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About the Authors Rushabh Lokhande is a Senior Data & MLEngineer with AWS Professional Services Analytics Practice. He helps customers implement bigdata, machine learning, analytics solutions, and generative AI solutions.
Use case and model governance plays a crucial role in implementing responsibleAI and helps with the reliability, fairness, compliance, and risk management of ML models across use cases in the organization. About the authors Ram Vittal is a Principal ML Solutions Architect at AWS.
It’s a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like Anthropic, Cohere, Meta, Mistral AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsibleAI.
Databricks Databricks is a cloud-native platform for bigdata processing, machine learning, and analytics built using the Data Lakehouse architecture. Delta Lake Delta Lake is an open-source storage layer that provides reliability, ACID transactions, and data versioning for bigdata processing frameworks such as Apache Spark.
Machine Learning Operations (MLOps) can significantly accelerate how data scientists and MLengineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.
BigData and Deep Learning (2010s-2020s): The availability of massive amounts of data and increased computational power led to the rise of BigData analytics. Deep Learning, a subfield of ML, gained attention with the development of deep neural networks.
Being aware of risks fosters transparency and trust in generative AI applications, encourages increased observability, helps to meet compliance requirements, and facilitates informed decision-making by leaders. Learn more about our commitment to ResponsibleAI and additional responsibleAI resources to help our customers.
Amazon SageMaker helps data scientists and machine learning (ML) engineers build FMs from scratch, evaluate and customize FMs with advanced techniques, and deploy FMs with fine-grain controls for generative AI use cases that have stringent requirements on accuracy, latency, and cost. Connect with Hin Yee on LinkedIn.
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