{"id":495585,"date":"2023-09-15T08:09:27","date_gmt":"2023-09-15T06:09:27","guid":{"rendered":"https:\/\/www.devoteam.com\/success-story\/how-fuga-leverages-devoteams-vertex-ai-foundations-to-push-more-ml-pipelines-into-production-2\/"},"modified":"2025-02-07T09:30:00","modified_gmt":"2025-02-07T08:30:00","slug":"how-fuga-leverages-devoteams-vertex-ai-foundations-to-push-more-ml-pipelines-into-production-2","status":"publish","type":"success-story","link":"https:\/\/devoteam.info\/en-pt\/success-story\/how-fuga-leverages-devoteams-vertex-ai-foundations-to-push-more-ml-pipelines-into-production-2\/","title":{"rendered":"How FUGA leverages Devoteam\u2019s Vertex AI Foundations to push more ML pipelines into production"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\"><strong>About the customer<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/fuga.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">FUGA<\/a> is a world-leading b2b music distribution, marketing services and technology company that offers a flexible and customisable approach to meet the needs of content owners of all sizes. A division of Downtown Music, FUGA&#8217;s advanced music distribution technology, detailed analytics software, and royalty accounting suite give their customers the control they need to navigate the digital landscape as their needs evolve. With their client-centric attitude to business, FUGA helps music rights holders succeed by getting their content to more than 250 global music platforms, like YouTube Music and Spotify. Backed by a team of over 200 experts, they&#8217;re the go-to platform for independent labels, management agencies, and distributors. Its industry-leading agnostic platform allows their clients to choose the services that best suit their business plan, so they can seize opportunities and overcome challenges in the digital music market.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>The problem: a missing note in the ML symphony<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">FUGA has several data scientists in its team, composing strong insights around their large amounts of data. They used to construct models orchestrated with a series of modular Python scripts and using a traditional machine learning cycle: data pre-processing, feature engineering, model training, evaluation phases and deployment. These processes cause a lot of operational overhead, leading to a high time-to-market and low development velocity. In their growing interest in advanced statistical models and machine learning, FUGA reached out to <a href=\"https:\/\/gcloud.devoteam.com\/about-us\/\">Devoteam G Cloud<\/a> to accelerate their adoption of best practices and reduce their time-to-market.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>The goal: accelerate, experiment, and empower<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In the quest of fully leveraging their wealth of data for the good of artists and fans, FUGA wants to be able to seamlessly develop more ML models and bring them to production. This can be achieved with <a href=\"https:\/\/gcloud.devoteam.com\/ebook\/the-machine-learning-on-google-cloud-workbook\/the-basics-of-mlops\/\">MLOps<\/a>. In short, it is to machine learning what DevOps is to software development. It brings standardized, automated and resilient practices to increase release velocity for machine learning.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>In addition to the improved velocity, FUGA was aiming to:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Adopt a fully <strong>serverless architecture<\/strong>, enabling them to elastically scale to their requirements while decreasing costs in periods where they need fewer resources.<\/li>\n\n\n\n<li>Build<strong> robust ML pipelines<\/strong>, giving them a lot of flexibility for their experimentations, and ensuring the reproducibility of their results.<\/li>\n\n\n\n<li>Enforce the <strong>traceability of ML artefacts<\/strong>, providing visibility on the exact state and evolution of each individual step of ML processes.<\/li>\n\n\n\n<li>Take advantage of <strong>detailed<\/strong> <strong>monitoring<\/strong>, thanks to granular dashboards containing resources usage, evaluation metrics, and other important values.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">By setting these goals, FUGA aimed to evolve their overall ML practices and, in doing so, secure a solid framework for advancing their goals. This transformation was rooted in their understanding that the real end goal encompassed not only the deployment of models but also a strategic foundation that would lead to greater confidence when deploying new ML models, faster release cycles, and substantial cost optimization.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">To attain these goals, FUGA partnered with Devoteam G Cloud to adapt one of FUGA\u2019s machine learning models to utilise solid MLOps principles, and then rely on Devoteam\u2019s support to help FUGA towards the adoption of the new framework in their machine learning roadmap.<\/p>\n\n\n\n\t<div class=\"align wp-block-acf-quote\">\n\n\t\n\t<figure class=\"text-center\"><blockquote class=\"blockquote\"><svg width=\"49\" height=\"38\" viewBox=\"0 0 49 38\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n<path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M42.875 0C44.4995 0 46.0574 0.652728 47.206 1.81459C48.3547 2.97645 49 4.55228 49 6.1954V15.4885C49 17.1316 48.3547 18.7075 47.206 19.8693C46.0574 21.0312 44.4995 21.6839 42.875 21.6839H33.6875C33.6875 24.1486 34.6555 26.5123 36.3785 28.2551C38.1014 29.9979 40.4383 30.977 42.875 30.977H45.9375C46.7497 30.977 47.5287 31.3034 48.103 31.8843C48.6773 32.4652 49 33.2532 49 34.0747C49 34.8963 48.6773 35.6842 48.103 36.2651C47.5287 36.8461 46.7497 37.1724 45.9375 37.1724H42.875C38.8139 37.1724 34.9191 35.5406 32.0474 32.6359C29.1758 29.7313 27.5625 25.7917 27.5625 21.6839V6.1954C27.5625 4.55228 28.2078 2.97645 29.3565 1.81459C30.5051 0.652728 32.063 0 33.6875 0H42.875ZM15.3125 0C16.937 0 18.4949 0.652728 19.6435 1.81459C20.7922 2.97645 21.4375 4.55228 21.4375 6.1954V15.4885C21.4375 17.1316 20.7922 18.7075 19.6435 19.8693C18.4949 21.0312 16.937 21.6839 15.3125 21.6839H6.125C6.125 24.1486 7.09296 26.5123 8.81596 28.2551C10.5389 29.9979 12.8758 30.977 15.3125 30.977H18.375C19.1872 30.977 19.9662 31.3034 20.5405 31.8843C21.1148 32.4652 21.4375 33.2532 21.4375 34.0747C21.4375 34.8963 21.1148 35.6842 20.5405 36.2651C19.9662 36.8461 19.1872 37.1724 18.375 37.1724H15.3125C11.2514 37.1724 7.35658 35.5406 4.48493 32.6359C1.61328 29.7313 0 25.7917 0 21.6839V6.1954C0 4.55228 0.645313 2.97645 1.79397 1.81459C2.94263 0.652728 4.50055 0 6.125 0H15.3125Z\" fill=\"#3C3C3A\"\/>\n<\/svg><p>FUGA already benefits from using a variety of GCP services, including BigQuery. With the guidance and expertise from our Cloud Partner Devoteam, we can now leverage the latest in Google\u2019s Artificial Intelligence service offerings and the strong data integrations across its cloud services to bring our data into a state-of-the-art Machine Learning environment that employs the best MLOps practices to date.<\/p><\/blockquote><figcaption class=\"blockquote-footer\">\n<div class=\"wp-block-group is-nowrap is-layout-flex wp-container-core-group-is-layout-8d39b2df wp-block-group-is-layout-flex\"><figure class=\"wp-block-image size-full is-resized is-style-rounded\"><img decoding=\"async\" src=\"https:\/\/devo2024.local\/wp-content\/uploads\/2024\/08\/nico-sienaert.jpg\" alt=\"\" class=\"wp-image-36180\" style=\"aspect-ratio:1;object-fit:cover;width:80px\"\/><\/figure>\n<div class=\"wp-block-group is-vertical is-layout-flex wp-container-core-group-is-layout-6fa7971b wp-block-group-is-layout-flex\">\n\t<p class=\"has-medium-font-size wp-block-paragraph\" style=\"font-style:normal;font-weight:500\">Gerrit Theron<\/p>\n\t\n\t<p class=\"wp-block-paragraph\">Head of Data Engineering at FUGA<\/p>\n\t<\/div>\n<\/div>\n<\/figcaption><\/figure>\n\t\n\t<\/div>\n\n\n\n\n<h2 class=\"wp-block-heading\"><strong>The solution: success with the Vertex AI Foundations from Devoteam<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The model of this project tackles an important challenge for music publishers: predicting revenues on all available platforms. This ensures that music publishers can project their sales and plan accordingly. Underpinning this model with solid MLOps principles is of the utmost importance, as this model needs to be available continuously, make accurate predictions, and be easily maintainable by FUGA\u2019s teams.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Leveraging Devoteam G Cloud\u2019s Vertex AI Foundations<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The <a href=\"https:\/\/gcloud.devoteam.com\/products\/google-cloud-accelerators\/accelerator-vertex-ai\/\">Vertex AI Foundations<\/a>, built by Devoteam G Cloud\u2019s in-house experts, served as a framework, integrating the essential components required to establish reliable principles and operations within Vertex AI. Employing an infrastructure-as-code approach, this framework incorporates the necessary tooling to properly connect pipelines, components, packages, and containers to apply MLOps on Vertex AI.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Using the Vertex AI Foundations for this project brought, amongst others, the following benefits:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Faster release cycles:<\/strong> instead of focusing on operational tasks (building artefacts, retraining models, manually experimenting with models parameters, \u2026), machine learning engineers can focus on the core of their jobs: building better models that deliver more business value.<\/li>\n\n\n\n<li><strong>Improved developer experience:<\/strong> thanks to the built-in integrations of the Vertex AI Foundations between Google Cloud services, code snippets and reusable services, adding new features to an ML project has never been easier.<\/li>\n\n\n\n<li><strong>Scalability \/ traceability \/ reproducibility:<\/strong> the Vertex AI Foundations combines all Vertex AI and Google Cloud services necessary in your MLOps journey, bringing the required features to confidently deploy models in production.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Technical Solution Framework<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large\"><img decoding=\"async\" src=\"https:\/\/devoteam.info\/wp-content\/uploads\/2024\/09\/FUGA-Customer-Stories-Devoteam-G-Cloud_Img0.png\" alt=\"\" class=\"wp-image-82658\"\/><\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Continuous Integration\/Continuous Deployment (CI\/CD):<\/strong> The solution includes a CI\/CD pipeline that automates the creation of artefacts. This practice is essential because it allows for versioning of every artefact, providing complete visibility into the entire ML pipeline. This is beneficial because in the event of production issues, it is now possible to trace back to the model&#8217;s code, the specific pipeline used for training, and the exact components and packages used.<\/li>\n\n\n\n<li><strong>Automated retraining for sustained performance: <\/strong>Another key feature is automatic retraining, which can be scheduled or triggered by evaluation metrics. If performance degradation is detected in production, the ML models can be automatically retrained. This practice aligns with the goal of ensuring that the model&#8217;s performance remains relevant over time.<\/li>\n\n\n\n<li><strong>Enhancing Data Preparation:<\/strong> Although data preparation was performed by the client, Devoteam G Cloud optimized the process in terms of both parallelization and cost-effectiveness. This enhancement resulted in a significant increase in speed and efficiency. This was a critical improvement given that the ML pipeline included over 1,000 components.<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/devoteam.info\/wp-content\/uploads\/2024\/09\/FUGA-Customer-Stories-Devoteam-G-Cloud_Img1.png\" alt=\"\" class=\"wp-image-82672\" style=\"width:567px;height:558px\" width=\"567\" height=\"558\"\/><\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Hyperparameter Tuning with Vizier AI:<\/strong> Google&#8217;s Vizier AI is used to identify the optimal model parameters for peak performance. The tool recommends parameters based on conducted experiments and iteratively adapts suggestions to the specific problem being addressed, resulting in a fine-tuned and optimized model. After identifying the best model, thorough evaluation and validation ensure its capability against a dedicated test set.<\/li>\n\n\n\n<li><strong>Efficient Evaluation and Automated Deployment: <\/strong>Models that pass performance benchmarks and validation criteria are enrolled in the model registry. The selected model is then deployed to Vertex AI through a fully automated process, making the transition from development to deployment easy.<\/li>\n<\/ul>\n\n\n\n\t<div class=\"align wp-block-acf-quote\">\n\n\t\n\t<figure class=\"text-center\"><blockquote class=\"blockquote\"><svg width=\"49\" height=\"38\" viewBox=\"0 0 49 38\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n<path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M42.875 0C44.4995 0 46.0574 0.652728 47.206 1.81459C48.3547 2.97645 49 4.55228 49 6.1954V15.4885C49 17.1316 48.3547 18.7075 47.206 19.8693C46.0574 21.0312 44.4995 21.6839 42.875 21.6839H33.6875C33.6875 24.1486 34.6555 26.5123 36.3785 28.2551C38.1014 29.9979 40.4383 30.977 42.875 30.977H45.9375C46.7497 30.977 47.5287 31.3034 48.103 31.8843C48.6773 32.4652 49 33.2532 49 34.0747C49 34.8963 48.6773 35.6842 48.103 36.2651C47.5287 36.8461 46.7497 37.1724 45.9375 37.1724H42.875C38.8139 37.1724 34.9191 35.5406 32.0474 32.6359C29.1758 29.7313 27.5625 25.7917 27.5625 21.6839V6.1954C27.5625 4.55228 28.2078 2.97645 29.3565 1.81459C30.5051 0.652728 32.063 0 33.6875 0H42.875ZM15.3125 0C16.937 0 18.4949 0.652728 19.6435 1.81459C20.7922 2.97645 21.4375 4.55228 21.4375 6.1954V15.4885C21.4375 17.1316 20.7922 18.7075 19.6435 19.8693C18.4949 21.0312 16.937 21.6839 15.3125 21.6839H6.125C6.125 24.1486 7.09296 26.5123 8.81596 28.2551C10.5389 29.9979 12.8758 30.977 15.3125 30.977H18.375C19.1872 30.977 19.9662 31.3034 20.5405 31.8843C21.1148 32.4652 21.4375 33.2532 21.4375 34.0747C21.4375 34.8963 21.1148 35.6842 20.5405 36.2651C19.9662 36.8461 19.1872 37.1724 18.375 37.1724H15.3125C11.2514 37.1724 7.35658 35.5406 4.48493 32.6359C1.61328 29.7313 0 25.7917 0 21.6839V6.1954C0 4.55228 0.645313 2.97645 1.79397 1.81459C2.94263 0.652728 4.50055 0 6.125 0H15.3125Z\" fill=\"#3C3C3A\"\/>\n<\/svg><p>The solution that Devoteam G Cloud provided us has exceeded all expectations, and they were already high. This will make a big difference in the direction of where FUGA is going as a company within the data space and has laid the foundation for new great steps ahead. We\u2019ll be making sure to get the most out of your knowledge and expertise and hopefully, we can collaborate on more projects in the future. Really happy to report back to FUGA the amazing work you have done and how valuable it has been for us.<\/p><\/blockquote><figcaption class=\"blockquote-footer\">\n<div class=\"wp-block-group is-nowrap is-layout-flex wp-container-core-group-is-layout-8d39b2df wp-block-group-is-layout-flex\"><figure class=\"wp-block-image size-full is-resized is-style-rounded\"><img decoding=\"async\" src=\"https:\/\/devo2024.local\/wp-content\/uploads\/2024\/08\/nico-sienaert.jpg\" alt=\"\" class=\"wp-image-36180\" style=\"aspect-ratio:1;object-fit:cover;width:80px\"\/><\/figure>\n<div class=\"wp-block-group is-vertical is-layout-flex wp-container-core-group-is-layout-6fa7971b wp-block-group-is-layout-flex\">\n\t<p class=\"has-medium-font-size wp-block-paragraph\" style=\"font-style:normal;font-weight:500\">Gerrit Theron<\/p>\n\t\n\t<p class=\"wp-block-paragraph\">Head of Data Engineering at FUGA<\/p>\n\t<\/div>\n<\/div>\n<\/figcaption><\/figure>\n\t\n\t<\/div>\n\n\n\n\n<h2 class=\"wp-block-heading\"><strong>The methodology<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The project started with the installation of the <a href=\"https:\/\/gcloud.devoteam.com\/products\/google-cloud-accelerators\/accelerator-vertex-ai\/\">Vertex AI Foundations<\/a>. Once the MLOps infrastructure was set up, Devoteam G Cloud\u2019s engineers could start focusing on the development of the ML pipeline from FUGA\u2019s existing code.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">To enhance data preparation and model performance, refinements were made iteratively. Thorough documentation and insights were then shared with the FUGA team, facilitating their understanding of the Vertex AI Foundations and its usage.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Devoteam G Cloud will continue supporting FUGA in their adoption of the Vertex AI Foundations in their roadmap towards having more ML use cases. This collaborative approach ensures a gradual adoption and impactful outcomes over time for FUGA and their customers.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>The results<\/strong>: efficiency, cost optimization and a foundation for their ML practices<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The benefits of this project were tangible for FUGA: a surge in efficiency, cost optimization, and especially a robust foundation for their ML practices. FUGA now has the power to monitor each step of the pipeline, trace the origin of every artefact, and adapt models with ease. Not to mention, FUGA&#8217;s team was thrilled with the collaboration, and the solution will greatly assist them in the next stage of their machine learning journey.<\/p>\n\n\n\n\t<div class=\"align wp-block-acf-quote\">\n\n\t\n\t<figure class=\"text-center\"><blockquote class=\"blockquote\"><svg width=\"49\" height=\"38\" viewBox=\"0 0 49 38\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n<path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M42.875 0C44.4995 0 46.0574 0.652728 47.206 1.81459C48.3547 2.97645 49 4.55228 49 6.1954V15.4885C49 17.1316 48.3547 18.7075 47.206 19.8693C46.0574 21.0312 44.4995 21.6839 42.875 21.6839H33.6875C33.6875 24.1486 34.6555 26.5123 36.3785 28.2551C38.1014 29.9979 40.4383 30.977 42.875 30.977H45.9375C46.7497 30.977 47.5287 31.3034 48.103 31.8843C48.6773 32.4652 49 33.2532 49 34.0747C49 34.8963 48.6773 35.6842 48.103 36.2651C47.5287 36.8461 46.7497 37.1724 45.9375 37.1724H42.875C38.8139 37.1724 34.9191 35.5406 32.0474 32.6359C29.1758 29.7313 27.5625 25.7917 27.5625 21.6839V6.1954C27.5625 4.55228 28.2078 2.97645 29.3565 1.81459C30.5051 0.652728 32.063 0 33.6875 0H42.875ZM15.3125 0C16.937 0 18.4949 0.652728 19.6435 1.81459C20.7922 2.97645 21.4375 4.55228 21.4375 6.1954V15.4885C21.4375 17.1316 20.7922 18.7075 19.6435 19.8693C18.4949 21.0312 16.937 21.6839 15.3125 21.6839H6.125C6.125 24.1486 7.09296 26.5123 8.81596 28.2551C10.5389 29.9979 12.8758 30.977 15.3125 30.977H18.375C19.1872 30.977 19.9662 31.3034 20.5405 31.8843C21.1148 32.4652 21.4375 33.2532 21.4375 34.0747C21.4375 34.8963 21.1148 35.6842 20.5405 36.2651C19.9662 36.8461 19.1872 37.1724 18.375 37.1724H15.3125C11.2514 37.1724 7.35658 35.5406 4.48493 32.6359C1.61328 29.7313 0 25.7917 0 21.6839V6.1954C0 4.55228 0.645313 2.97645 1.79397 1.81459C2.94263 0.652728 4.50055 0 6.125 0H15.3125Z\" fill=\"#3C3C3A\"\/>\n<\/svg><p>A new standard of what we can do in the Data space with our company<\/p><\/blockquote><figcaption class=\"blockquote-footer\">\n<div class=\"wp-block-group is-nowrap is-layout-flex wp-container-core-group-is-layout-8d39b2df wp-block-group-is-layout-flex\"><figure class=\"wp-block-image size-full is-resized is-style-rounded\"><img decoding=\"async\" src=\"https:\/\/devo2024.local\/wp-content\/uploads\/2024\/08\/nico-sienaert.jpg\" alt=\"\" class=\"wp-image-36180\" style=\"aspect-ratio:1;object-fit:cover;width:80px\"\/><\/figure>\n<div class=\"wp-block-group is-vertical is-layout-flex wp-container-core-group-is-layout-6fa7971b wp-block-group-is-layout-flex\">\n\t<p class=\"has-medium-font-size wp-block-paragraph\" style=\"font-style:normal;font-weight:500\">Gerrit Theron<\/p>\n\t\n\t<p class=\"wp-block-paragraph\">Head of Data Engineering at FUGA<\/p>\n\t<\/div>\n<\/div>\n<\/figcaption><\/figure>\n\t\n\t<\/div>\n\n\n\n\n\t<div class=\"align wp-block-acf-lead-magnet\">\n\n\t\n\t\n<div class=\"wp-block-group is-nowrap is-layout-flex wp-container-core-group-is-layout-70787b68 wp-block-group-is-layout-flex\" style=\"border-top-color:var(--wp--preset--color--gray);border-top-width:1px;border-bottom-color:var(--wp--preset--color--gray);border-bottom-width:1px;margin-top:var(--wp--preset--spacing--x-large);margin-bottom:var(--wp--preset--spacing--x-large);padding-top:var(--wp--preset--spacing--large);padding-bottom:var(--wp--preset--spacing--large)\">\n<figure class=\"wp-block-image size-large has-custom-border\"><img decoding=\"async\" src=\"https:\/\/devo2024.local\/wp-content\/uploads\/2024\/09\/diego-jimenez-A-NVHPka9Rk-unsplash-scaled-1-1024x683.jpg\" alt=\"\" class=\"wp-image-37449\" style=\"border-top-right-radius:150px;border-bottom-right-radius:150px;aspect-ratio:3\/2;object-fit:cover\"\/><\/figure>\n\n<div class=\"wp-block-group has-global-padding is-layout-constrained wp-container-core-group-is-layout-2efe2b56 wp-block-group-is-layout-constrained\" style=\"padding-right:0;padding-left:0\"><p>u003ch2u003eReady to speed up your AI u0026amp; ML journey with the Vertex AI Foundations, just like Fuga?u003c\/h2u003ernLearn how Devoteam&#8217;s Vertex AI Foundations can accelerate your ML projects, ensuring quicker deployment, cost savings, and a strong foundation for success. Don&#8217;t hesitate \u2013 u003ca href=u0022https:\/\/gcloud.devoteam.com\/contact-us\/u0022u003eget in touchu003c\/au003e to start your journey and stay ahead using the latest machine learning technology.<\/p>\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" target=\"\" href=\"https:\/\/gcloud.devoteam.com\/products\/google-cloud-accelerators\/accelerator-vertex-ai\/\">Discover Vertex AI Foundations<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\t\n\t<\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>FUGA, a leading music distribution and technology company, partnered with Devoteam to streamline their machine learning processes. With Devoteam&#8217;s Vertex AI Foundations, FUGA achieved faster ML model deployment, cost efficiency, improved monitoring capabilities, and especially a robust foundation for their ML practices. Now, FUGA is well-prepared to pave the way for future success.<\/p>\n","protected":false},"featured_media":0,"template":"","categories":[915,917],"tags":[2411,2510,3251,3252,2838],"industry":[],"class_list":["post-495585","success-story","type-success-story","status-publish","hentry","category-ai-en-pt","category-google-cloud-en-pt","tag-g-cloud-en-pt","tag-google-cloud-platform-en-pt","tag-machine-learning-en-pt","tag-ml-ai-en-pt","tag-vertex-ai-en-pt"],"acf":[],"cards":"\n\t<div class=\"single-post-card\">\n\n\t\t\n\n\t\t\n\t\t<div class=\"wp-block-group is-vertical is-layout-flex wp-container-core-group-is-layout-43282307 wp-block-group-is-layout-flex\">\n\t<p style=\"font-style:normal;font-weight:700\" class=\"has-link-color wp-elements-1 wp-block-lp-post-type has-text-color has-primary-color has-small-font-size\">Success story<\/p>\n\n\t\t\n\t\t<h3 style=\"font-style:normal;font-weight:400\" class=\"wp-block-post-title has-base-font-size\"><a href=\"https:\/\/devoteam.info\/en-pt\/success-story\/how-fuga-leverages-devoteams-vertex-ai-foundations-to-push-more-ml-pipelines-into-production-2\/\" target=\"_self\" >How FUGA leverages Devoteam\u2019s Vertex AI Foundations to push more ML pipelines into production<\/a><\/h3><\/div>\n\t\t\n\t<\/div>\n\n","yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.4 (Yoast SEO v28.4) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>How FUGA leverages Devoteam\u2019s Vertex AI Foundations to push more ML pipelines into production | Devoteam<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/devoteam.info\/en-pt\/success-story\/how-fuga-leverages-devoteams-vertex-ai-foundations-to-push-more-ml-pipelines-into-production-2\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How FUGA leverages Devoteam\u2019s Vertex AI Foundations to push more ML pipelines into production\" \/>\n<meta property=\"og:description\" content=\"FUGA, a leading music distribution and technology company, partnered with Devoteam to streamline their machine learning processes. 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