Big Data, Metadata and ML Engineer - Artificial Intelligence Zone

Build a robust text-to-SQL solution generating complex queries, self-correcting, and querying diverse data sources

AWS Machine Learning Blog

FEBRUARY 28, 2024

Structured Query Language (SQL) is a complex language that requires an understanding of databases and metadata. The solution in this post aims to bring enterprise analytics operations to the next level by shortening the path to your data using natural language. Today, generative AI can enable people without SQL knowledge.

Metadata

Metadata Generative AI LLM NLP

MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

JUNE 27, 2023

Databricks Databricks is a cloud-native platform for big data processing, machine learning, and analytics built using the Data Lakehouse architecture. When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support.

Machine Learning

Machine Learning Metadata Data Scientist Data Quality

Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker

AWS Machine Learning Blog

OCTOBER 20, 2023

Data scientists search and pull features from the central feature store catalog, build models through experiments, and select the best model for promotion. Data scientists create and share new features into the central feature store catalog for reuse.

ML

ML Data Scientist ML Engineer Data Science

Webinars

The Intersection of AI and Sales: Personalization Without Compromise

How to Achieve High-Accuracy Results When Using LLMs

Relevance, Reach, Revenue: How to Turn Marketing Trends From Hype to High-Impact

MORE WEBINARS

Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning Blog

FEBRUARY 13, 2024

Let’s demystify this using the following personas and a real-world analogy: Data and ML engineers (owners and producers) – They lay the groundwork by feeding data into the feature store Data scientists (consumers) – They extract and utilize this data to craft their models Data engineers serve as architects sketching the initial blueprint.

ML

ML Machine Learning ML Engineer Data Scientist

Revolutionizing clinical trials with the power of voice and AI

AWS Machine Learning Blog

MARCH 18, 2025

You will notice the content of this file as JSON with a text transcript available under the key transcripts, along with other metadata. Rushabh Lokhande is a Senior Data & ML Engineer with AWS Professional Services Analytics Practice. He helps customers implement big data, machine learning, and analytics solutions.

LLM

LLM NLP Data Integration AI

Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning Blog

OCTOBER 2, 2024

Additionally, you can enable model invocation logging to collect invocation logs, full request response data, and metadata for all Amazon Bedrock model API invocations in your AWS account. Leveraging her expertise in Computer Vision and Deep Learning, she empowers customers to harness the power of the ML in AWS cloud efficiently.

Generative AI

Generative AI Data Ingestion AI AI

Bundesliga Match Fact Ball Recovery Time: Quantifying teams’ success in pressing opponents on AWS

AWS Machine Learning Blog

MARCH 30, 2023

This allows for seamless communication of positional data and various outputs of Bundesliga Match Facts between containers in real time. The match-related data is collected and ingested using DFL’s DataHub. Both the Lambda function and the Fargate container publish the data for further consumption in the relevant MSK topics.

Machine Learning

Machine Learning Data Scientist Data Science Metadata

Use Amazon SageMaker Model Card sharing to improve model governance

AWS Machine Learning Blog

AUGUST 31, 2023

Model cards are intended to be a single source of truth for business and technical metadata about the model that can reliably be used for auditing and documentation purposes. Ram Vittal is a Principal ML Solutions Architect at AWS. They provide a fact sheet of the model that is important for model governance.

ML

ML Data Scientist Machine Learning Data Science

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

Snorkel AI

JULY 5, 2023

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? And the important thing here is really the predictive signal in the data. Maybe I’ll start us off here Robert?

Large Language Models

Large Language Models Metadata Machine Learning AI

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

Snorkel AI

JULY 5, 2023

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? And the important thing here is really the predictive signal in the data. Maybe I’ll start us off here Robert?

Large Language Models

Large Language Models Metadata Machine Learning AI

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

Snorkel AI

JULY 5, 2023

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? And the important thing here is really the predictive signal in the data. Maybe I’ll start us off here Robert?

Large Language Models

Large Language Models Metadata Machine Learning AI

Use Amazon SageMaker Model Cards sharing to improve model governance

AWS Machine Learning Blog

AUGUST 31, 2023

Model cards are intended to be a single source of truth for business and technical metadata about the model that can reliably be used for auditing and documentation purposes. Ram Vittal is a Principal ML Solutions Architect at AWS. They provide a fact sheet of the model that is important for model governance.

ML

ML Data Scientist Machine Learning Data Science

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

NOVEMBER 14, 2024

However, model governance functions in an organization are centralized and to perform those functions, teams need access to metadata about model lifecycle activities across those accounts for validation, approval, auditing, and monitoring to manage risk and compliance. About the authors Ram Vittal is a Principal ML Solutions Architect at AWS.

ML

ML Machine Learning Auto-complete Auto-classification

Search enterprise data assets using LLMs backed by knowledge graphs

Flipboard

NOVEMBER 27, 2024

Enterprises are facing challenges in accessing their data assets scattered across various sources because of increasing complexities in managing vast amount of data. Traditional search methods often fail to provide comprehensive and contextual results, particularly for unstructured data or complex queries.

Metadata

Metadata Auto-complete Data Discovery ML Engineer

Improve governance of models with Amazon SageMaker unified Model Cards and Model Registry

AWS Machine Learning Blog

NOVEMBER 13, 2024

You can now register machine learning (ML) models in Amazon SageMaker Model Registry with Amazon SageMaker Model Cards , making it straightforward to manage governance information for specific model versions directly in SageMaker Model Registry in just a few clicks.

Metadata

Metadata ML Software Engineer Machine Learning

Artificial Intelligence Zone

Build a robust text-to-SQL solution generating complex queries, self-correcting, and querying diverse data sources

MLOps Landscape in 2023: Top Tools and Platforms

Webinars

Trending Sources

Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker

Webinars

Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

Revolutionizing clinical trials with the power of voice and AI

Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

Bundesliga Match Fact Ball Recovery Time: Quantifying teams’ success in pressing opponents on AWS

Use Amazon SageMaker Model Card sharing to improve model governance

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

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

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

Use Amazon SageMaker Model Cards sharing to improve model governance

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

Search enterprise data assets using LLMs backed by knowledge graphs

Improve governance of models with Amazon SageMaker unified Model Cards and Model Registry

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