Accelerate innovation cycles with a comprehensive set of AI and ML services in the AWS cloud.
Machine learning is revolutionizing the way businesses operate by automating complex processes and solving previously intractable problems. However, building and productionizing AI-based systems can pose several challenges that often leave machine learning initiatives stuck in the prototyping stage, frequently due to extensive infrastructure and scalability requirements or lack of technical expertise. AWS alleviates many of these challenges through pre-built services that abstract away many of the complex underlying technical details, allowing users to focus on the data and models.
Adastra’s AI services enable you to fully leverage the suite of machine learning capabilities on AWS to both build and deploy production-grade machine learning models in the cloud. We can assess your organization’s analytics goals and design an environment that supports end-to-end AI/ML development, from data preparation, model development, training/tuning, deployment, and management. Our team of data science experts can build a wide range of models to support your business-specific use cases and establish frameworks and best practices for future development.
Conduct a series of discovery sessions with relevant stakeholders to gain a thorough understanding of your machine learning and AI goals, aligned to specific business outcomes and data assets.
Establish a SageMaker environment to enable data preparation and development of AI/ML models, with connectivity to key data stores.
Iteratively prototype models to obtain desired accuracy, including feature augmentation, feature engineering, and model development cycles. Incorporate pre-built AWS ML services depending on the specific use case. Leverage SageMaker Model Registry to catalog and manage model versions.
Leverage SageMaker endpoints to deploy machine learning (ML) models for batches of real-time inference.
Measure model performance and quickly identify when issues arise, such as model degradation or data drift. Automatically and continuously retrain models to ensure the most accurate predictions.
Establish patterns and best practices for model development and production to enable faster time-to-market for new use cases.
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