How LinkedIn Uses Python

The article discusses how LinkedIn developed the Contextual Agent Playbooks & Tools (CAPT) to enhance AI coding agents with organizational context, enabling them to better assist engineers in their...

The article discusses LinkedIn's modernization of its Static Application Security Testing (SAST) capabilities to enhance security for its members.

The article discusses how LinkedIn utilized Jupyter Notebooks to create collaborative prompt engineering playgrounds, enhancing the development of AI-powered features like AccountIQ.

The article discusses Liger-Kernel, an open-source library designed to enhance GPU efficiency for training large language models (LLMs).

The article discusses LinkedIn's transition to Azure Linux as its primary operating system, detailing the motivations, challenges, and strategies involved in the migration.

GrafLI is a cloud-native monitoring and visualization platform developed by LinkedIn to enhance the developer experience and increase engineering velocity in Azure environments.

The article emphasizes the importance of representation and diversity in engineering, particularly in the context of developing AI technologies.

The article discusses the open sourcing of FlyteInteractive, a tool developed by LinkedIn to enhance machine learning (ML) development productivity.

The article discusses LinkedIn's innovative use of Apache Beam for real-time streaming processing, handling over 4 trillion events daily across more than 3,000 pipelines.

The article explores Javier's transition from a music career to data science, highlighting the intersection of math and music in his journey.

The article discusses Costwiz, a tool developed by LinkedIn to optimize cloud costs on Azure by monitoring resource utilization and providing actionable insights.

The article discusses the open-sourcing of AvroTensorDataset, a TensorFlow dataset designed for efficiently processing Avro data.

The article discusses Metal as a Service (MaaS), an internal tool developed by LinkedIn to manage server lifecycles at scale.

The article discusses how LinkedIn scaled its Salt infrastructure to support its growing needs for remote execution jobs, achieving a tenfold increase in job capacity and improved reliability.

LinkedIn has integrated Google Protocol Buffers (Protobuf) with Rest. li to enhance microservices performance, achieving significant reductions in latency and improvements in resource utilization.

The article discusses LinkedIn's approach to measuring developer productivity and happiness through the development of the Developer Insights Hub (iHub).

The article discusses Vasundhara's journey as an AI engineer at LinkedIn, highlighting her transition from Seoul to Dublin during the pandemic and her work on machine learning algorithms for Linked...

The article shares the career journey of Deepti, a biomedical engineer turned data scientist at LinkedIn, highlighting her transitions between industries and roles.

The article introduces FastTreeSHAP, an open-source Python package designed to accelerate SHAP value computations for tree-based models.

The article discusses DARWIN, LinkedIn's unified Data Science and Artificial Intelligence Workbench, designed to streamline the workflows of data scientists and AI engineers by centralizing various...

The article reflects on the significant technological advancements and innovations achieved by LinkedIn's Engineering teams in 2021, highlighting milestones in data storage and infrastructure scali...

The article discusses LinkedIn's Remote Development initiative, which leverages cloud computing to enhance developer productivity by drastically reducing setup and build times.

The article discusses LinkedIn's Distributed Firewall (DFW), a host-level firewalling system designed to manage extensive firewall rules across a vast network of hosts.

This article discusses the evolution of LinkedIn's Daily Executive Dashboard (DED) from a simple dashboard to a robust enterprise-grade data pipeline.

The article introduces Greykite, an open-source Python library designed for fast and accurate time series forecasting.

The article discusses the launch of the School of SRE, an open-source initiative aimed at training aspiring Site Reliability Engineers (SREs).

The article discusses the challenges of memory fragmentation encountered by the Venice team at LinkedIn and how they addressed these issues using Jemalloc, a memory allocator.

The article discusses the Smart Argument Suite, a Python library designed to streamline the process of passing command-line arguments for AI workflows.

GDMix is a deep ranking personalization framework developed by LinkedIn to enhance the efficiency of training large-scale personalization models.

The article discusses the implementation of dark canary clusters at LinkedIn, a technique for safely testing new code in production environments without impacting end users.

The article discusses the Mobile Test Orchestrator (MTO), a framework developed by LinkedIn to efficiently manage and execute Android tests across multiple emulators and servers.

The article discusses the open-sourcing of LinkedIn's Spark inequality A/B testing library, named spark-inequality-impact, aimed at measuring and reducing inequality in product design.

The article discusses LinkedIn's transition from Python 2 to Python 3, emphasizing the importance of this migration for improving developer happiness and productivity.

The article discusses the 'skills genome methodology' developed by LinkedIn to identify unique skills associated with emerging jobs, which are rapidly growing but may lack a large workforce.

The article discusses how LinkedIn improved developer productivity and build times using Gradle build cache.

The article discusses the importance of data management at LinkedIn, focusing on expediting data fixes and migrations through a centralized, scalable self-service platform.

The article discusses how Apache Calcite can bridge the gap between offline and nearline computations in big data processing.

The article announces the release of Samza 1. 0, a distributed stream processing framework developed at LinkedIn, highlighting its significant features and improvements.

The article discusses the re-architecture of LinkedIn's contacts and calendar ecosystem, focusing on the migration to a single source of truth for contact data.

The article discusses the open sourcing of TonY, a framework designed to enable native support for TensorFlow on Hadoop.

The article discusses the enhancements made to the Women in Tech High School Trainee Program at LinkedIn, highlighting the personalized curriculum and mentorship that aim to inspire high school gir...

The article discusses the personal and career development journey of Jiechun (Jamie) Sun, a software engineer at Google, focusing on the challenges faced during her transition into the tech industr...

The article introduces Terry Juan Bates, a Site Reliability Engineer at LinkedIn, detailing his career journey, daily responsibilities, and insights into the engineering culture at LinkedIn.

The article discusses LinkedOut, a request-level failure injection framework developed by LinkedIn to enhance resilience engineering.

The article introduces shiv, an open-source tool developed by LinkedIn for creating single binary artifacts from Python projects that include all dependencies.

The article discusses the evolution of Couchbase at LinkedIn, detailing its transition from Memcached to Couchbase as a standard caching platform.

The article discusses the essential elements of modern data science, particularly in the context of Big Data and AI.

The article discusses LinkedIn's commitment to the open source community, particularly in the context of International Women's Day.

The article discusses the open sourcing of two automation tools, Fossor and Ascii Etch, designed to assist Site Reliability Engineers (SREs) in automating the investigation of broken hosts and serv...

The article discusses the Couchbase ecosystem at LinkedIn, detailing its critical role in the company's caching systems and the tooling developed to scale its deployment.