AWS Machine Learning Blog

MARCH 13, 2025

In synchronous orchestration, just like in traditional process automation, a supervisor agent orchestrates the multi-agent collaboration, maintaining a high-level view of the entire process while actively directing the flow of information and tasks. The following diagram illustrates the supervisor agent methodology.

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AI AI Automation LLM 86

IBM Journey to AI blog

NOVEMBER 3, 2023

Our suite of managed integrations offers APIs to automate cluster setup and management: Domains : Link a custom domain to your cluster’s load balancer by using (CIS). Update the Kubernetes secret definition by adding or removing fields or updating the referenced Secrets Manager CRN for a TLS secret.

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Metadata Automation 220

IBM Journey to AI blog

JULY 1, 2024

Whenever anyone talks about data lineage and how to achieve it, the spotlight tends to shine on automation. This is expected, as automating the process of calculating and establishing lineage is crucial to understanding and maintaining a trustworthy system of data pipelines.

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ETL Automation Metadata Business Intelligence 100

AWS Machine Learning Blog

MARCH 18, 2025

SQL is one of the key languages widely used across businesses, and it requires an understanding of databases and table metadata. JSONs inherently structured format allows for clear and organized representation of complex data such as table schemas, column definitions, synonyms, and sample queries.

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LLM Metadata Large Language Models Python 90

AWS Machine Learning Blog

MAY 7, 2024

With a decade of enterprise AI experience, Veritone supports the public sector, working with US federal government agencies, state and local government, law enforcement agencies, and legal organizations to automate and simplify evidence management, redaction, person-of-interest tracking, and eDiscovery.

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Metadata Generative AI Machine Learning Large Language Models 122

AWS Machine Learning Blog

SEPTEMBER 4, 2024

Specifically for the model building stage, Amazon SageMaker Pipelines automates the process by managing the infrastructure and resources needed to process data, train models, and run evaluation tests. This configuration takes the form of a Directed Acyclic Graph (DAG) represented as a JSON pipeline definition.

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DevOps ML Engineer ML Metadata 94

AWS Machine Learning Blog

MARCH 25, 2025

Implement metadata filtering , adding contextual layers to chunk retrieval. For code samples for metadata filtering using Amazon Bedrock Knowledge Bases, refer to the following GitHub repo. However, generating and maintaining large datasets using human annotators is a time-consuming and costly approach.

Prompt Engineer 82

Prompt Engineer Prompt Engineering Metadata Responsible AI 82

DataRobot Blog

MAY 10, 2022

In this post, we will dive deeper into the first component of managing model risk, and look at opportunities at how automation provided by DataRobot brings about efficiencies in the development and implementation of models. . With this definition of model risk, how do we ensure the models we build are technically correct?

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Automation Machine Learning Data Quality Algorithm 64

Marktechpost

JANUARY 15, 2024

Even with the advances in automated subtitling facilitated by Machine Translation (MT) and Automatic Speech Recognition (ASR), automated dubbing is still a laborious and expensive procedure that frequently requires human involvement. It also offers strong metadata support for a range of difficult video operations.

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Automation Metadata ML AI 60

The MLOps Blog

JUNE 4, 2024

GitHub Actions and Neptune are an ideal combination for automating machine-learning model training and experimentation. But, recording metadata is only half the secret to ML modeling success. Once we’ve committed the workflow definition to our repository and pushed it to GitHub, we’ll see our new workflow in the “Actions” tab.

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ML Automation Machine Learning Metadata 59

AWS Machine Learning Blog

AUGUST 22, 2024

It automatically keeps track of model artifacts, hyperparameters, and metadata, helping you to reproduce and audit model versions. In the notebook, we already added the @step decorator at the beginning of each function definition in the cell where the function was defined, as shown in the following code.

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Generative AI Metadata Python ML 114

AWS Machine Learning Blog

OCTOBER 2, 2023

It is architected to automate the entire machine learning (ML) process, from data labeling to model training and deployment at the edge. Automating data labeling Data labeling is an inherently labor-intensive task that involves humans (labelers) to label the data. If you haven’t read it yet, we recommend checking out Part 1.

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Automation DevOps Machine Learning ML 110

Unite.AI

JUNE 20, 2024

Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. Kubernetes provides mechanisms like StatefulSets and Custom Resource Definitions (CRDs) to manage and orchestrate distributed LLM deployments with model parallelism and sharding.

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Large Language Models LLM Metadata BERT 130

AWS Machine Learning Blog

DECEMBER 19, 2023

It’s much more than just automation. Continuous integration/delivery facilitates the automated building, testing, and packaging of the model training pipeline and deploying it into the target execution environment. The automated pipeline includes steps for out-of-the-box model storage and metric tracking.

Machine Learning 121

Machine Learning ML Engineer DevOps ML 121

Unite.AI

JUNE 13, 2024

For me, computer science is like solving a series of intricate puzzles with the added thrill of automation. For explainability, KGs allow us to link answers back to term definitions, data sources, and metrics, providing a verifiable trail that enhances trust and usability. I started with BASIC and quickly moved on to assembly language.

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Explainability Data Integration Metadata Generative AI 130

AWS Machine Learning Blog

JUNE 11, 2024

This feature streamlines the process of launching new instances with the most up-to-date Neuron SDK, enabling you to automate your deployment workflows and make sure you’re always using the latest optimizations. Amazon ECS configuration For Amazon ECS, create a task definition that references your custom Docker image.

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Deep Learning ML Automation Auto-complete 113

AWS Machine Learning Blog

APRIL 12, 2023

Games24x7 employs an automated, data-driven, AI powered framework for the assessment of each player’s behavior through interactions on the platform and flags users with anomalous behavior. There was no mechanism to pass and store the metadata of the multiple experiments done on the model. amazonaws.com/tensorflow-training:2.11.0-cpu-py39-ubuntu20.04-sagemaker",

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Metadata Deep Learning ML Data Science 94

Applied Data Science

MAY 6, 2021

To keep myself sane, I use Airflow to automate tasks with simple, reusable pieces of code for frequently repeated elements of projects, for example: Web scraping ETL Database management Feature building and data validation And much more! link] We finally have the definition of the DAG. It’s a lot of stuff to stay on top of, right?

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Data Scientist Automation Python Data Science 52

ODSC - Open Data Science

SEPTEMBER 27, 2023

Unlike traditional data warehouses or relational databases, data lakes accept data from a variety of sources, without the need for prior data transformation or schema definition. Automated Testing and Validation: Automated testing and validation procedures help detect and rectify any anomalies or inconsistencies resulting from data changes.

Big Data 98

Big Data Metadata ETL Data Science 98

AWS Machine Learning Blog

JANUARY 12, 2023

In this post, The Very Group shows how they use Amazon Comprehend to add a further layer of automated defense on top of policies to design threat modelling into all systems, to prevent PII from being sent in log data to Elasticsearch for indexing. Overview of solution.

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DevOps Natural Language Processing Metadata Automation 122

The MLOps Blog

JANUARY 23, 2023

Machine Learning Operations (MLOps): Overview, Definition, and Architecture” By Dominik Kreuzberger, Niklas Kühl, Sebastian Hirschl Great stuff. If you haven’t read it yet, definitely do so. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”. Came to ML from software.

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DevOps Metadata Software Engineer Data Scientist 59

Snorkel AI

APRIL 2, 2024

Our goal was to automate the process of extracting complex information from extensive legal PDFs, freeing up the bank’s subject matter experts (SMEs) to concentrate on the more enjoyable—and more valuable—parts of their jobs. This helped to better organize the chunks and enhance them with relevant metadata.

Metadata 52

Metadata Large Language Models LLM Data Scientist 52

Snorkel AI

APRIL 2, 2024

Our goal was to automate the process of extracting complex information from extensive legal PDFs, freeing up the bank’s subject matter experts (SMEs) to concentrate on the more enjoyable—and more valuable—parts of their jobs. This helped to better organize the chunks and enhance them with relevant metadata.

Metadata 52

Metadata Data Science Machine Learning Large Language Models 52

AWS Machine Learning Blog

MARCH 26, 2024

In this post, we show how to automate the accounts payable process using Amazon Textract for data extraction. We also provide a reference architecture to build an invoice automation pipeline that enables extraction, verification, archival, and intelligent search. You can visualize the indexed metadata using OpenSearch Dashboards.

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IDP Metadata Data Extraction DevOps 107

deepsense.ai

JULY 30, 2023

Those tools and practices not only help to integrate consecutive steps (see Figure 1) together and make them work smoothly; they also make sure that the whole process is reproducible, automated and properly monitored at each stage – model training as well as model inference. Not the best combination, right?

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Large Language Models LLM Machine Learning Automation 98

AWS Machine Learning Blog

JULY 19, 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. The model registry supports a hierarchical structure for organizing and storing ML models with model metadata information.

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Metadata ML Machine Learning Python 89

AWS Machine Learning Blog

SEPTEMBER 27, 2024

Exposing Anthropic’s Claude 3 Sonnet to multiple CloudFormation templates will allow it to analyze and learn from the structure, resource definitions, parameter configurations, and other essential elements consistently implemented across your organization’s templates. Second, we want to add metadata to the CloudFormation template.

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Metadata Generative AI LLM Large Language Models 116

Marktechpost

JUNE 16, 2023

An online store’s full customer service and stock-keeping processes can now be automated. You get to choose which processes are automated. AI creates high-definition images that seem like the originals, reviving photography. This article reviews the best artificial intelligence tools for an online store in 2023.

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AI Tools Artificial Intelligence Artificial Intelligence Automation 107

AWS Machine Learning Blog

SEPTEMBER 24, 2024

It also enables operational capabilities including automated testing, conversation analytics, monitoring and observability, and LLM hallucination prevention and detection. “We Leave the four entries for Index Details at their default values (index name, vector field name, metadata field name, and text field name). seconds or less.

Generative AI 93

Generative AI Auto-complete LLM Natural Language Processing 93

AWS Machine Learning Blog

AUGUST 20, 2024

Amazon SageMaker Ground Truth significantly reduces the cost and time required for labeling data by integrating human annotators with machine learning to automate the labeling process. You can call the SageMaker ListWorkteams or DescribeWorkteam APIs to view workteams’ metadata, including the WorkerAccessConfiguration.

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Software Engineer ML Machine Learning Metadata 102

AWS Machine Learning Blog

APRIL 18, 2023

RallyPoint has identified the transition period to a civilian career as a major opportunity to improve the quality of life for this population by creating automated and compelling job recommendations. For the definitions of all available offline metrics, refer to Metric definitions. This was stored in S3.

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Machine Learning ML Computer Vision Metadata 81

AWS Machine Learning Blog

AUGUST 26, 2024

We envision a future where AI seamlessly integrates into our teams’ workflows, automating repetitive tasks, providing intelligent recommendations, and freeing up time for more strategic, high-value interactions. Role context – Start each prompt with a clear role definition.

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Generative AI LLM AI AI 117

AWS Machine Learning Blog

SEPTEMBER 1, 2023

After the completion of the research phase, the data scientists need to collaborate with ML engineers to create automations for building (ML pipelines) and deploying models into production using CI/CD pipelines. All the produced models and code automation are stored in a centralized tooling account using the capability of a model registry.

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Generative AI Prompt Engineer Prompt Engineering ML 120

ODSC - Open Data Science

SEPTEMBER 21, 2023

Yes, these things are part of any job in technology, and they can definitely be super fun, but you have to be strategic about how you spend your time and always be aware of your value proposition. It includes your data for training, your results from running your models, your artifacts, and important metadata.

Machine Learning 98

Machine Learning ML Engineer ML Data Science 98

AWS Machine Learning Blog

JULY 8, 2024

This automated solution helps you get started quickly by providing all the code and configuration necessary to generate your unique images—all you need is images of your subject. However, this solution uses the equivalent GUI parameters as a pre-configured TOML file to automate the entire Stable Diffusion XL fine-tuning process.

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ML Automation DevOps Software Engineer 114

Pickl AI

JUNE 28, 2023

Moreover, these tools are designed to automate tasks like generating SQL scripts, documenting metadata and others. This automation boosts productivity and also saves time. Data Dictionary A data dictionary is a repository of metadata. It allows users to document and manage metadata efficiently.

Metadata 52

Metadata Data Integration Automation Software Development 52

AWS Machine Learning Blog

OCTOBER 8, 2024

SageMaker AutoMLV2 is part of the SageMaker Autopilot suite, which automates the end-to-end machine learning workflow from data preparation to model deployment. All other columns in the dataset are optional and can be used to include additional time-series related information or metadata about each item.

Machine Learning 88

Machine Learning Auto-complete Auto-classification Metadata 88