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Our client, a leading international media company, faced a major challenge: Video processing was slow and required significant manual intervention. The company needed a solution to improve scalability, reduce maintenance costs and shorten video processing time. It also needed to implement existing DevOps practices and introduce workflow management to increase efficiency.
To address this challenge, we conducted a comprehensive requirements analysis based on the currently implemented processes and the client's business needs. Based on these findings, we designed a scalable architecture based on an MLOps platform.
The architecture design included the implementation of Airflow, a workflow management tool running on Kubernetes in a cloud and on-premise environment. By implementing Airflow, we were able to enable the parallelisation of tasks, drastically reducing the processing time of the videos. Additionally, we integrated MLflow into the architecture to enable tracking and management of models. This has allowed the company to benefit from improved model management and increased code quality.
We also implemented DevOps best practices such as versioning, testing and CI/CD to ensure seamless integration and deployment of the developed solutions. By implementing these practices, the company has been able to improve the agility and efficiency of machine learning model development and deployment.
Thanks to the MLOps platform we developed, our client was able to achieve significant improvements. The processing time of the videos was significantly reduced, resulting in considerable time and cost savings. Manual intervention has been minimised as automated workflows run smoothly. The adoption of DevOps best practices has increased code quality and improved collaboration between the development and operations teams.
In addition, our client received a comprehensive overview of the different roles in the entire ML lifecycle and an assessment of current needs. This enabled the company to allocate resources efficiently and further optimise internal processes.
A detailed cost analysis was also conducted to evaluate the advantages and disadvantages of on-premise and cloud deployment of the architecture design. This enabled the company to make an informed decision that met their specific requirements and budget constraints.
In summary, our MLOps platform solution has helped the international media company to improve videoprocessing, increase efficiency and save costs. The introduction of Airflow and MLflow significantly reduced video processing time, enabling the company to deliver content faster. The automated workflows minimised manual intervention and ensured smooth processing of the videos.
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Michael Scharpf | Sr. Principal Key Account Manager | Alexander Thamm GmbH