This post was written with NVIDIA and the authors would like to thank Adi Margolin, Eliuth Triana, and Maryam Motamedi for their collaboration. Organizations today face the challenge of processing large volumes of audio data–from customer calls and meeting recordings to podcasts and voice messages–to unlock valuable insights. Automatic SpeechContinue Reading
Deploying machine learning (ML) models into production can often be a complex and resource-intensive task, especially for customers without deep ML and DevOps expertise. Amazon SageMaker Canvas simplifies model building by offering a no-code interface, so you can create highly accurate ML models using your existing data sources and withoutContinue Reading
Large-scale AI model training faces significant challenges with failure recovery and monitoring. Traditional training requires complete job restarts when even a single training process fails, resulting in additional downtime and increased costs. As training clusters expand, identifying and resolving critical issues like stalled GPUs and numerical instabilities typically requires complexContinue Reading
Generative AI is rapidly reshaping the music industry, empowering creators—regardless of skill—to create studio-quality tracks with foundation models (FMs) that personalize compositions in real time. As demand for unique, instantly generated content grows and creators seek smarter, faster tools, Splash Music collaborated with AWS to develop and scale music generationContinue Reading
Scala stands out as a versatile programming language that combines object-oriented and functional programming approaches. By running on the Java Virtual Machine (JVM), it maintains seamless compatibility with Java libraries while offering a concise and scalable development experience. The language has distinguished itself in the realm of distributed computing andContinue Reading
This post was written with Dominic Catalano from Anyscale. Organizations building and deploying large-scale AI models often face critical infrastructure challenges that can directly impact their bottom line: unstable training clusters that fail mid-job, inefficient resource utilization driving up costs, and complex distributed computing frameworks requiring specialized expertise. These factorsContinue Reading
This post is cowritten with Gayathri Rengarajan and Harshit Kumar Nyati from PowerSchool. PowerSchool is a leading provider of cloud-based software for K-12 education, serving over 60 million students in more than 90 countries and over 18,000 customers, including more than 90 of the top 100 districts by student enrollmentContinue Reading
This post is cowritten with Thomas Voss and Bernhard Hersberger from Hapag-Lloyd. Hapag-Lloyd is one of the world’s leading shipping companies with more than 308 modern vessels, 11.9 million TEUs (twenty-foot equivalent units) transported per year, and 16,700 motivated employees in more than 400 offices in 139 countries. They connectContinue Reading
This post was written with Sarah Ostermeier from Comet. As enterprise organizations scale their machine learning (ML) initiatives from proof of concept to production, the complexity of managing experiments, tracking model lineage, and managing reproducibility grows exponentially. This is primarily because data scientists and ML engineers constantly explore different combinationsContinue Reading
Organizations building custom machine learning (ML) models often have specialized requirements that standard platforms can’t accommodate. For example, healthcare companies need specific environments to protect patient data while meeting HIPAA compliance, financial institutions require specific hardware configurations to optimize proprietary trading algorithms, and research teams need flexibility to experiment withContinue Reading
