AWS Machine Learning Blog

APRIL 7, 2025

Amazon Bedrock Knowledge Bases offers fully managed, end-to-end Retrieval Augmented Generation (RAG) workflows to create highly accurate, low-latency, secure, and custom generative AI applications by incorporating contextual information from your companys data sources.

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NVIDIA

MARCH 19, 2025

AI has been shaping the media and entertainment industry for decades, from early recommendation engines to AI-driven editing and visual effects automation. Sixth-Generation NVIDIA NVDEC: Provides up to double H.264 264 decoding throughput and offers support for 4:2:2 H.264 264 and HEVC decode.

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AWS Machine Learning Blog

MAY 7, 2024

If you prefer to generate post call recording summaries with Amazon Bedrock rather than Amazon SageMaker, checkout this Bedrock sample solution. The service allows for simple audio data ingestion, easy-to-read transcript creation, and accuracy improvement through custom vocabularies.

Automation 125

Automation Auto-complete DevOps UX Design 125

AWS Machine Learning Blog

NOVEMBER 26, 2023

Today, we are excited to announce three launches that will help you enhance personalized customer experiences using Amazon Personalize and generative AI. Generative AI is quickly transforming how enterprises do business. Amazon Personalize has helped us achieve high levels of automation in content customization.

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AWS Machine Learning Blog

FEBRUARY 7, 2025

This deployment guide covers the steps to set up an Amazon Q solution that connects to Amazon Simple Storage Service (Amazon S3) and a web crawler data source, and integrates with AWS IAM Identity Center for authentication. An AWS CloudFormation template automates the deployment of this solution.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Rockets legacy data science environment challenges Rockets previous data science solution was built around Apache Spark and combined the use of a legacy version of the Hadoop environment and vendor-provided Data Science Experience development tools. Rockets legacy data science architecture is shown in the following diagram.

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AWS Machine Learning Blog

AUGUST 5, 2024

Building and deploying these components can be complex and error-prone, especially when dealing with large-scale data and models. Solution overview The solution provides an automated end-to-end deployment of a RAG workflow using Knowledge Bases for Amazon Bedrock. Choose Sync to initiate the data ingestion job.

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AWS Machine Learning Blog

JULY 11, 2024

Agents for Amazon Bedrock automates the prompt engineering and orchestration of user-requested tasks. In this blog post, we explore how Agents for Amazon Bedrock can be used to generate customized, organization standards-compliant IaC scripts directly from uploaded architecture diagrams.

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Data Ingestion DevOps Prompt Engineer Prompt Engineering 128

IBM Journey to AI blog

SEPTEMBER 20, 2023

Large language models (LLMs) have taken the field of AI by storm. Scale and accelerate the impact of AI There are several steps to building and deploying a foundational model (FM). brings new generative AI capabilities—powered by FMs and traditional machine learning (ML)—into a powerful studio spanning the AI lifecycle.

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AWS Machine Learning Blog

FEBRUARY 5, 2025

There is also an automated ingestion job from Slack conversation data to the S3 bucket powered by an AWS Lambda function. The architectures strengths lie in its consistency across environments, automatic data ingestion processes, and comprehensive monitoring capabilities.

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AWS Machine Learning Blog

AUGUST 28, 2024

This post demonstrates how to seamlessly automate the deployment of an end-to-end RAG solution using Knowledge Bases for Amazon Bedrock and the AWS Cloud Development Kit (AWS CDK), enabling organizations to quickly set up a powerful question answering system. Choose Sync to initiate the data ingestion job.

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John Snow Labs

FEBRUARY 18, 2025

Combining healthcare-specific LLMs along with a terminology service and scalable data ingestion pipelines, it excels in complex queries and is ideal for organizations seeking OMOP data enrichment.

Generative AI 52

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AWS Machine Learning Blog

NOVEMBER 15, 2023

In order analyze the calls properly, Principal had a few requirements: Contact details: Understanding the customer journey requires understanding whether a speaker is an automated interactive voice response (IVR) system or a human agent and when a call transfer occurs between the two.

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AWS Machine Learning Blog

JULY 17, 2024

Amazon Q Business is a fully managed, secure, generative-AI powered enterprise chat assistant that enables natural language interactions with your organization’s data. These components include an Amazon S3 data source connector, required IAM roles, and Amazon Q Business web experience.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Customers across all industries run IDP workloads on AWS to deliver business value by automating use cases such as KYC forms, tax documents, invoices, insurance claims, delivery reports, inventory reports, and more. Effectively manage your data and its lifecycle Data plays a key role throughout your IDP solution.

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Unite.AI

JUNE 4, 2024

Lastly, the integration of generative AI is set to revolutionize business operations across various industries. Google Cloud’s AI and machine learning services, including the new generative AI models, empower businesses to harness advanced analytics, automate complex processes, and enhance customer experiences.

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Marktechpost

DECEMBER 3, 2024

As generative AI continues to grow, the need for an efficient, automated solution to transform various data types into an LLM-ready format has become even more apparent. Meet MegaParse : an open-source tool for parsing various types of documents for LLM ingestion. Check out the GitHub Page.

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AWS Machine Learning Blog

FEBRUARY 27, 2024

Automation of building new projects based on the template is streamlined through AWS Service Catalog , where a portfolio is created, serving as an abstraction for multiple products. The model will be approved by designated data scientists to deploy the model for use in production.

Machine Learning 131

Machine Learning DevOps Data Scientist Data Quality 131

deepsense.ai

JULY 30, 2023

Other steps include: data ingestion, validation and preprocessing, model deployment and versioning of model artifacts, live monitoring of large language models in a production environment, monitoring the quality of deployed models and potentially retraining them. Of course, the desired level of automation is different for each project.

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AWS Machine Learning Blog

AUGUST 21, 2024

This empowers organizations to unlock the full potential of ML and generative AI while maintaining control and oversight over their data assets. He is focused on AI/ML technology, ML model management, and ML governance to improve overall organizational efficiency and productivity. Huong Nguyen is a Sr.

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ODSC - Open Data Science

FEBRUARY 25, 2025

At ODSC East 2025 , were excited to present 12 curated tracks designed to equip data professionals, machine learning engineers, and AI practitioners with the tools they need to thrive in this dynamic landscape. This track will explore how AI and machine learning are accelerating breakthroughs in life sciences.

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Data Scientist Machine Learning Large Language Models ML Engineer 52

Pickl AI

AUGUST 28, 2024

Unified ML Workflow: Vertex AI provides a simplified ML workflow, encompassing data ingestion, analysis, transformation, model training, evaluation, and deployment. This unified approach enables seamless collaboration among data scientists, data engineers, and ML engineers.

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Machine Learning ML Engineer ML Automation 52

Snorkel AI

DECEMBER 2, 2024

When combined with Snorkel Flow, it becomes a powerful enabler for enterprises seeking to harness the full potential of their proprietary data. What the Snorkel Flow + AWS integrations offer Streamlined data ingestion and management: With Snorkel Flow, organizations can easily access and manage unstructured data stored in Amazon S3.

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Data Ingestion Large Language Models LLM Machine Learning 52

ODSC - Open Data Science

DECEMBER 11, 2024

This evolution underscores the demand for innovative platforms that simplify data ingestion and transformation, enabling faster, more reliable decision-making. However, the opaque nature of some AI systems raises concerns about false discoveries, particularly in high-stakes fields like trading.

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Data Ingestion Large Language Models Data Scientist Data Science 52

Topbots

FEBRUARY 19, 2024

Networking Capabilities: Ensure your infrastructure has the networking capabilities to handle large volumes of data transfer. Data Pipeline Management: Set up efficient data pipelines for data ingestion, processing, and management. Maintain Ongoing Monitoring for Model Safety in Your LLM Application.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

In order to train transformer models on internet-scale data, huge quantities of PBAs were needed. In November 2022, ChatGPT was released, a large language model (LLM) that used the transformer architecture, and is widely credited with starting the current generative AI boom.

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AWS Machine Learning Blog

FEBRUARY 7, 2025

Second, the platform gives data science teams the autonomy to create accounts, provision ML resources and access ML resources as needed, reducing resource constraints that often hinder their work. Sovik Kumar Nath is an AI/ML and Generative AI senior solution architect with AWS.

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ML Data Scientist ML Engineer Data Science 63

AWS Machine Learning Blog

OCTOBER 23, 2024

The landscape of enterprise application development is undergoing a seismic shift with the advent of generative AI. This innovative platform empowers employees, regardless of their coding skills, to create generative AI processes and applications through a low-code visual designer.

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AWS Machine Learning Blog

JANUARY 21, 2025

How Amazon SageMaker Canvas can help retail and CPG manufacturers solve their forecasting challenges The combination of a user-friendly UI interface and automated ML technology available in SageMaker Canvas gives users the tools to efficiently build, deploy, and maintain ML models with little to no coding required.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

What Zeta has accomplished in AI/ML In the fast-evolving landscape of digital marketing, Zeta Global stands out with its groundbreaking advancements in artificial intelligence. Using AI, Zeta Global has revolutionized how brands connect with their audiences, offering solutions that aren’t just innovative, but also incredibly effective.

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AWS Machine Learning Blog

SEPTEMBER 19, 2024

Challenges with fine-tuning LLMs Generative AI models offer many promising business use cases. This helps address the requirements of the generative AI fine-tuning lifecycle, from data ingestion and multi-node fine-tuning to inference and evaluation. In this use case, we fine-tune a Meta Llama 3.1

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ML Large Language Models Machine Learning Data Ingestion 93

AWS Machine Learning Blog

NOVEMBER 20, 2024

In the future, high automation will play a crucial role in this domain. Using generative AI allows businesses to improve accuracy and efficiency in email management and automation. The combination of retrieval augmented generation (RAG) and knowledge bases enhances automated response accuracy.

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Automation Data Ingestion Auto-complete Software Engineer 105

Flipboard

NOVEMBER 27, 2024

Seamless integration of customer experience, collaboration tools, and relevant data is the foundation for delivering knowledge-based productivity gains. The RAG workflow consists of two key components: data ingestion and text generation.

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Flipboard

NOVEMBER 26, 2024

Amazon Bedrock Agents helps accelerate generative AI application development by orchestrating multistep tasks. Additionally, agents streamline workflows and automate repetitive tasks. With the power of AI automation, you can boost productivity and reduce costs.

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AWS Machine Learning Blog

DECEMBER 4, 2024

AWS customers use Amazon Kendra with large language models (LLMs) to quickly create secure, generative AI –powered conversational experiences on top of your enterprise content. This approach combines a retriever with an LLM to generate responses. A retriever is responsible for finding relevant documents based on the user query.

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Metadata Generative AI Data Ingestion Software Engineer 93

AWS Machine Learning Blog

NOVEMBER 7, 2024

Regardless of the models used, they all include data preprocessing, training, and inference over several billions of records containing weekly data spanning multiple years and markets to produce forecasts. A fully automated production workflow The MLOps lifecycle starts with ingesting the training data in the S3 buckets.

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ML Machine Learning Automation Data Scientist 102

AWS Machine Learning Blog

OCTOBER 11, 2024

Customers across all industries are experimenting with generative AI to accelerate and improve business outcomes. They contribute to the effectiveness and feasibility of generative AI applications across various domains.

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