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This lesson introduces the core concepts of ML model deployment on AWS. It begins by differentiating between training and inference workloads, highlighting the unique requirements of each. We will then survey the primary managed deployment options within Amazon SageMaker, using a decision-making framework to understand when to use each. The focus will be on Batch Transform (for offline bulk inference), Asynchronous Inference (for large payloads and long processing times), Serverless Inference (for intermittent traffic), and Real-Time Inference (for low-latency, persistent endpoints). The lesson will also briefly cover using pre-trained AWS AI Services like Rekognition and Comprehend via their APIs as a starting point for ML integration.
Introduction to AWS object storage with Amazon S3. Covers core concepts like buckets, objects, and keys. Detailed exploration of the S3 consistency model (read-after-write for new objects, eventual for updates/deletes). Deep dive into S3 Storage Classes: Standard, Intelligent-Tiering, Standard-IA, One Zone-IA, and the Glacier tiers. The session will focus on the role of S3 in modern data architecture as a scalable, durable, and cost-effective data lake storage layer, emphasizing the schema-on-read approach.
This lesson provides a foundational overview of the history and core concepts of data analytics, leading into the modern AWS data analytics pipeline and the concept of a data lake, based on the provided document 'History of Analytics and Big Data'. The session will start by covering the historical evolution from traditional data warehousing to the current 'New World Order' driven by technologies like Hadoop and cloud computing. We will then dissect the modern analytics pipeline, covering the key stages: Collection, Storage (hot, warm, cold data), Processing, and Visualization (referencing the Gartner Analytics Maturity Model). The majority of the lesson will focus on mapping these conceptual stages to the AWS Big Data Reference Architecture, identifying core services like Amazon S3, AWS Glue, Amazon Kinesis, and Amazon QuickSight. Finally, the lesson will define the data lake concept and briefly outline the five steps to building one on AWS, positioning AWS Lake Formation as a service that simplifies this process. The lesson will conclude with a recommendation to attempt the assessment questions from the chapter to self-evaluate understanding.
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