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14 Powerful Techniques Defining the Evolution of Embedding

Analytics Vidhya

Now, when we talk about the evolution of embeddings, we mean numerical snapshots that capture not just which words appear but what they really mean, how they relate to each other […] The post 14 Powerful Techniques Defining the Evolution of Embedding appeared first on Analytics Vidhya. Well, things have come a long way since then.

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Build a high-performance quant research platform with Apache Iceberg

AWS Big Data

Iceberg provides time travel and snapshotting capabilities out of the box to manage lookahead bias that could be embedded in the data (such as delayed data delivery). Icebergs time travel capability is driven by a concept called snapshots , which are recorded in metadata files.

Metadata 111
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Proposals for model vulnerability and security

O'Reilly on Data

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. Data poisoning attacks. General concerns.

Modeling 278
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Run Apache XTable in AWS Lambda for background conversion of open table formats

AWS Big Data

In practice, OTFs are used in a broad range of analytical workloads, from business intelligence to machine learning. Querying all snapshots, we can see that we created three snapshots with overwrites after the initial one. Moreover, they can be combined to benefit from individual strengths.

Metadata 105
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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

Much has been written about struggles of deploying machine learning projects to production. This approach has worked well for software development, so it is reasonable to assume that it could address struggles related to deploying machine learning in production too. However, the concept is quite abstract. Versioning.

IT 364
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Monitoring Apache Iceberg metadata layer using AWS Lambda, AWS Glue, and AWS CloudWatch

AWS Big Data

This enables more informed decision-making and innovative insights through various analytics and machine learning applications. History and versioning : Iceberg’s versioning feature captures every change in table metadata as immutable snapshots, facilitating data integrity, historical views, and rollbacks.

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Simplify data integration with AWS Glue and zero-ETL to Amazon SageMaker Lakehouse

AWS Big Data

Extract, transform, and load (ETL) is the process of combining, cleaning, and normalizing data from different sources to prepare it for analytics, artificial intelligence (AI), and machine learning (ML) workloads. About the authors Shovan Kanjilal is a Senior Analytics and Machine Learning Architect with Amazon Web Services.