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Real value, real time: Production AI with Amazon SageMaker and Tecton

AWS Machine Learning Blog

It seems straightforward at first for batch data, but the engineering gets even more complicated when you need to go from batch data to incorporating real-time and streaming data sources, and from batch inference to real-time serving. Without the capabilities of Tecton , the architecture might look like the following diagram.

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9 Careers You Could Go into With a Data Science Degree

Smart Data Collective

In this role, you would perform batch processing or real-time processing on data that has been collected and stored. As a data engineer, you could also build and maintain data pipelines that create an interconnected data ecosystem that makes information available to data scientists. Data Analyst.

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Accelerate disaster response with computer vision for satellite imagery using Amazon SageMaker and Amazon Augmented AI

AWS Machine Learning Blog

For more information, refer to Preview: Use Amazon SageMaker to Build, Train, and Deploy ML Models Using Geospatial Data. The solution is then able to make predictions on the rest of the training data, and route lower-confidence results for human review. In the following sections, we dive into each pipeline in more detail.

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What are the Biggest Challenges with Migrating to Snowflake?

phData

Walking you through the biggest challenges we have found when migrating our customer’s data from a legacy system to Snowflake. Background Information on Migrating to Snowflake So you’ve decided to move from your current data warehousing solution to Snowflake, and you want to know what challenges await you.

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Using Fivetran’s New Hybrid Architecture to Replicate Data In Your Cloud Environment

phData

As data and AI continue to dominate today’s marketplace, the ability to securely and accurately process and centralize that data is crucial to an organization’s long-term success. Fivetran’s Hybrid Architecture allows an organization to maintain ownership and control of its data through the entire data pipeline.

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LLMOps: What It Is, Why It Matters, and How to Implement It

The MLOps Blog

While there are many similarities with MLOps, LLMOps is unique because it requires specialized handling of natural-language data, prompt-response management, and complex ethical considerations. Retrieval Augmented Generation (RAG) enables LLMs to extract and synthesize information like an advanced search engine.

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Generative AI for agriculture: How Agmatix is improving agriculture with Amazon Bedrock

AWS Machine Learning Blog

Focused on addressing the challenge of agricultural data standardization, Agmatix has developed proprietary patented technology to harmonize and standardize data, facilitating informed decision-making in agriculture. The first step in developing and deploying generative AI use cases is having a well-defined data strategy.

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