How to Build a ML Platform Efficiently Using Open-Source

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Fast-growing startups usually face a common set of challenges when employing machine learning. Data scientists are expected to work on new products and develop new models as well as iterate on existing ones. Once in production, models should be continuously monitored and regularly maintained as the infrastructure evolves. Before too long, data scientists end up spending most of their time doing maintenance and firefighting of existing models instead of creating new ones. At GetYourGuide, we faced these challenges and decided to think about machine learning development holistically, which led us to our machine learning platform. Our platform uses MLflow to keep track of our machine learning life-cycle and ease the development experience. To integrate our models into our production environment, we also need to deal with additional requirements like API specification, SLOs and monitoring. To empower our data scientists, we have built a templating system that takes care of the heavy lifting of going to production, leveraging software engineering tools and ML-specific ones like BentoML. In this talk we will present: – Our previous approaches for deploying models and their tradeoffs – Our data science and platform principles – The main functionalities of our platform – A live demo to create a new service – Our learnings in the process

Data & Analytics

How to Build a ML
Platform Efficiently
Using Open-Source
Jean Carlo Machado
Theodore Meynard
GetYourGuide 1

Agenda
▪ Introduction
▪ ML at GetYourGuide
▪ Before the Platform
▪ ML Platform
▪ Demo
▪ Final words
2

Who Are We
Theo: Senior Data Scientist
Jean: Senior Software Engineer
3
theodore.meynard@getyourguide.com
jean.machado@getyourguide.com

We’ve built the world’s largest
marketplace for travel activities…
Millions of travelers use
GetYourGuide every year
We facilitate the transaction We offer more than 40,000
activities worldwide
Connecting
customers...
...to suppliers
around the world
5

Amsterdam
:
Canal
Cruise
Other Data Products
We also use ML for:
• Demand forecasting
• Inventory labeling
⇒ 20+ ML projects distributed in 2
teams + delivered models to other
teams to maintain
10

Data Product Principles
We follow clean code principles
PoCs are temporary
We build solid, resilient deployment processes
Know our models health at every point in time
Quality,
Testing &
Monitoring
We integrate into the engineering
ecosystem and leverage
open-source
Data workflows are efficient and
cost-effective
We take reproducibility and
modularity seriously
Engineering
We Promote the Data Product
mindset
We deeply integrate with data
stakeholders
Stakeholder
Engagement
We actively manage the unknowns in our
planning
Data analytics dynamically informs our
project plans
Workflow
Customer and business value over
fancy solutions
Exploration is one of our goals
Strategy
We value small iterations on
existing models
We value explainability over pure
accuracy
Performance is proven online
Model
Our principles explained 11

How We Started
Pros
● Widely used by ML practitioners
● Good to start new projects &
prototype
● Great visualization
Cons
● No proper version control
● No code reuse
● No automatic testing
13

A Major Improvement
Pros
● Tests included in library
● Version control with code review
● Maintainable projects
Cons
● No CI/CD
● No model tracking
14

From Amsterdam:
Volendam, Marken and
Windmills
ML Platform Key Features
● CI/CD
● Model Tracking
● Batch & Online inference
16

ML Platform Principles
• Maximize data scientist’s model
ownership
• Reproducible Machine Learning
• Reuse most of our existing
infrastructure
• Build incrementally
• Use open-source and open
standards
17

From MLﬂow to BentoML Example
import mlflow
from iris_classifier import IrisClassifier
# Load mlflow model
mlflow_model = mlflow.sklearn.load_model(model_uri)
# Create a iris classifier service instance
iris_classifier_service = IrisClassifier()
# Pack the newly trained model artifact
iris_classifier_service.pack("model", mlflow_model)
# Save the prediction service for model serving
saved_path = iris_classifier_service.save()
from bentoml import env, artifacts, api, BentoService
from bentoml.adapters import DataframeInput
from bentoml.frameworks.sklearn import SklearnModelArtifact
@artifacts([SklearnModelArtifact("model")])
class IrisClassifier(BentoService):
"""A minimum prediction service"""
@api(input=DataframeInput())
def predict(self, df: pd.DataFrame):
"""An inference API named `predict`"""
return self.artifacts.model.predict(df)
iris_classiﬁer.py
mlﬂow2bentoml.py
24

● The integration with the existing
architecture fosters proactive
collaboration
● The tool space is very new making
the exploration vital but time
consuming
● A design review is necessary to align
everyone
Learnings
27

Amsterdam:
Moco Museum
Conclusion
● As we grew, we needed to reﬁne our
Data science process
● Software engineering good practices
+ special twist for ML
● Our platform helps Data Scientists to
○ Build faster
○ Deploy safer
○ Document automatically
28

Feedback
Your feedback is important to us.
Don’t forget to rate and review the sessions.
29

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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

Databricks

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

Databricks

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

Databricks

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

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

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

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

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Stage Level Scheduling Improving Big Data and AI Integration

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Scaling your Data Pipelines with Apache Spark on Kubernetes

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Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

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Webinar One View, Multiple Systems No-Code Integration of Salesforce and ERPs

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Webinar One View, Multiple Systems No-Code Integration of Salesforce and ERPs

How to Build a ML Platform Efficiently Using Open-Source

1. How to Build a ML Platform Efficiently Using Open-Source Jean Carlo Machado Theodore Meynard GetYourGuide 1

2. Agenda ▪ Introduction ▪ ML at GetYourGuide ▪ Before the Platform ▪ ML Platform ▪ Demo ▪ Final words 2

3. Who Are We Theo: Senior Data Scientist Jean: Senior Software Engineer 3 theodore.meynard@getyourguide.com jean.machado@getyourguide.com

4. Introduction 4

5. We’ve built the world’s largest marketplace for travel activities… Millions of travelers use GetYourGuide every year We facilitate the transaction We offer more than 40,000 activities worldwide Connecting customers... ...to suppliers around the world 5

6. ML at GetYourGuide 6

7. Data Product: Ranking Service 7

8. Data Product: Recommendation Panels 8

9. Data Product: Paid Search 9

10. Amsterdam : Canal Cruise Other Data Products We also use ML for: • Demand forecasting • Inventory labeling ⇒ 20+ ML projects distributed in 2 teams + delivered models to other teams to maintain 10

11. Data Product Principles We follow clean code principles PoCs are temporary We build solid, resilient deployment processes Know our models health at every point in time Quality, Testing & Monitoring We integrate into the engineering ecosystem and leverage open-source Data workflows are efficient and cost-effective We take reproducibility and modularity seriously Engineering We Promote the Data Product mindset We deeply integrate with data stakeholders Stakeholder Engagement We actively manage the unknowns in our planning Data analytics dynamically informs our project plans Workflow Customer and business value over fancy solutions Exploration is one of our goals Strategy We value small iterations on existing models We value explainability over pure accuracy Performance is proven online Model Our principles explained 11

12. Before the platform 12

13. How We Started Pros ● Widely used by ML practitioners ● Good to start new projects & prototype ● Great visualization Cons ● No proper version control ● No code reuse ● No automatic testing 13

14. A Major Improvement Pros ● Tests included in library ● Version control with code review ● Maintainable projects Cons ● No CI/CD ● No model tracking 14

15. ML Platform 15

16. From Amsterdam: Volendam, Marken and Windmills ML Platform Key Features ● CI/CD ● Model Tracking ● Batch & Online inference 16

17. ML Platform Principles • Maximize data scientist’s model ownership • Reproducible Machine Learning • Reuse most of our existing infrastructure • Build incrementally • Use open-source and open standards 17

18. Our Current Workﬂow 18

19. ML CI/CD 19

20. Training Path 20

21. Batch Inference Path 21

22. Online Inference Path 22

23. Online Inference With BentoML 23

24. From MLflow to BentoML Example import mlflow from iris_classifier import IrisClassifier # Load mlflow model mlflow_model = mlflow.sklearn.load_model(model_uri) # Create a iris classifier service instance iris_classifier_service = IrisClassifier() # Pack the newly trained model artifact iris_classifier_service.pack("model", mlflow_model) # Save the prediction service for model serving saved_path = iris_classifier_service.save() from bentoml import env, artifacts, api, BentoService from bentoml.adapters import DataframeInput from bentoml.frameworks.sklearn import SklearnModelArtifact @artifacts([SklearnModelArtifact("model")]) class IrisClassifier(BentoService): """A minimum prediction service""" @api(input=DataframeInput()) def predict(self, df: pd.DataFrame): """An inference API named `predict`""" return self.artifacts.model.predict(df) iris_classifier.py mlflow2bentoml.py 24

25. Demo 25

26. Final remarks 26

27. ● The integration with the existing architecture fosters proactive collaboration ● The tool space is very new making the exploration vital but time consuming ● A design review is necessary to align everyone Learnings 27

28. Amsterdam: Moco Museum Conclusion ● As we grew, we needed to reﬁne our Data science process ● Software engineering good practices + special twist for ML ● Our platform helps Data Scientists to ○ Build faster ○ Deploy safer ○ Document automatically 28

29. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions. 29

How to Build a ML Platform Efficiently Using Open-Source

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