Best practices and design patterns for building machine learning workflows with Amazon SageMaker Pipelines

Amazon SageMaker Pipelines is a fully managed AWS service for building and orchestrating machine learning (ML) workflows. SageMaker Pipelines offers ML application developers the ability to orchestrate different steps of the ML workflow, including data loading, data transformation, training, tuning, …

Analyze Amazon SageMaker spend and determine cost optimization opportunities based on usage, Part 3: Processing and Data Wrangler jobs

In 2021, we launched AWS Support Proactive Services as part of the AWS Enterprise Support plan. Since its introduction, we’ve helped hundreds of customers optimize their workloads, set guardrails, and improve the visibility of their machine learning (ML) workloads’ cost …

How OCX Cognition reduced ML model development time from weeks to days and model update time from days to real time using AWS Step Functions and Amazon SageMaker

This post was co-authored by Brian Curry (Founder and Head of Products at OCX Cognition) and Sandhya MN (Data Science Lead at InfoGain)

OCX Cognition is a San Francisco Bay Area-based startup, offering a commercial B2B software as a service …

Publish predictive dashboards in Amazon QuickSight using ML predictions from Amazon SageMaker Canvas

Understanding business trends, customer behavior, sales revenue, increase in demand, and buyer propensity all start with data. Exploring, analyzing, interpreting, and finding trends in data is essential for businesses to achieve successful outcomes.

Business analysts play a pivotal role in …

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