Semantic Image Logging Using Approximate Statistics & MLflow

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As organizations launch complex multi-modal models into human-facing applications, data governance becomes both increasingly important, and difficult. Specifically, monitoring the underlying ML models for accuracy and reliability becomes a critical component of any data governance system. When complex data, such as image, text and video, is involved, monitoring model performance is particularly problematic given the lack of semantic information. In industries such as health care and automotive, fail-safes are needed for compliant performance and safety but access to validation data is in short supply, or in some cases, completely absent. However, to date, there have been no widely accessible approaches for monitoring semantic information in a performant manner. In this talk, we will provide an overview of approximate statistical methods, how they can be used for monitoring, along with debugging data pipelines for detecting concept drift and out-of-distribution data in semantic-full data, such as images. We will walk through an open source library, whylogs, which combines Apache Spark and novel approaches to semantic data sketching. We will conclude with practical examples equipping ML practitioners with monitoring tools for computer vision, and semantic-full models.

Daten & Analysen

Semantic image logging with
approximate statistical
methods & MLflow
Leandro G. Almeida, PhD

Four steps to image logging
• Scaling to real-world datasets with
approximate statistics
• Logging in ML applications
• Logging semantic image data

Approximate Statistics
• approximate distribution

• Quantiles ( min, max, .. )

• Std-dev

• Count

• Type counts

• Top k frequent items

Constant memory footprint!

whylogs Minimal Setup
Start logging in 4 lines of code
github.com/whylabs/whylogs

Three steps to image logging
• Logging in ML applications
• Logging semantic image data
• Scaling to real-world datasets with
approximate statistics
• Why (to) Log ?
• How (to) Log ?
• What (to) Log ?

Why (to) Log ? Testing doesn’t stop at the test set.

Why (to) Log ?
Monitoring Deployments
• Data drift
• Model drift
• Concept drift
• Domain shift
• Head to Tail drift

Why (to) Log ?
Monitoring Deployments
• Data drift
• Model drift
• Concept drift
• Domain shift
• Head to Tail drift
• Input Data is inherently different
• Feedback Loop where model affects user behavior
• Target Properties change over time
• Biased Dataset
• Tasks based on the relevance of outliers

What (to) Log ?
• Inputs/Outputs
• Task Metrics
• Perfomance Metrics

What (to) Log ?
• Meta Data
• Device
• Encoding
• Raw Resolution
• Aspect Ratio
• Features distributions
• Quality Based
• Engineered
• Outputs
• Semantic
• Inputs/Outputs
• Task Metrics
• Perfomance Metrics

What (to) Log ? • File Meta Data
• Device
• Encoding
• Raw Resolution
• Aspect Ratio
• Inputs/Outputs
• Task Metrics
• Perfomance Metrics

What (to) Log ?
• Features distributions
• IQA
• Engineered
• Learned
• Outputs
• Embeddings

What (to) Log ?
• Features distributions
• IQA
• Engineered
• Learned
• Outputs
• Embeddings
Reference Set
(Baseline)
Current Image or Set

What (to) Log ?
• Features distributions
• IQA
• Engineered
• Learned
• Outputs (image based)
• Embeddings
Current Image or Set
Reference Set
(Baseline)

What (to) Log ?
Current Image or Set
Reference Set
(Baseline)

What (to) Log ?
Current Image or Set
Pair Distance dij: over entire dataset or per cluster Distance from each cluster center (closest concentre embedding)
C1
C2
C3
Cn
C4
…

What (to) Log ?
• Features distributions
• IQA
• Engineered
• Learned
• Outputs (non images)
• Embeddings
Current Image or Set

Four Steps
• Scaling to real-world datasets with
approximate statistics
• Approximate Statistics
• Logging in ML applications
• Logging semantic image data

23
Try today & contribute
bit.ly/whylogs

Thank you!
leandro@whylabs.ai
@lalmei
24
bit.ly/whylogs

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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

Databricks

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

Databricks

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

Databricks

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

Databricks

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

Databricks

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

Databricks

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

Databricks

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

Databricks

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

Databricks

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

Databricks

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.

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Digital advertising, or paid media, encompasses the strategic deployment of online advertisements to reach target audiences efficiently and effectively. This includes any digital platform that supports advertising to deliver unique messages for any objective. Understanding the mechanics of digital advertising platforms, along with insights into audience behaviors and preferences, allows marketers to optimize their ad spend and achieve significant engagement and conversion rates. This lecture is for Advanced Digital & Social Media Strategy (MGMTX 466.05) at UCLA Extension.

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1. Semantic image logging with approximate statistical methods & MLflow Leandro G. Almeida, PhD

2. Four steps to image logging • Scaling to real-world datasets with approximate statistics • Logging in ML applications • Logging semantic image data

3. Approximate Statistics • approximate distribution • Quantiles ( min, max, .. ) • Std-dev • Count • Type counts • Top k frequent items Constant memory footprint!

4. whylogs Minimal Setup Start logging in 4 lines of code github.com/whylabs/whylogs

5. Even easier with

6. 6 Spark-powered scaling

7. Three steps to image logging • Logging in ML applications • Logging semantic image data • Scaling to real-world datasets with approximate statistics • Why (to) Log ? • How (to) Log ? • What (to) Log ?

8. Why (to) Log ? Testing doesn’t stop at the test set.

9. Why (to) Log ? Monitoring Deployments • Data drift • Model drift • Concept drift • Domain shift • Head to Tail drift

10. Why (to) Log ? Monitoring Deployments • Data drift • Model drift • Concept drift • Domain shift • Head to Tail drift • Input Data is inherently different • Feedback Loop where model affects user behavior • Target Properties change over time • Biased Dataset • Tasks based on the relevance of outliers

11. What (to) Log ?

12. What (to) Log ? • Inputs/Outputs • Task Metrics • Perfomance Metrics

13. What (to) Log ? • Meta Data • Device • Encoding • Raw Resolution • Aspect Ratio • Features distributions • Quality Based • Engineered • Outputs • Semantic • Inputs/Outputs • Task Metrics • Perfomance Metrics

14. What (to) Log ? • File Meta Data • Device • Encoding • Raw Resolution • Aspect Ratio • Inputs/Outputs • Task Metrics • Perfomance Metrics

15. What (to) Log ? • Features distributions • IQA • Engineered • Learned • Outputs • Embeddings

16. What (to) Log ? • Features distributions • IQA • Engineered • Learned • Outputs • Embeddings Reference Set (Baseline) Current Image or Set

17. What (to) Log ? • Features distributions • IQA • Engineered • Learned • Outputs (image based) • Embeddings Current Image or Set Reference Set (Baseline)

18. What (to) Log ? Current Image or Set Reference Set (Baseline)

19. What (to) Log ? Current Image or Set Pair Distance dij: over entire dataset or per cluster Distance from each cluster center (closest concentre embedding) C1 C2 C3 Cn C4 …

20. What (to) Log ? • Features distributions • IQA • Engineered • Learned • Outputs (non images) • Embeddings Current Image or Set

21. Four Steps • Scaling to real-world datasets with approximate statistics • Approximate Statistics • Logging in ML applications • Logging semantic image data

22. 22 Spark-powered scaling

23. 23 Try today & contribute bit.ly/whylogs

24. Thank you! leandro@whylabs.ai @lalmei 24 bit.ly/whylogs

Semantic Image Logging Using Approximate Statistics & MLflow

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