Data Platform Engineer

Builds scalable pipelines, transforms and loads data into structures complete with metadata that can be readily consumed by DS. Data, Analytics, and Machine Learning (ML) will change data ingestion, system requirements and performance, and customer experiences. Then supplement the skills with architecture, data, and storage support advanced machine learning modeling and intelligence workloads Requirements ML Building Blocks: Services and Terminology Understand both the machine learning stack and the terms and processes that build a good foundation in machine learning.

Process Model: CRISP-DM on the AWS Stack Walk through the CRISP-DM methodology and framework and then apply the model's six phases to your daily work. Data Analytics Fundamentals The process for planning data analysis solutions and the various data analytic processes that are involved. This course takes you through five key factors that indicate the need for specific AWS services in collecting, processing, analyzing, and presenting your data.

Machine Learning Data Readiness Focuses on the concept of data readiness in the context of machine learning (ML). Determine data readiness and identify when to employ data readiness as part of your ML process. Storage Deep Dives Architect and manage highly available solutions, with a focus on AWS storage services. Machine Learning Security Secure applications and environments with specific topics detailing NACLs, security groups, AWS identity and access management, and encryption key management.

Big Data on AWS Cloud-based big data solutions such as Amazon Elastic Map. Reduce (EMR), Amazon Redshift, Amazon Kinesis, and the rest of the AWS big data platform.