wants to ensure reliable access to its critical data. Clustering in PostgreSQL is one of the most effective ways to achieve both. In this talk, we will explore the ins and outs of PostgreSQL clustering, including the benefits and challenges of different clustering approaches, and how to set up a highly available and disaster-resilient PostgreSQL cluster.
We will dive into topics such as synchronous vs. asynchronous replication, load balancing, failover, and disaster recovery. We will also touch on some open source tools that are readily available to aid in PostgreSQL cluster management.
20230511 - PGConf Nepal - Clustering in PostgreSQL_ Because one database serv...
20230511 - PGConf Nepal - Clustering in PostgreSQL_ Because one database serv...Umair Shahid This document discusses clustering in PostgreSQL to provide high availability and redundancy. It begins by defining high availability and different "nines of availability". It notes that cloud databases like Amazon RDS only provide three and a half nines of availability, leaving room for hours of downtime per year. The document then discusses different clustering architectures like primary-standby replication, active-active multi-node clusters, and globally distributed clusters. It also covers challenges like split-brain situations, network latency, false alarms, and data inconsistency. Finally, it recommends open-source tools like repmgr, pgpool-II and Patroni that can help implement PostgreSQL clustering.
KNOLX_Data_preprocessing
KNOLX_Data_preprocessingKnoldus Inc. The document discusses the key steps involved in data pre-processing for machine learning:
1. Data cleaning involves removing noise from data by handling missing values, smoothing outliers, and resolving inconsistencies.
2. Data transformation strategies include data aggregation, feature scaling, normalization, and feature selection to prepare the data for analysis.
3. Data reduction techniques like dimensionality reduction and sampling are used to reduce large datasets size by removing redundant features or clustering data while maintaining most of the information.
(Faiz) MachineLearning(ppt).pptx
(Faiz) MachineLearning(ppt).pptxFaiz430036 Machine learning (ML) is a branch of artificial intelligence that uses computing systems to analyze data, recognize patterns, and learn from that data. ML systems can learn and improve from experience over time as they are exposed to more data. There are two main types of ML: supervised learning, where the model learns from labeled examples with known outputs, and unsupervised learning, where the model finds hidden patterns in unlabeled data. The ML process involves defining objectives, preparing data, building models, evaluating models, and deploying the best performing model. Data preparation is an important step that can impact model performance, such as transforming, normalizing, and engineering features from the data.
The Critical Missing Component in the Production ML Stack
The Critical Missing Component in the Production ML StackDatabricks The day the ML application is deployed to production and begins facing the real world is the best and the worst day in the life of the model builder. The joy of seeing accurate predictions is quickly overshadowed by a myriad of operational challenges. Debugging, troubleshooting & monitoring takes over the majority of their day, leaving little time for model building. In DevOps, software operations are taken to a level of an art. Sophisticated tools enable engineers to quickly identify and resolve issues, continuously improving software stability and robustness. In the ML world, operations are still largely a manual process that involves Jupyter notebooks and shell scripts. One of the cornerstones of the DevOps toolchain is logging. Traces and metrics are built on top of logs enabling monitoring and feedback loops. What does logging look like in an ML system?
In this talk we will demonstrate how to enable data logging for an AI application using MLflow in a matter of minutes. We will discuss how something so simple enables testing, monitoring and debugging in an AI application that handles TBs of data and runs in real-time. Attendees will leave the talk equipped with tools and best practices to supercharge MLOps in their team.
10 ways to stumble with big data
10 ways to stumble with big dataLars Albertsson Many companies have data with great potential. There are many ways to go wrong with Big Data projects, however; the difference between a successful and a failed project can be huge, both in cost and the return of investment. In this talk. we will describe the most common pitfalls, and how to avoid them. You will learn to:
- Be aware of the existing risk factors in your organisation that may cause a data project to fail.
- Learn how to recognise the most common and costly causes of project failure.
- Learn how to avoid or mitigate project problems in order to ensure return of investment in a lean manner.
Production-Ready BIG ML Workflows - from zero to hero
Production-Ready BIG ML Workflows - from zero to heroDaniel Marcous Data science isn't an easy task to pull of.
You start with exploring data and experimenting with models.
Finally, you find some amazing insight!
What now?
How do you transform a little experiment to a production ready workflow? Better yet, how do you scale it from a small sample in R/Python to TBs of production data?
Building a BIG ML Workflow - from zero to hero, is about the work process you need to take in order to have a production ready workflow up and running.
Covering :
* Small - Medium experimentation (R)
* Big data implementation (Spark Mllib /+ pipeline)
* Setting Metrics and checks in place
* Ad hoc querying and exploring your results (Zeppelin)
* Pain points & Lessons learned the hard way (is there any other way?)
SFScon 22 - Fiete Lüer - Heading towards reproducible machine learning resear...
SFScon 22 - Fiete Lüer - Heading towards reproducible machine learning resear...South Tyrol Free Software Conference
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More from Databricks (20)
Data Lakehouse Symposium | Day 1 | Part 1
Data Lakehouse Symposium | Day 1 | Part 1Databricks 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 2Databricks 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 2Databricks 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 4Databricks The document discusses the challenges of modern data, analytics, and AI workloads. Most enterprises struggle with siloed data systems that make integration and productivity difficult. The future of data lies with a data lakehouse platform that can unify data engineering, analytics, data warehousing, and machine learning workloads on a single open platform. The Databricks Lakehouse platform aims to address these challenges with its open data lake approach and capabilities for data engineering, SQL analytics, governance, and machine learning.
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 HadoopDatabricks 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
Democratizing Data Quality Through a Centralized PlatformDatabricks 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
Why APM Is Not the Same As ML Monitoring
Why APM Is Not the Same As ML MonitoringDatabricks 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.
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 FixDatabricks 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
Stage Level Scheduling Improving Big Data and AI IntegrationDatabricks 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
Simplify Data Conversion from Spark to TensorFlow and PyTorchDatabricks 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
Scaling your Data Pipelines with Apache Spark on KubernetesDatabricks 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
Scaling and Unifying SciKit Learn and Apache Spark PipelinesDatabricks 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
Sawtooth Windows for Feature AggregationsDatabricks 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
Redis + Apache Spark = Swiss Army Knife Meets Kitchen SinkDatabricks 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
Raven: End-to-end Optimization of ML Prediction QueriesDatabricks 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.
Processing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache SparkDatabricks 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.
Massive Data Processing in Adobe Using Delta Lake
Massive Data Processing in Adobe Using Delta LakeDatabricks 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
Machine Learning CI/CD for Email Attack DetectionDatabricks 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.
Jeeves Grows Up: An AI Chatbot for Performance and Quality
Jeeves Grows Up: An AI Chatbot for Performance and QualityDatabricks 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.
Intuitive & Scalable Hyperparameter Tuning with Apache Spark + Fugue
Intuitive & Scalable Hyperparameter Tuning with Apache Spark + FugueDatabricks This presentation introduces Tune and Fugue, frameworks for intuitive and scalable hyperparameter optimization (HPO). Tune supports both non-iterative and iterative HPO problems. For non-iterative problems, Tune supports grid search, random search, and Bayesian optimization. For iterative problems, Tune generalizes algorithms like Hyperband and Asynchronous Successive Halving. Tune allows tuning models both locally and in a distributed manner without code changes. The presentation demonstrates Tune's capabilities through examples tuning Scikit-Learn and Keras models. The goal of Tune and Fugue is to make HPO development easy, testable, and scalable.
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- 1. Re-imagine Data Monitoring with whylogs and Apache Spark Andy Dang Co-Founder & Lead Engineer, WhyLabs
- 2. Outline ML Data Challenges How traditional data analysis techniques fail ML data pipelines Lightweight Profiling for Big ML Data Profiling techniques for detecting data quality problems The Open Source whylogs Library Building the standard for data logging 2
- 3. Source: Google Cloud AI 3 ML Lifecycle
- 4. Issues encountered in production (small sample)... ...or it simply doesn’t work, and nobody know why... ● Experiment/production environment mismatch ● Wrong model version deployed ● Underprovisioned hardware ● Inappropriate hardware ● Latency/SLA issues ● Data permissions misconfigured ● Untracked changes broke prod ● Traffic sent to the wrong model ● Computational instability ● Customers gaming the model (adversarial attacks) ● PII data exposed ● Expected accuracy doesn’t materialize ● Pre-processing mismatch in experiments vs. production ● Retrained on faulty data ● Accuracy improves on one segment, regresses in others ● Outliers predicted incorrectly ● Bias identified ● Correlation with protected features ● Overfitting on training/test ● Surge in missing values ● Surge in duplicates ● Poor performance on new categories ● Poor performance on new customer segments ● Poor performance on outliers ● Data quality issues affect accuracy ● Production data doesn’t match test/training ● Accuracy is decaying over time ● Data drift in inputs ● Concept drift in outputs ● Extreme predictions for out of distribution data ● Model not generalizing on new data / new segments ● Major customer behavior shift 4
- 5. Issues encountered in production (small sample)... issues caused by data ● Experiment/production environment mismatch ● Wrong model version deployed ● Underprovisioned hardware ● Inappropriate hardware ● Latency/SLA issues ● Data permissions misconfigured ● Untracked changes broke prod ● Traffic sent to the wrong model ● Computational instability ● Customers gaming the model (adversarial attacks) ● PII data exposed ● Expected accuracy doesn’t materialize ● Pre-processing mismatch in experiments vs. production ● Retrained on faulty data ● Accuracy improves on one segment, regresses in others ● Outliers predicted incorrectly ● Bias identified ● Correlation with protected features ● Overfitting on training/test ● Surge in missing values ● Surge in duplicates ● Poor performance on new categories ● Poor performance on new customer segments ● Poor performance on outliers ● Data quality issues affect accuracy ● Production data doesn’t match test/training ● Accuracy is decaying over time ● Data drift in inputs ● Concept drift in outputs ● Extreme predictions for out of distribution data ● Model not generalizing on new data / new segments ● Major customer behavior shift 5
- 6. Data Logs Model Metadata Pipeline Metadata i.e. data profiling Data profiling refers to the analysis of information [...] in order to clarify the structure, content, relationships, and derivation rules of the data [Wikipedia] 6 Data monitoring starts with logging
- 7. 7 Sampling Profiling Pros ● Easy to build ● Little upfront design ● Log & raw data analysis identical ● Scalable & lightweight ● Flexible & configurable ● Rare events and outlier-dependent metrics ● Directly interpretable results Cons ● I/O & storage ● Noisy ● Requires statistical analysis ● Rare events & outliers ● Min/max, unique values, etc ● Data dependent output format ● No existing widespread solutions ● Mathematical & engineering challenges Data logs: sampling vs. profiling
- 8. 8 Data logs: must be accurate Median: errors in the estimate of the median for sampling vs profiling for various distributions. Mean absolute error and mean relative (fractional) absolute error are shown.
- 9. 9 Data logs: must be scalable Dataset Size # of entries # of features Memory consumption Output size Lending Club 1.6G 2.2M 151 14MB 7.4MB NYC Tickets 1.9G 10.8 43 14MB 2.3MB Pain pills 75GB 178M 42 15MB 2MB
- 10. 10 Logging ML data at scale Four key paradigms: ● Approximations rather than exact results ● Lightweight ● Additive ● Batch and streaming support profile: collection of lightweight metrics that provide these properties
- 11. Lightweight Old Approach 11 process process process process Data Warehouse/ Data Lake Processing Engine New Approach process profiling process profiling process profiling process profiling Profile Store Analysis Only feasible if: ● Profiling is fast ● Profiling is not memory intensive
- 12. Additive 12 dataset 1 dataset 2 dataset 3 sort (shuffle) reduce step Median dataset 1 profile 1 dataset 2 profile 2 dataset 3 profile 3 add(profile1, 2, 3) Estimated Median
- 13. Batch and streaming support 13 partition 1 profiling partition 2 profiling partition 3 profiling partition n profiling Spark/Hive Query Engine No shuffle! day 0 profiling day 1 profiling day 2 profiling day 3 profiling ... ... sum(profiles)
- 14. Approximate Statistics ● Using Stochastic Streaming Algorithms ○ Model the problem as a stochastic process ○ Apache Datasketches is the open source implementation ● Statistics that we focus on at the moment: ○ Histograms ○ Frequent items ○ Cardinality 14
- 15. whylogs: The Data Logging Library ● Multi-language support: Python + Java ● Support both data engineering and data science workflows ● Extensibility: image support. Text, video, audio & embeddings support to come ● Growing integration list: 15
- 16. 16 whylogs: Python ● A few lines of code to start logging ● Integrate with popular data science libraries ● Out of the box visualization utilities
- 17. 17 whylogs in Apache Spark Data Lake col1 , col2 , …, coln partition 1 partition 2 partition k profile profile merge ( profile1 , profile2 …, profilek ) global profile Schema Metadata Sketches profile Metrics
- 20. 20 Catch distribution drift in a few lines of code
- 21. 21 Scalable monitoring at input feature granularity
- 22. Monitoring layer for ML applications 22
- 24. [email protected] @andy_dng 24 bit.ly/whylogs Help build the open standard for data logging! Thank you!