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Depop Case Study

2020

Depop offers an alternative to shopping through its marketplace for unique fashion. It turned to AWS after reaching the limits of its existing PaaS technology. AWS enables Depop to quickly iterate, deploy, and scale out new features, with a focus on machine learning.

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No matter what you want to do, there is an AWS service for it.”

Robert Erdin
Director of Applications and Services, Depop

Depop Fashions a Data-Driven Business

Depop bills itself as a community-driven social shopping experience where the next generation buys, sells, and discovers unique fashion. Depop aims to enable the fashion industry to become more sustainable and purpose-driven by offering a circular-first alternative to shopping new.

To support growth and the new capabilities it would need in the future, Depop went all-in on AWS. It has since evolved from an early-stage startup to a late-stage success story with 25 million users (90 percent of its active users are under the age of 25) and year-on-year customer growth of 130 percent across its key markets of the US, UK, and Australia.

Reaching the Limits of PaaS

Depop moved to AWS around two-and-a-half years ago, as it was hitting the limits of its existing Platform-as-a-Service (PaaS) technology. “With our PaaS provider, we were facing several challenges, such as scalability limits, lack of flexibility in how we scale our applications, and constraints in the way solutions are built due to a fully managed, but limited, set of available capabilities,” says Depop chief technology and data officer Remo Gettini.

Robert Erdin, Depop director of applications and services, adds that the company also needed a more cost-effective infrastructure over which it had more control.

Making the Shift to AWS

Depop performed the migration itself, with AWS solution architects helping to overcome challenges. Those challenges included understanding the various cost-saving measures and how to consistently monitor and apply them, as well as the maturity of different AWS services.

Another challenge was finding the right balance between providing reusable, secure, and easy-to-use abstractions for application developers, and granting developers direct access to provisioning infrastructure.

Supporting Growth

Thanks to using AWS, the company has increased the number of teams able to simultaneously work on the Depop application. That number has grown from two to eight, with more than 10 teams possible soon. This has greatly improved Depop’s ability to develop, test, and deploy new services. Clemence J. Burnichon, Depop head of data science and machine learning (ML), says AWS has also given her team the flexibility to expand from a two-instance cluster to 25 instances.

AWS also supports Depop’s rapid market growth. The Amazon CloudFront content delivery network, integrated with Amazon Simple Storage Service (Amazon S3), will be crucial to facilitating this expansion by supporting the distribution of images and videos.

Machine Learning at the Core

Depop’s services are increasingly driven by ML with the company making extensive use of the AWS ML service for its ML workloads. One of the main technology challenges for Depop is dealing with an ever-growing inventory in which no two items are the same. The company relies on the Amazon S3-based data lake solution to manage its vast inventory of 25 million items and transactions, employing Amazon Kinesis Data Firehose to stream data, with some use of Amazon Managed Streaming for Apache Kafka (Amazon MSK).

Combined with visual recognition technology, ML algorithms categorize clothing items held in the data lake in different ways, including by size, color, and brand, supporting Depop’s Personal Shopper search and recommendation service.

Depop plans to use Amazon Elasticsearch Service on its mobile app to take advantage of the categorized data held in the data lake. It will provide more granular control to search and enable more ML algorithms to be added over time.

Other Amazon technologies used in conjunction with the data lake include the Amazon Redshift data warehouse for creating cleaner, packaged versions of data, and the Amazon Athena interactive query service to provide rapid access to data.

As a result of having these AWS tools at its disposal, Depop’s ML team is also able to quickly iterate new deep-learning models. According to Burnichon, the ML team currently has around 30 ML models in production, processing a combined 1.5 million messages per hour.

These models are supported by Amazon SageMaker and Amazon EMR for indexing and relevance, while extract, transform, and load (ETL) processes are provided via AWS Glue.

Democratized Access to Infrastructure

AWS has allowed Depop to move toward “more granular backend services, which allowed us to significantly scale up our engineering team and work on more features concurrently,” explains Gettini.

Depop is using Amazon Elastic Kubernetes Service (Amazon EKS) to provide serverless compute for containers that support its mobile app, giving the development team the ability to deploy new production services within a day. Erdin says AWS has enabled a “democratization of access to infrastructure,” allowing the development teams to easily and cost-effectively try out new things in a safe way, without incurring technical debt. “No matter what you want to do, there is an AWS service for it,” he says.

Using AWS also ensures that successful services or applications are already in the right environment to go into production. According to Erdin, Depop has seen more than 100 services go live.

Success through Collaboration

Another major benefit is the access that Depop has to AWS specialists. Erdin cites the example of being able to unblock ideas by bouncing questions off of AWS solution architects. AWS also provides Depop with frequent training, from entry-level sessions about AWS and cloud to deep dives into specific technologies.

For Gettini, Depop’s biggest achievement with AWS is being able “to support a product and engineering organization of over 100 people in nine cross-functional teams with only a handful of engineers dedicated to maintaining infrastructure.”

“Comparing this to the beginning of my career almost 30 years ago, it is still mind-blowing.”


About Depop

UK-based Depop bills itself as a community-driven social shopping experience where the next generation buys, sells and discovers unique fashion. Depop aims to enable the fashion industry to become more sustainable and purpose-driven by offering a circular-first alternative to shopping new.

Benefits of AWS

  • Quickly iterate, deploy, and scale out features
  • Easily create and integrate machine learning capabilities
  • Focus on developing customer services, rather than managing infrastructure
  • Scale up engineering team to work on more features concurrently
  • Democratize access to infrastructure
  • Scale business to expand into new markets
 

AWS Services Used

Data Lake on AWS

The AWS Cloud provides many of the building blocks required to help customers implement a secure, flexible, and cost-effective data lake. These include AWS managed services that help ingest, store, find, process, and analyze both structured and unstructured data.

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Amazon SageMaker

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly.

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Amazon EMR

Amazon EMR is the industry-leading cloud big data platform for processing vast amounts of data using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto.

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Amazon Elastic Kubernetes Service

Amazon Elastic Kubernetes Service (Amazon EKS) is a fully managed Kubernetes service. Customers such as Intel, Snap, Intuit, GoDaddy, and Autodesk trust EKS to run their most sensitive and mission critical applications because of its security, reliability, and scalability.

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Amazon Kinesis Data Firehose

Amazon Kinesis Data Firehose is the easiest way to reliably load streaming data into data lakes, data stores, and analytics services.

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Managed Streaming for Apache Kafka (MSK)

Amazon MSK is a fully managed service that makes it easy for you to build and run applications that use Apache Kafka to process streaming data.

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Get Started

Companies of all sizes across all industries are transforming their businesses every day using AWS. Learn how to implement a secure, flexible, and cost-effective data lake on AWS and start your own AWS Cloud journey today.