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The Age of Health Informatics: Part 1

Heartbeat

The Role of Data Scientists and ML Engineers in Health Informatics At the heart of the Age of Health Informatics are data scientists and ML engineers who play a critical role in harnessing the power of data and developing intelligent algorithms.

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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning Blog

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Databricks Databricks is a cloud-native platform for big data 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 big data processing frameworks such as Apache Spark.

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Google experts on practical paths to data-centricity in applied AI

Snorkel AI

We thought we’d structure this more as a conversation where we walk you through some of our thinking around some of the most common themes in data centricity in applied AI. Is more data always better? One of them is that it is really hard to maintain high data quality with rigorous validation.

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Google experts on practical paths to data-centricity in applied AI

Snorkel AI

We thought we’d structure this more as a conversation where we walk you through some of our thinking around some of the most common themes in data centricity in applied AI. Is more data always better? One of them is that it is really hard to maintain high data quality with rigorous validation.

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ML Pipeline Architecture Design Patterns (With 10 Real-World Examples)

The MLOps Blog

Getting a workflow ready which takes your data from its raw form to predictions while maintaining responsiveness and flexibility is the real deal. At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. Synchronous training What is synchronous training architecture?

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