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LiveAgent stands out with its ultra-fast performance, robust ticketing system, and user-friendly interface. It is a scalable solution equipped with over 180+ features and 200+ integrations, capable of growing as your customer service needs expand.
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LiveAgent offers 24/7 availability, an exceptional 20-second average response time, and extraordinary usability. Suitable for any type of business, its unbeatable value for money makes it a top choice for reliable and efficient customer service.
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Our primary audience consists of businesses of all sizes seeking to enhance their customer service experience. This includes startups, SMEs, and large corporations across various industries.
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Born out of the need for better customer interactions, LiveAgent was founded in 2004. Driven by the philosophy 'to treat customers as people, not tickets,' we've grown into a leading customer service solution.
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As a cloud-based solution, LiveAgent employs cutting-edge technologies to ensure a fast, secure, and reliable customer service platform. Our intricate infrastructure guarantees optimal functionality and high performance at all times.
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Renowned brands like Huawei, Yamaha, BMW, and Oxford University are proud users of LiveAgent, trusting us for world-class customer service.
Based on our record, Apache Airflow seems to be more popular. It has been mentiond 68 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Instead of the custom orchestrator I used, a proper orchestration tool should replace it like Apache Airflow, Dagster, ..., etc. - Source: dev.to / 13 days ago
An integral part of an ML project is data acquisition and data transformation into the required format. This involves creating ETL (extract, transform, load) pipelines and running them periodically. Airflow is an open source platform that helps engineers create and manage complex data pipelines. Furthermore, the support for Python programming language makes it easy for ML teams to adopt Airflow. - Source: dev.to / 24 days ago
Level 1 of MLOps is when you've put each lifecycle stage and their intefaces in an automated pipeline. The pipeline could be a python or bash script, or it could be a directed acyclic graph run by some orchestration framework like Airflow, dagster or one of the cloud-provider offerings. AI- or data-specific platforms like MLflow, ClearML and dvc also feature pipeline capabilities. - Source: dev.to / about 2 months ago
For the third, examples here might be analytics plugins in specialized databases like Clickhouse, data-transformations in places like your ETL pipeline using Airflow or Fivetran, or special integrations in your authentication workflow with Auth0 hooks and rules. - Source: dev.to / 5 months ago
Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows. Source: 8 months ago
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