Precisely

OCTOBER 3, 2024

In reviewing best practices for your AWS cloud migration, it’s crucial to define your business case first, and work from there. Migrating to AWS can unlock incredible value for your business, but it requires careful planning, risk management, and the right technical and organizational strategies.

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AWS Machine Learning Blog

JUNE 18, 2024

On December 6 th -8 th 2023, the non-profit organization, Tech to the Rescue , in collaboration with AWS, organized the world’s largest Air Quality Hackathon – aimed at tackling one of the world’s most pressing health and environmental challenges, air pollution. As always, AWS welcomes your feedback.

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Flipboard

NOVEMBER 24, 2023

In this post, we will talk about how BMW Group, in collaboration with AWS Professional Services, built its Jupyter Managed (JuMa) service to address these challenges. For example, teams using these platforms missed an easy migration of their AI/ML prototypes to the industrialization of the solution running on AWS.

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AWS Machine Learning Blog

MARCH 14, 2024

Generative artificial intelligence (generative AI) has enabled new possibilities for building intelligent systems. Given the data sources, LLMs provided tools that would allow us to build a Q&A chatbot in weeks, rather than what may have taken years previously, and likely with worse performance.

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AWS Machine Learning Blog

MARCH 8, 2023

In order to train a model using data stored outside of the three supported storage services, the data first needs to be ingested into one of these services (typically Amazon S3). This requires building a data pipeline (using tools such as Amazon SageMaker Data Wrangler ) to move data into Amazon S3.

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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

MARCH 15, 2024

This approach can help heart stroke patients, doctors, and researchers with faster diagnosis, enriched decision-making, and more informed, inclusive research work on stroke-related health issues, using a cloud-native approach with AWS services for lightweight lift and straightforward adoption. Stroke victims can lose around 1.9

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AWS Machine Learning Blog

DECEMBER 20, 2023

Whether logs are coming from Amazon Web Services (AWS), other cloud providers, on-premises, or edge devices, customers need to centralize and standardize security data. After the security log data is stored in Amazon Security Lake, the question becomes how to analyze it. Subscribe an AWS Lambda function to the SQS queue.

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AWS Machine Learning Blog

DECEMBER 18, 2024

Training an LLM is a compute-intensive and complex process, which is why Fastweb, as a first step in their AI journey, used AWS generative AI and machine learning (ML) services such as Amazon SageMaker HyperPod. The team opted for fine-tuning on AWS.

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AWS Machine Learning Blog

AUGUST 8, 2024

As one of the largest AWS customers, Twilio engages with data, artificial intelligence (AI), and machine learning (ML) services to run their daily workloads. Data is the foundational layer for all generative AI and ML applications. Access to Amazon Bedrock FMs isn’t granted by default.

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AWS Machine Learning Blog

OCTOBER 24, 2024

Amazon Bedrock Agents is instrumental in customization and tailoring apps to help meet specific project requirements while protecting private data and securing their applications. These agents work with AWS managed infrastructure capabilities and Amazon Bedrock , reducing infrastructure management overhead.

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AWS Machine Learning Blog

AUGUST 22, 2024

Building a deployment pipeline for generative artificial intelligence (AI) applications at scale is a formidable challenge because of the complexities and unique requirements of these systems. Generative AI applications require continuous ingestion, preprocessing, and formatting of vast amounts of data from various sources.

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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

DECEMBER 5, 2023

AWS is especially well suited to provide enterprises the tools necessary for deploying LLMs at scale to enable critical decision-making. In their implementation of generative AI technology, enterprises have real concerns about data exposure and ownership of confidential information that may be sent to LLMs.

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Data Science Dojo

JULY 3, 2024

Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. Data Processing and Analysis : Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python.

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AWS Machine Learning Blog

DECEMBER 4, 2024

It seems straightforward at first for batch data, but the engineering gets even more complicated when you need to go from batch data to incorporating real-time and streaming data sources, and from batch inference to real-time serving. You can also find Tecton at AWS re:Invent.

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Pickl AI

DECEMBER 26, 2024

Each platform offers unique capabilities tailored to varying needs, making the platform a critical decision for any Data Science project. Major Cloud Platforms for Data Science Amazon Web Services ( AWS ), Microsoft Azure, and Google Cloud Platform (GCP) dominate the cloud market with their comprehensive offerings.

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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. Data Engineering Platforms Spark is still the leader for data pipelines but other platforms are gaining ground. Google Cloud is starting to make a name for itself as well.

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AWS Machine Learning Blog

SEPTEMBER 28, 2023

This process significantly benefits from the MLOps features of SageMaker, which streamline the data science workflow by harnessing the powerful cloud infrastructure of AWS. The following diagram illustrates the inference pipeline. Click here to open the AWS console and follow along.

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IBM Journey to AI blog

AUGUST 12, 2024

Instead, businesses tend to rely on advanced tools and strategies—namely artificial intelligence for IT operations (AIOps) and machine learning operations (MLOps)—to turn vast quantities of data into actionable insights that can improve IT decision-making and ultimately, the bottom line.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). And finally, some activities, such as those involved with the latest advances in artificial intelligence (AI), are simply not practically possible, without hardware acceleration.

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Precisely

JANUARY 18, 2024

With the emergence of cloud hyperscalers like AWS, Google, and Microsoft, the shift to the cloud has accelerated significantly. Instead of performing major surgery on their critical business systems, enterprises are opting for real-time data integration built around inherently reliable and scalable change data capture (CDC) technology.

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AWS Machine Learning Blog

NOVEMBER 1, 2023

Give the features a try and send us feedback either through the AWS forum for Amazon Comprehend or through your usual AWS support contacts. About the Authors Aman Tiwari is a General Solutions Architect working with Worldwide Commercial Sales at AWS.

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Towards AI

MAY 30, 2024

Build and Run Data Pipelines with Sagemaker Pipelines by Jake Teo This article shows how to run long-running, repetitive, centrally managed, and traceable data pipelines leveraging AWS’s MLOps platform, Sagemaker, and its underlying services, Sagemaker pipelines, and Studio.

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Precisely

MARCH 4, 2024

What if every decision, recommendation, and prediction made by artificial intelligence (AI) was as reliable as your most trusted team members? Next, you’ll see valuable AI use cases and how data integrity powers success. Technology-driven insights and capabilities depend on trusted data.

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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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IBM Journey to AI blog

JULY 17, 2023

” Das Kamhout, VP and Senior Principal Engineer of the Cloud and Enterprise Solutions Group at Intel Watsonx.data supports our customers’ increasing needs around hybrid cloud deployments and is available on premises and across multiple cloud providers, including IBM Cloud and Amazon Web Services (AWS).

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ODSC - Open Data Science

FEBRUARY 2, 2023

Cloud Services The only two to make multiple lists were Amazon Web Services (AWS) and Microsoft Azure. Most major companies are using one of the two, so excelling in one or the other will help any aspiring data scientist. Saturn Cloud is picking up a lot of momentum lately too thanks to its scalability.

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Pickl AI

OCTOBER 10, 2024

It supports batch and real-time data processing, making it a preferred choice for large enterprises with complex data workflows. Informatica’s AI-powered automation helps streamline data pipelines and improve operational efficiency. AWS Glue AWS Glue is a fully managed ETL service provided by Amazon Web Services.

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AWS Machine Learning Blog

JANUARY 25, 2023

This field is often referred to as explainable artificial intelligence (XAI). Amazon SageMaker Clarify is a feature of Amazon SageMaker that enables data scientists and ML engineers to explain the predictions of their ML models. Solution overview SageMaker algorithms have fixed input and output data formats.

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Precisely

MAY 13, 2024

If you’re in the market for a data integration solution, there are many things to consider – including the flexibility of integration solutions, the availability of a strong network of service providers, and the vendor’s reputation for thought leadership in the integration space.

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ODSC - Open Data Science

FEBRUARY 6, 2024

Apache Kafka For data engineers dealing with real-time data, Apache Kafka is a game-changer. This open-source streaming platform enables the handling of high-throughput data feeds, ensuring that data pipelines are efficient, reliable, and capable of handling massive volumes of data in real-time.

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phData

JULY 5, 2024

The release of ChatGPT in late 2022 introduced generative artificial intelligence to the general public and triggered a new wave of AI-oriented companies, products, and open-source projects that provide tools and frameworks to enable enterprise AI.

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Pickl AI

OCTOBER 15, 2024

It supports both batch and real-time data processing , making it highly versatile. Its ability to integrate with cloud platforms like AWS and Azure makes it an excellent choice for businesses moving to the cloud. Apache Nifi Apache Nifi is an open-source ETL tool that automates data flow between systems.

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IBM Journey to AI blog

AUGUST 6, 2024

Today, all leading CSPs, including Amazon Web Services (AWS Lambda), Microsoft Azure (Azure Functions) and IBM (IBM Cloud Code Engine) offer serverless platforms. Specifically, serverless helps enable something called event-driven AI, where a constant flow of intelligence informs real-time decision-making capabilities.

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ODSC - Open Data Science

JANUARY 18, 2024

This individual is responsible for building and maintaining the infrastructure that stores and processes data; the kinds of data can be diverse, but most commonly it will be structured and unstructured data. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable.

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Pickl AI

JULY 25, 2024

Improved Decision-making By providing a consolidated and accessible view of data, organisations can identify trends, patterns, and anomalies more quickly, leading to better-informed and timely decisions. Data Ingestion Tools To facilitate the process, various tools and technologies are available. The post What is Data Ingestion?

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