Select and configure your infrastructure for machine learning success
Making the right infrastructure decisions is essential to getting your ML models into production at scale and at optimal cost. But how can you really ensure that you have adequate infrastructure to support the compute, network, and storage needs of common ML use cases? Read Propel 4 Common Machine Learning Use Cases into Production for practical insights for setting-up your infrastructure for computer vision, fraud detection, natural language processing, and recommendations.
- Learn about successful results from AWS customers after deploying ML applications
- Get real-world guidance on evaluating your infrastructure and exploring AWS ML infrastructure solutions
- Discover practical solutions to the common challenges you may face as your team moves from model concept to production