AWS Machine Learning Blog

JUNE 18, 2024

This manual synchronization process, hindered by disparate data formats, is resource-intensive, limiting the potential for widespread data orchestration. The platform, although functional, deals with CSV and JSON files containing hundreds of thousands of rows from various manufacturers, demanding substantial effort for data ingestion.

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Generative AI Data Ingestion Python LLM 115

IBM Journey to AI blog

SEPTEMBER 20, 2023

These include data ingestion, data selection, data pre-processing, FM pre-training, model tuning to one or more downstream tasks, inference serving, and data and AI model governance and lifecycle management—all of which can be described as FMOps. IBM watsonx consists of the following: IBM watsonx.ai

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Unite.AI

OCTOBER 14, 2024

Oftentimes, this requires implementing a “hot” part of the initial data ingest, or landing zone where applications and users can work as fast as possible. How does Quantum plan to stay ahead in the rapidly evolving AI and data management landscape? In today’s world, being merely a “storage provider” is not enough.

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Unite.AI

JUNE 4, 2024

Although migration work is a key component of our business, it’s the data platform engagements that really stand out when you’re talking about value to the business. This led to inconsistent data standards and made it difficult for them to gain actionable insights. The impact of these efforts was transformative.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

The teams built a new data ingestion mechanism, allowing the CTR files to be jointly delivered with the audio file to an S3 bucket. Principal and AWS collaborated on a new AWS Lambda function that was added to the Step Functions workflow.

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Snorkel AI

JANUARY 8, 2025

In todays fast-paced AI landscape, seamless integration between data platforms and AI development tools is critical. At Snorkel, weve partnered with Databricks to create a powerful synergy between their data lakehouse and our Snorkel Flow AI data development platform.

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AWS Machine Learning Blog

FEBRUARY 27, 2024

Axfood has a structure with multiple decentralized data science teams with different areas of responsibility. Together with a central data platform team, the data science teams bring innovation and digital transformation through AI and ML solutions to the organization.

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AWS Machine Learning Blog

DECEMBER 18, 2023

In this post, we will explore the potential of using MongoDB’s time series data and SageMaker Canvas as a comprehensive solution. MongoDB Atlas MongoDB Atlas is a fully managed developer data platform that simplifies the deployment and scaling of MongoDB databases in the cloud.

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NVIDIA

MARCH 18, 2025

While a traditional data center typically handles diverse workloads and is built for general-purpose computing, AI factories are optimized to create value from AI. They orchestrate the entire AI lifecycle from data ingestion to training, fine-tuning and, most critically, high-volume inference.

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TransOrg Analytics

SEPTEMBER 24, 2024

Snowflake Data Engineering Solutions Maximize the potential of your data with Snowflakes data cloud. Our services harness Snowflakes powerful features to architect, build, and manage a modern data platform. Secure Data Sharing: Share data securely within and across organizations.

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TransOrg Analytics

SEPTEMBER 24, 2024

Snowflake Data Engineering Solutions Maximize the potential of your data with Snowflake’s data cloud. Our services harness Snowflake’s powerful features to architect, build, and manage a modern data platform. Secure Data Sharing: Share data securely within and across organizations.

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ETL LLM Data Ingestion Automation 52

Pickl AI

DECEMBER 16, 2024

Whether you aim for comprehensive data integration or impactful visual insights, this comparison will clarify the best fit for your goals. Key Takeaways Microsoft Fabric is a full-scale data platform, while Power BI focuses on visualising insights. Fabric suits large enterprises; Power BI fits team-level reporting needs.

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ETL Data Ingestion Data Integration Machine Learning 52

The MLOps Blog

JANUARY 26, 2024

Keeping track of how exactly the incoming data (the feature pipeline’s input) has to be transformed and ensuring that each model receives the features precisely how it saw them during training is one of the hardest parts of architecting ML systems. This is where feature stores come in. What is a feature store?

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Mlearning.ai

FEBRUARY 16, 2023

Arranging Efficient Data Streams Modern companies typically receive data from multiple sources. Therefore, quick data ingestion for instant use can be challenging. Attend Snowflake University Snowflake has an online university that aims to educate users with all levels of expertise through a variety of courses.

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AWS Machine Learning Blog

MARCH 29, 2023

Data ingestion HAYAT HOLDING has a state-of-the art infrastructure for acquiring, recording, analyzing, and processing measurement data. Two types of data sources exist for this use case. HAYAT KIMYA integrated the ML solution in one of its plants.

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Pickl AI

OCTOBER 10, 2024

Its drag-and-drop interface makes it user-friendly, allowing data engineers to build complex workflows without extensive coding knowledge. Nifi excels in data ingestion, routing, transformation, and system-to-system data flow management.

Data Quality 52

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The MLOps Blog

MARCH 15, 2023

A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. Data Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.

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Pickl AI

JUNE 29, 2023

Advantages of Using Splunk Real-time Visibility One of the significant advantages of Splunk is its ability to provide real-time data visibility. Thus, it lets users gain insights from vast data in real time. Additionally, it also supports a host of data formats. Thereby enabling faster decision-making and problem-solving.

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Bugra Akyildiz

FEBRUARY 24, 2024

Streaming data platforms: Apache Kafka and Apache Flink enable real-time ingestion and processing of user actions, clickstream data, and product catalogs, feeding fresh data to the models.

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ODSC - Open Data Science

SEPTEMBER 29, 2023

Arjuna Chala, associate vice president, HPCC Systems For those not familiar with the HPCC Systems data lake platform, can you describe your organization and the development history behind HPCC Systems? They were interested in creating a data platform capable of managing a sizable number of datasets.

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ODSC - Open Data Science

JULY 19, 2023

Data Estate: This element represents the organizational data estate, potential data sources, and targets for a data science project. Data Engineers would be the primary owners of this element of the MLOps v2 lifecycle. The Azure data platforms in this diagram are neither exhaustive nor prescriptive.

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The MLOps Blog

MARCH 12, 2024

Tools range from data platforms to vector databases, embedding providers, fine-tuning platforms, prompt engineering, evaluation tools, orchestration frameworks, observability platforms, and LLM API gateways. Develop the text preprocessing pipeline Data ingestion: Use Unstructured.io

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The MLOps Blog

MARCH 21, 2023

They work with other users to make sure the data reflects the business problem, the experimentation process is good enough for the business, and the results reflect what would be valuable to the business. What do they want to accomplish?

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Machine Learning Data Scientist ML Metadata 69

Flipboard

NOVEMBER 27, 2024

Retrieval Augmented Generation Amazon Bedrock Knowledge Bases gives FMs contextual information from your private data sources for RAG to deliver more relevant, accurate, and customized responses. The RAG workflow consists of two key components: data ingestion and text generation.

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