AWS Machine Learning Blog

FEBRUARY 5, 2025

The following diagram illustrates the data pipeline for indexing and query in the foundational search architecture. The listing indexer AWS Lambda function continuously polls the queue and processes incoming listing updates. For data handling, 24 data nodes (r6gd.2xlarge.search

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AWS Machine Learning Blog

OCTOBER 23, 2024

SnapLogic uses Amazon Bedrock to build its platform, capitalizing on the proximity to data already stored in Amazon Web Services (AWS). Control plane and data plane implementation SnapLogic’s Agent Creator platform follows a decoupled architecture, separating the control plane and data plane for enhanced security and scalability.

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Smart Data Collective

AUGUST 17, 2022

You can safely use an Apache Kafka cluster for seamless data movement from the on-premise hardware solution to the data lake using various cloud services like Amazon’s S3 and others. It is because you usually see Kafka producers publish data or push it towards a Kafka topic so that the application can consume the data.

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Smart Data Collective

SEPTEMBER 8, 2021

A lot of Open-Source ETL tools house a graphical interface for executing and designing Data Pipelines. It can be used to manipulate, store, and analyze data of any structure. It generates Java code for the Data Pipelines instead of running Pipeline configurations through an ETL Engine.

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Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. To do this, we provide an AWS CloudFormation template to create a stack that contains the resources.

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AWS Machine Learning Blog

APRIL 5, 2024

Solution workflow In this section, we discuss how the different components work together, from data acquisition to spatial modeling and forecasting, serving as the core of the UHI solution. Now, with the specialized geospatial container in SageMaker, managing and running clusters for geospatial processing has become more straightforward.

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AWS Machine Learning Blog

FEBRUARY 24, 2023

AWS recently released Amazon SageMaker geospatial capabilities to provide you with satellite imagery and geospatial state-of-the-art machine learning (ML) models, reducing barriers for these types of use cases. In the following sections, we dive into each pipeline in more detail.

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AWS Machine Learning Blog

DECEMBER 15, 2023

In this post, we discuss how to bring data stored in Amazon DocumentDB into SageMaker Canvas and use that data to build ML models for predictive analytics. Without creating and maintaining data pipelines, you will be able to power ML models with your unstructured data stored in Amazon DocumentDB.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

Learning means identifying and capturing historical patterns from the data, and inference means mapping a current value to the historical pattern. The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference.

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AWS Machine Learning Blog

SEPTEMBER 18, 2023

With Ray and AIR, the same Python code can scale seamlessly from a laptop to a large cluster. The full code can be found on the aws-samples-for-ray GitHub repository. It integrates smoothly with other data processing libraries like Spark, Pandas, NumPy, and more, as well as ML frameworks like TensorFlow and PyTorch.

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Flipboard

NOVEMBER 24, 2023

In this post, we show you how SnapLogic , an AWS customer, used Amazon Bedrock to power their SnapGPT product through automated creation of these complex DSL artifacts from human language. SnapLogic background SnapLogic is an AWS customer on a mission to bring enterprise automation to the world.

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AWS Machine Learning Blog

NOVEMBER 1, 2023

PII Detected tagged documents are fed into Logikcull’s search index cluster for their users to quickly identify documents that contain PII entities. The request is handled by Logikcull’s application servers hosted on Amazon EC2 and the servers communicates with the search index cluster to find the documents.

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ODSC - Open Data Science

FEBRUARY 17, 2023

Cloud Computing, APIs, and Data Engineering NLP experts don’t go straight into conducting sentiment analysis on their personal laptops. TensorFlow is desired for its flexibility for ML and neural networks, PyTorch for its ease of use and innate design for NLP, and scikit-learn for classification and clustering.

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Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud.

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Women in Big Data

NOVEMBER 27, 2024

How to Choose a Data Warehouse for Your Big Data Choosing a data warehouse for big data storage necessitates a thorough assessment of your unique requirements. Begin by determining your data volume, variety, and the performance expectations for querying and reporting.

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The MLOps Blog

MARCH 15, 2023

In this post, you will learn about the 10 best data pipeline tools, their pros, cons, and pricing. 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.

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AWS Machine Learning Blog

APRIL 19, 2023

Then we needed to Dockerize the application, write a deployment YAML file, deploy the gRPC server to our Kubernetes cluster, and make sure it’s reliable and auto scalable. It also includes support for new hardware like ARM (both in servers like AWS Graviton and laptops with Apple M1 ) and AWS Inferentia.

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Pickl AI

MAY 15, 2024

As a Data Analyst, you’ve honed your skills in data wrangling, analysis, and communication. But the allure of tackling large-scale projects, building robust models for complex problems, and orchestrating data pipelines might be pushing you to transition into Data Science architecture.

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phData

FEBRUARY 21, 2025

The Cloud represents an iteration beyond the on-prem data warehouse, where computing resources are delivered over the Internet and are managed by a third-party provider. Examples include: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).

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phData

DECEMBER 4, 2023

Thirty seconds is a good default for human users; if you find that queries are regularly queueing, consider making your warehouse a multi-cluster that scales on-demand. Cluster Count If your warehouse has to serve many concurrent requests, you may need to increase the cluster count to meet demand.

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Pickl AI

JUNE 7, 2024

Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Read More: Advanced SQL Tips and Tricks for Data Analysts.

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Pickl AI

NOVEMBER 4, 2024

Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. They are crucial in ensuring data is readily available for analysis and reporting.

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The MLOps Blog

JUNE 27, 2023

For example, if you use AWS, you may prefer Amazon SageMaker as an MLOps platform that integrates with other AWS services. SageMaker Studio offers built-in algorithms, automated model tuning, and seamless integration with AWS services, making it a powerful platform for developing and deploying machine learning solutions at scale.

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Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Big Data Processing: Apache Hadoop, Apache Spark, etc.

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Mlearning.ai

FEBRUARY 6, 2023

When building your Processing Docker image, don't place any data required by your container in these directories. The sample bash script will take care of all the AWS-related authentication, create a repository named sm-semantic-similarity, tag it and finally push it to the Amazon ECR repository. docker build -t ${algorithm_name}.

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phData

MARCH 26, 2024

However, it is now available in public preview in specific AWS regions, excluding trial accounts. The real benefit of utilizing Hybrid tables is that they bring transactional and analytical data together in a single platform. Hybrid tables can streamline data pipelines, reduce costs, and unlock deeper insights from data.

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Iguazio

JANUARY 16, 2024

The system’s architecture ensures the data flows through the different systems effectively. First, the data lake is fed from a number of data sources. These include conversational data, ATS Data and more. Sense onboarded Iguazio as an MLOps solution for the ML training and serving component of the pipeline.

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Iguazio

JANUARY 16, 2024

The system’s architecture ensures the data flows through the different systems effectively. First, the data lake is fed from a number of data sources. These include conversational data, ATS data, and more. Sense onboarded Iguazio as an MLOps platform for the ML training and serving component of the pipeline.

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IBM Journey to AI blog

SEPTEMBER 4, 2024

Kafka helps simplify the communication between customers and businesses, using its data pipeline to accurately record events and keep records of orders and cancellations—alerting all relevant parties in real-time. Developers using Apache can speed app development with support for whatever requirements their organization has.

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DagsHub

APRIL 7, 2024

Image generated with Midjourney In today’s fast-paced world of data science, building impactful machine learning models relies on much more than selecting the best algorithm for the job. Data scientists and machine learning engineers need to collaborate to make sure that together with the model, they develop robust data pipelines.

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phData

AUGUST 4, 2023

Snowflake stores and manages data in the cloud using a shared disk approach, which simplifies data management. The shared-nothing architecture ensures that users don’t have to worry about distributing data across multiple cluster nodes. The data can then be processed using Snowflake’s SQL capabilities.

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DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date.

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ODSC - Open Data Science

JANUARY 7, 2025

Environments Data science environments encompass the tools and platforms where professionals perform their work. From development environments like Jupyter Notebooks to robust cloud-hosted solutions such as AWS SageMaker, proficiency in these systems is critical.

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Flipboard

MARCH 21, 2025

Through this unified query capability, you can create comprehensive insights into customer transaction patterns and purchase behavior for active products without the traditional barriers of data silos or the need to copy data between systems. Environments are the actual data infrastructure behind a project.

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DagsHub

APRIL 21, 2024

It offers implementations of various machine learning algorithms, including linear and logistic regression , decision trees , random forests , support vector machines , clustering algorithms , and more. Apache Airflow Apache Airflow is an open-source workflow orchestration tool that can manage complex workflows and data pipelines.

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The MLOps Blog

MARCH 21, 2023

Orchestrators are concerned with lower-level abstractions like machines, instances, clusters, service-level grouping, replication, and so on. Along with the schedulers, they are integral to managing the regular workflows your data scientists run and how the tasks in those workflows communicate with the ML platform.

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The MLOps Blog

MARCH 12, 2024

Data and workflow orchestration: Ensuring efficient data pipeline management and scalable workflows for LLM performance. Combine this with the serverless BentoCloud or an auto-scaling group on a cloud platform like AWS to ensure your resources match the demand.

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