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Netflix AI-DL-ML-LLM related GitHub Repositories
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For the top 15 GitHub repos, ask for 10 paragraphs. e.g. Amazon SageMaker Features, Amazon SageMaker Alternatives, Amazon SageMaker Security, , Amazon SageMaker DevOps
Netflix Metaflow
Introduced in 2019, Netflix Metaflow is an open-source platform that assists data scientists and engineers in developing, scaling, and deploying machine learning and data science projects. It provides a user-friendly Python API for managing workflows, versioning data, and orchestrating compute resources, thereby streamlining the process of transitioning from prototype to production in ML projects.
Netflix Polynote
Launched in 2019, Netflix Polynote is an open-source polyglot notebook environment that supports multiple programming languages within a single notebook. Designed to enhance data science workflows, it allows seamless integration of languages like Scala, Python, and SQL, facilitating interactive data analysis and visualization.
Netflix Surus
Released in 2014, Netflix Surus is a collection of machine learning and data analysis tools built on top of Apache Pig. It includes implementations of various algorithms such as logistic regression, k-means clustering, and principal component analysis, enabling scalable data processing and analysis within the Hadoop ecosystem.
Netflix Vectorflow
Introduced in 2016, Netflix Vectorflow is a minimalist neural network library designed for high performance. Written in Scala, it focuses on efficiency and speed, providing a platform for developing and deploying deep learning models in production environments.
Netflix Conductor
Launched in 2017, Netflix Conductor is an open-source microservices orchestration engine. It facilitates the development of complex workflows by coordinating multiple microservices, enabling scalable and resilient application architectures.
Netflix Atlas
Released in 2014, Netflix Atlas is an in-memory dimensional time series database designed for real-time operational monitoring. It efficiently collects, stores, and queries large volumes of time-series data, providing insights into system performance and health.
Netflix Hystrix
Introduced in 2012, Netflix Hystrix is a latency and fault tolerance library designed to isolate points of access to remote systems, services, and third-party libraries. It helps prevent cascading failures and improves system resilience by managing the interactions between distributed services.
Netflix Ribbon
Launched in 2013, Netflix Ribbon is a client-side load balancer that provides software load balancing algorithms and service discovery capabilities. It enables efficient distribution of network traffic across multiple servers, enhancing the scalability and reliability of applications.
Netflix Zuul
Released in 2013, Netflix Zuul is an edge service that provides dynamic routing, monitoring, resiliency, and security. It acts as a gateway for all incoming requests to the backend services, offering functionalities such as authentication, insights, and stress testing.
Netflix Eureka
Introduced in 2012, Netflix Eureka is a service registry that facilitates service discovery in microservices architectures. It allows services to locate and communicate with each other, enabling dynamic scaling and resilience in distributed systems.
https://github.com/Netflix/eureka
Netflix Machine Learning Infrastructure
In 2020, Netflix introduced its Machine Learning Infrastructure, designed to support the development and deployment of ML models at scale. This infrastructure provides tools and platforms that enable data scientists and engineers to build, train, and deploy models efficiently, facilitating the integration of ML into various Netflix services.
Netflix Mantis
Launched in 2014, Netflix Mantis is a platform for real-time stream processing. It allows users to process large volumes of data with low latency, enabling real-time analytics and monitoring. Mantis is utilized within Netflix for various use cases, including operational monitoring and machine learning data pipelines.
Netflix Dyno
Introduced in 2013, Netflix Dyno is a Java client library for Redis, designed for high availability and scalability. It provides a robust interface for interacting with Redis clusters, supporting the data needs of Netflix's services, including those that leverage machine learning models.
Netflix Hollow
Released in 2016, Netflix Hollow is a Java library and toolset for handling in-memory datasets efficiently. It is particularly useful for applications that require access to large datasets with minimal latency, such as recommendation systems powered by machine learning algorithms.
Netflix Vizceral
In 2015, Netflix developed Vizceral, a tool for visualizing traffic flow within complex systems. It provides real-time insights into the state of the network, aiding in the monitoring and optimization of services, including those that incorporate AI and ML components.
Netflix Titus
Launched in 2015, Netflix Titus is a container management platform that provides scalable and reliable container execution. It supports the deployment of machine learning models in containerized environments, facilitating the integration of ML workloads into Netflix's infrastructure.
Netflix Spinnaker
Introduced in 2015, Netflix Spinnaker is a continuous delivery platform that enables fast and reliable software releases. It supports the deployment of machine learning models by automating the delivery process, ensuring that new models can be tested and rolled out efficiently.
Netflix Fast Data
In 2017, Netflix open-sourced its Fast Data platform, designed for processing streaming data in real-time. It enables the development of applications that require immediate insights, such as those powered by machine learning models for recommendations and personalization.
Netflix Genie
Released in 2013, Netflix Genie is a federated job orchestration engine that provides job management capabilities for big data processing. It supports the execution of machine learning workflows, allowing data scientists to run and monitor their jobs across different environments.
Netflix Iceberg
In 2018, Netflix contributed to the development of Apache Iceberg, a high-performance format for huge analytic tables. It enables the management of petabyte-scale datasets, facilitating the storage and retrieval of data used in machine learning and AI applications.
https://github.com/apache/iceberg
Netflix Meson
In 2020, Netflix introduced Meson, a workflow orchestration and scheduling system designed to manage complex data processing pipelines. It enables efficient scheduling, monitoring, and management of workflows, ensuring reliable data processing for various applications, including those involving machine learning and AI models.
Netflix Dispatch
Launched in 2020, Netflix Dispatch is an open-source incident management platform that facilitates the coordination of incident response efforts. It integrates with various communication and documentation tools, streamlining the process of managing incidents, which is crucial for maintaining the reliability of systems that deploy AI and ML models.
Netflix Lemur
Released in 2016, Netflix Lemur is an open-source certificate management framework that automates the issuance and renewal of security certificates. By ensuring secure communication channels, it supports the safe deployment of applications, including those utilizing machine learning models.
Netflix Scumblr
Introduced in 2014, Netflix Scumblr is a tool for performing periodic searches and storing actionable results. It aids in monitoring and analyzing data from various sources, which can be beneficial for security assessments and data analysis tasks involving AI and ML techniques.
Netflix Suro
Launched in 2013, Netflix Suro is a data pipeline service for collecting, aggregating, and dispatching large volumes of event data. It plays a critical role in data ingestion processes, feeding data into machine learning models for analysis and decision-making.
Netflix Edda
Released in 2012, Netflix Edda is a service that polls AWS resources and exposes them via a web interface. It provides insights into cloud infrastructure, aiding in the management and monitoring of resources that support AI and ML workloads.
Netflix Simian Army
Introduced in 2011, the Netflix Simian Army is a suite of tools designed to improve cloud infrastructure resilience. By intentionally causing failures, it tests the system's ability to withstand and recover, ensuring robustness for applications, including those deploying machine learning models.
Netflix Zuul 2
Launched in 2016, Netflix Zuul 2 is an edge service that provides dynamic routing, monitoring, resiliency, and security. It acts as a gateway for all incoming requests to backend services, offering functionalities such as authentication and insights, which are essential for applications utilizing AI and ML models.
Netflix EVCache
Released in 2012, Netflix EVCache is a distributed in-memory caching solution that provides low-latency data retrieval. It supports high-throughput applications, including those involving machine learning models, by caching frequently accessed data.
Netflix Archaius
Introduced in 2013, Netflix Archaius is a configuration management library that provides dynamic properties and configuration loading. It enables applications, including those deploying AI and ML models, to manage configurations efficiently, supporting dynamic changes without redeployment.
https://github.com/Netflix/archaius
Netflix Conductor
In 2017, Netflix introduced Conductor, an open-source microservices orchestration engine designed to manage complex workflows. It enables developers to build scalable and resilient applications by coordinating multiple microservices, facilitating the development and deployment of machine learning pipelines and other data-driven processes.
Netflix Metaflow
Launched in 2019, Netflix Metaflow is a human-friendly Python library that assists scientists and engineers in building and managing real-life data science projects. It provides a unified framework to prototype, deploy, and scale machine learning workflows, integrating seamlessly with existing infrastructure and supporting both batch and real-time data processing.
Netflix Polynote
Released in 2019, Netflix Polynote is an open-source polyglot notebook environment that supports multiple programming languages within a single notebook. It facilitates data analysis and visualization by allowing seamless integration of languages like Scala, Python, and SQL, enhancing productivity in data science workflows.
Netflix Surus
Introduced in 2014, Netflix Surus is a collection of machine learning and data analysis tools built on top of Apache Pig. It includes implementations of various algorithms such as logistic regression, k-means clustering, and principal component analysis, enabling scalable data processing and analysis within the Hadoop ecosystem.
Netflix Vectorflow
In 2016, Netflix developed Vectorflow, a minimalist neural network library written in Scala. Designed for high performance, it provides a platform for developing and deploying deep learning models in production environments, focusing on efficiency and speed.
Netflix Mantis
Launched in 2014, Netflix Mantis is a platform for real-time stream processing. It allows users to process large volumes of data with low latency, enabling real-time analytics and monitoring, which are essential for applications such as recommendation systems and anomaly detection powered by machine learning algorithms.
Netflix Dyno
Released in 2013, Netflix Dyno is a Java client library for Redis, designed for high availability and scalability. It provides a robust interface for interacting with Redis clusters, supporting the data needs of Netflix's services, including those that leverage machine learning models.
Netflix Hollow
Introduced in 2016, Netflix Hollow is a Java library and toolset for handling in-memory datasets efficiently. It is particularly useful for applications that require access to large datasets with minimal latency, such as recommendation systems powered by machine learning algorithms.
Netflix Vizceral
In 2015, Netflix developed Vizceral, a tool for visualizing traffic flow within complex systems. It provides real-time insights into the state of the network, aiding in the monitoring and optimization of services, including those that incorporate AI and ML components.
Netflix Titus
Launched in 2015, Netflix Titus is a container management platform that provides scalable and reliable container execution. It supports the deployment of machine learning models in containerized environments, facilitating the integration of ML workloads into Netflix's infrastructure.
https://github.com/Netflix/titus-control-plane
Netflix Metaflow
In 2019, Netflix introduced Metaflow, a human-friendly Python library that assists scientists and engineers in building and managing real-life data science projects. It provides a unified framework to prototype, deploy, and scale machine learning workflows, integrating seamlessly with existing infrastructure and supporting both batch and real-time data processing.
Netflix Polynote
Released in 2019, Polynote is an open-source polyglot notebook environment developed by Netflix. It supports multiple programming languages within a single notebook, facilitating data analysis and visualization by allowing seamless integration of languages like Scala, Python, and SQL, thereby enhancing productivity in data science workflows.
Netflix Surus
Introduced in 2014, Surus is a collection of machine learning and data analysis tools built on top of Apache Pig. Developed by Netflix, it includes implementations of various algorithms such as logistic regression, k-means clustering, and principal component analysis, enabling scalable data processing and analysis within the Hadoop ecosystem.
Netflix Vectorflow
In 2016, Netflix developed Vectorflow, a minimalist neural network library written in Scala. Designed for high performance, it provides a platform for developing and deploying deep learning models in production environments, focusing on efficiency and speed.
Netflix Mantis
Launched in 2014, Mantis is a platform for real-time stream processing developed by Netflix. It allows users to process large volumes of data with low latency, enabling real-time analytics and monitoring, which are essential for applications such as recommendation systems and anomaly detection powered by machine learning algorithms.
Netflix Dyno
Released in 2013, Dyno is a Java client library for Redis, designed by Netflix for high availability and scalability. It provides a robust interface for interacting with Redis clusters, supporting the data needs of Netflix's services, including those that leverage machine learning models.
Netflix Hollow
Introduced in 2016, Hollow is a Java library and toolset for handling in-memory datasets efficiently. Developed by Netflix, it is particularly useful for applications that require access to large datasets with minimal latency, such as recommendation systems powered by machine learning algorithms.
Netflix Vizceral
In 2015, Netflix developed Vizceral, a tool for visualizing traffic flow within complex systems. It provides real-time insights into the state of the network, aiding in the monitoring and optimization of services, including those that incorporate AI and ML components.
Netflix Titus
Launched in 2015, Titus is a container management platform developed by Netflix that provides scalable and reliable container execution. It supports the deployment of machine learning models in containerized environments, facilitating the integration of ML workloads into Netflix's infrastructure.
Netflix Conductor
In 2017, Netflix introduced Conductor, an open-source microservices orchestration engine designed to manage complex workflows. It enables developers to build scalable and resilient applications by coordinating multiple microservices, facilitating the development and deployment of machine learning pipelines and other data-driven processes.