AWS Machine Learning Blog

FEBRUARY 27, 2024

In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker. This is a guest post written by Axfood AB.

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AWS Machine Learning Blog

SEPTEMBER 18, 2023

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. Ingest the prepared data into the feature group by using the Boto3 SDK.

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Pickl AI

JULY 25, 2024

Summary: Data ingestion is the process of collecting, importing, and processing data from diverse sources into a centralised system for analysis. This crucial step enhances data quality, enables real-time insights, and supports informed decision-making. This is where data ingestion comes in.

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AI News

SEPTEMBER 29, 2024

As AI models grow and data volumes expand, databases must scale horizontally, to allow organisations to add capacity without significant downtime or performance degradation. Additionally, they accelerate time-to-market for AI-driven innovations by enabling rapid data ingestion and retrieval, facilitating faster experimentation.

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AWS Machine Learning Blog

OCTOBER 23, 2024

Mani Khanuja is a Tech Lead – Generative AI Specialist, author of the book Applied Machine Learning and High Performance Computing on AWS , and a member of the Board of Directors for Women in Manufacturing Education Foundation Board. She enjoys spending time with family and friends, reading, playing volleyball, and teaching others.

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AI News

AUGUST 29, 2023

If you think about building a data pipeline, whether you’re doing a simple BI project or a complex AI or machine learning project, you’ve got data ingestion, data storage and processing, and data insight – and underneath all of those four stages, there’s a variety of different technologies being used,” explains Faruqui.

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Marktechpost

AUGUST 8, 2023

In the ever-evolving landscape of machine learning, feature management has emerged as a key pain point for ML Engineers at Airbnb. Airbnb recognized the need for a solution that could streamline feature data management, provide real-time updates, and ensure consistency between training and production environments.

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AWS Machine Learning Blog

AUGUST 21, 2024

Amazon DataZone makes it straightforward for engineers, data scientists, product managers, analysts, and business users to access data throughout an organization so they can discover, use, and collaborate to derive data-driven insights. He supports enterprise customers migrate and modernize their workloads on AWS cloud.

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Snorkel AI

DECEMBER 2, 2024

The SageMaker Jumpstart machine learning hub offers a suite of tools for building, training, and deploying machine learning models at scale. When combined with Snorkel Flow, it becomes a powerful enabler for enterprises seeking to harness the full potential of their proprietary data.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks. Apache Hive was used to provide a tabular interface to data stored in HDFS, and to integrate with Apache Spark SQL.

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AWS Machine Learning Blog

MARCH 22, 2023

Universities and other higher learning institutions have collected massive amounts of data over the years, and now they are exploring options to use that data for deeper insights and better educational outcomes. You can use machine learning (ML) to generate these insights and build predictive models.

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Snorkel AI

JANUARY 8, 2025

At Snorkel, weve partnered with Databricks to create a powerful synergy between their data lakehouse and our Snorkel Flow AI data development platform. Ingesting raw data from Databricks into Snorkel Flow Efficient data ingestion is the foundation of any machine learning project.

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Unite.AI

OCTOBER 25, 2023

It demands substantial effort in data preparation, coupled with a difficult optimization procedure, necessitating a certain level of machine learning expertise. Data Indexes : Post data ingestion, LlamaIndex assists in indexing this data into a retrievable format.

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AWS Machine Learning Blog

MARCH 8, 2023

MPII is using a machine learning (ML) bid optimization engine to inform upstream decision-making processes in power asset management and trading. This solution helps market analysts design and perform data-driven bidding strategies optimized for power asset profitability.

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Marktechpost

JUNE 13, 2024

Data scientists often spend up to 80% of their time on data engineering in data science projects. Objective of Data Engineering: The main goal is to transform raw data into structured data suitable for downstream tasks such as machine learning.

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IBM Journey to AI blog

SEPTEMBER 27, 2023

Over the years, an overwhelming surplus of security-related data and alerts from the rapidly expanding cloud digital footprint has put an enormous load on security solutions that need greater scalability, speed and efficiency than ever before.

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IBM Journey to AI blog

SEPTEMBER 11, 2023

enhances data management through automated insights generation, self-tuning performance optimization and predictive analytics. It leverages machine learning algorithms to continuously learn and adapt to workload patterns, delivering superior performance and reducing administrative efforts.

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AWS Machine Learning Blog

AUGUST 9, 2024

Deltek is continuously working on enhancing this solution to better align it with their specific requirements, such as supporting file formats beyond PDF and implementing more cost-effective approaches for their data ingestion pipeline. The first step is data ingestion, as shown in the following diagram. What is RAG?

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Towards AI

NOVEMBER 20, 2023

Creating RESTful APIs and services with JuliaImage Generated by AI on Gencraft U+1F44B Hello and welcome back to our series to explore the Julia programming language to develop end-to-end machine learning (ML) projects. In this post, we will introduce a package that could help develop RESTful APIs in Julia U+1F680.

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The MLOps Blog

JANUARY 26, 2024

Training and evaluating models is just the first step toward machine-learning success. For this, we have to build an entire machine-learning system around our models that manages their lifecycle, feeds properly prepared data into them, and sends their output to downstream systems. But what is an ML pipeline?

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AWS Machine Learning Blog

FEBRUARY 19, 2024

RAG architecture involves two key workflows: data preprocessing through ingestion, and text generation using enhanced context. The data ingestion workflow uses LLMs to create embedding vectors that represent semantic meaning of texts. It offers fully managed data ingestion and text generation workflows.

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Flipboard

MARCH 3, 2023

Data proliferation has become a norm and as organizations become more data driven, automating data pipelines that enable data ingestion, curation, …

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AWS Machine Learning Blog

APRIL 26, 2024

With this new capability, you can ask questions of your data without the overhead of setting up a vector database or ingesting data, making it effortless to use your enterprise data. You can now interact with your documents in real time without prior data ingestion or database configuration.

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AWS Machine Learning Blog

AUGUST 5, 2024

Choose Sync to initiate the data ingestion job. After data synchronization is complete, select the desired FM to use for retrieval and generation (it requires model access to be granted to this FM in Amazon Bedrock before using). He specializes in generative AI, machine learning, and system design.

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Towards AI

NOVEMBER 3, 2024

📔This is a beginner-friendly tutorial so quick notes on Retrieval Augmented Generation (RAG) and LangChain before we get started with the hands-on.

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AWS Machine Learning Blog

FEBRUARY 5, 2025

The architectures strengths lie in its consistency across environments, automatic data ingestion processes, and comprehensive monitoring capabilities. To learn more about the AWS services used in this solution, refer to the Amazon Q User Guide , Deploy a Slack gateway for Amazon Bedrock , and the Amazon Kendra Developer Guide.

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ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

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Flipboard

DECEMBER 31, 2024

Empowering AI teams with seamless integration, rapid prototyping, and robust data handling through a serverless, RAG-as-a-Service platform.

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AWS Machine Learning Blog

AUGUST 28, 2024

Choose Sync to initiate the data ingestion job. After the data ingestion job is complete, choose the desired FM to use for retrieval and generation. About the Authors Sandeep Singh is a Senior Generative AI Data Scientist at Amazon Web Services, helping businesses innovate with generative AI.

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AWS Machine Learning Blog

JUNE 18, 2024

More than 170 tech teams used the latest cloud, machine learning and artificial intelligence technologies to build 33 solutions. This manual synchronization process, hindered by disparate data formats, is resource-intensive, limiting the potential for widespread data orchestration.

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Unite.AI

DECEMBER 5, 2023

In BI systems, data warehousing first converts disparate raw data into clean, organized, and integrated data, which is then used to extract actionable insights to facilitate analysis, reporting, and data-informed decision-making. The post A Beginner’s Guide to Data Warehousing appeared first on Unite.AI.

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Pickl AI

AUGUST 28, 2024

Summary: Vertex AI is a comprehensive platform that simplifies the entire Machine Learning lifecycle. From data preparation and model training to deployment and management, Vertex AI provides the tools and infrastructure needed to build intelligent applications.

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Marktechpost

SEPTEMBER 26, 2023

AI Copilots can learn from the developer’s coding patterns and adapt to their preferences over time, which enhances the user’s assistance experience. AI Copilots leverage various artificial intelligence, natural language processing (NLP), machine learning, and code analysis. Check out the GitHub and Documentation.

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Marktechpost

JUNE 16, 2024

This solution addresses the complexities data engineering teams face by providing a unified platform for data ingestion, transformation, and orchestration. Image Source Key Components of LakeFlow: LakeFlow Connect: This component offers point-and-click data ingestion from numerous databases and enterprise applications.

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IBM Journey to AI blog

SEPTEMBER 20, 2023

Foundational models (FMs) are marking the beginning of a new era in machine learning (ML) and artificial intelligence (AI) , which is leading to faster development of AI that can be adapted to a wide range of downstream tasks and fine-tuned for an array of applications. Large language models (LLMs) have taken the field of AI by storm.

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AWS Machine Learning Blog

SEPTEMBER 19, 2023

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. This will show all the runs.

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IBM Journey to AI blog

OCTOBER 30, 2023

However, these were often stand-alone solutions that didn’t address the underlying issues of siloed, incomplete or duplicative data. Canada used integrated public health information systems, like Panorama, for seamless data ingestion, cleansing and import processing.

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