AWS Machine Learning Blog

APRIL 3, 2025

Amazon AppFlow was used to facilitate the smooth and secure transfer of data from various sources into ODAP. Additionally, Amazon Simple Storage Service (Amazon S3) served as the central data lake, providing a scalable and cost-effective storage solution for the diverse data types collected from different systems.

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Data Science 101

NOVEMBER 7, 2019

Microsoft just held one of its largest conferences of the year, and a few major announcements were made which pertain to the cloud data science world. Azure Synapse Analytics can be seen as a merge of Azure SQL Data Warehouse and Azure Data Lake. R Support for Azure Machine Learning. Azure Synapse.

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IBM Journey to AI blog

FEBRUARY 2, 2023

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics, that enable faster decision making and insights.

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phData

NOVEMBER 9, 2023

Snowflake’s Data Cloud has emerged as a leader in cloud data warehousing. As a fundamental piece of the modern data stack , Snowflake is helping thousands of businesses store, transform, and derive insights from their data easier, faster, and more efficiently than ever before. What is a Data Lake?

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Dataconomy

SEPTEMBER 4, 2023

Cloud-based business intelligence (BI): Cloud-based BI tools enable organizations to access and analyze data from cloud-based sources and on-premises databases. These tools offer the flexibility of accessing insights from anywhere, and they often integrate with other cloud analytics solutions.

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Dataversity

FEBRUARY 17, 2022

With machine learning (ML) and artificial intelligence (AI) applications becoming more business-critical, organizations are in the race to advance their AI/ML capabilities. To realize the full potential of AI/ML, having the right underlying machine learning platform is a prerequisite.

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IBM Journey to AI blog

JUNE 15, 2023

It is comprised of commodity cloud object storage, open data and open table formats, and high-performance open-source query engines. To help organizations scale AI workloads, we recently announced IBM watsonx.data , a data store built on an open data lakehouse architecture and part of the watsonx AI and data platform.

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Dataversity

FEBRUARY 15, 2021

The post 2021 Crystal Ball: What’s in Store for AI, Machine Learning, and Data appeared first on DATAVERSITY. From business processes and smart home technology to healthcare and life sciences, AI continues to evolve and grow as it plays an increasing role in many aspects of our work, home lives, and beyond.

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Flipboard

NOVEMBER 24, 2023

In an increasingly digital and rapidly changing world, BMW Group’s business and product development strategies rely heavily on data-driven decision-making. With that, the need for data scientists and machine learning (ML) engineers has grown significantly.

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AWS Machine Learning Blog

SEPTEMBER 5, 2023

In order to improve our equipment reliability, we partnered with the Amazon Machine Learning Solutions Lab to develop a custom machine learning (ML) model capable of predicting equipment issues prior to failure. Data processing and model inference need to scale as our data grows.

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Smart Data Collective

OCTOBER 27, 2021

Google BigQuery is a serverless and cost-effective multi-cloud data warehouse. Its built-in machine learning makes it possible for users to gain insights predictive and real-time analytics. It can also batch load files from data lakes such as Amazon S3 and HDFS.

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Tableau

JUNE 8, 2021

We often hear that organizations have invested in data science capabilities but are struggling to operationalize their machine learning models. Domain experts, for example, feel they are still overly reliant on core IT to access the data assets they need to make effective business decisions.

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IBM Journey to AI blog

MAY 9, 2023

After some impressive advances over the past decade, largely thanks to the techniques of Machine Learning (ML) and Deep Learning , the technology seems to have taken a sudden leap forward. A data store built on open lakehouse architecture, it runs both on premises and across multi-cloud environments. Watsonx.ai

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Flipboard

AUGUST 17, 2023

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development.

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AWS Machine Learning Blog

NOVEMBER 27, 2023

SageMaker endpoints can be registered to the Salesforce Data Cloud to activate predictions in Salesforce. Set up OAuth for Salesforce Data Cloud in SageMaker Canvas. Connect to Salesforce Data Cloud data using the built-in SageMaker Canvas Salesforce Data Cloud connector and import the dataset.

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Alation

DECEMBER 14, 2021

Compliance in the Cloud ( GDPR, CCPA ) is still in in its infancy and tough to navigate, with people wondering: How do you manage policies in the cloud? How do you provide access and connect the right people to the right data? AWS has created a way to manage policies and access, but this is only for data lake formation.

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IBM Journey to AI blog

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. Automated development: Automates data preparation, model development, feature engineering and hyperparameter optimization using AutoAI.

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phData

NOVEMBER 1, 2024

Chief Information Officer, Legal Industry Survey respondents noted improved data quality and compliance and risk management as the top two outcomes for organizations with a focus on more standardized data controls when working to implement GenAI and LLM applications.

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AWS Machine Learning Blog

APRIL 12, 2023

The PdMS includes AWS services to securely manage the lifecycle of edge compute devices and BHS assets, cloud data ingestion, storage, machine learning (ML) inference models, and business logic to power proactive equipment maintenance in the cloud. Outside of work, Fauzan enjoys spending time in nature.

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Precisely

JANUARY 25, 2024

These systems support containerized applications, virtualization, AI and machine learning, API and cloud connectivity, and more. In light of this, modernization should not be viewed as a replacement strategy so much as an approach to unifying mainframe operations with today’s cloud platforms.

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Alation

JUNE 9, 2021

We have an explosion, not only in the raw amount of data, but in the types of database systems for storing it ( db-engines.com ranks over 340) and architectures for managing it (from operational datastores to data lakes to cloud data warehouses). Organizations are drowning in a deluge of data.

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Alation

MARCH 29, 2021

Data domains group data logically by, for example, business function, product line, geographic region, or any other construct. Alation increases understanding of data Alation leverages machine learning alongside human curation to speed up data search and understanding.

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The MLOps Blog

MAY 17, 2023

Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. We also discuss different types of ETL pipelines for ML use cases and provide real-world examples of their use to help data engineers choose the right one.

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phData

SEPTEMBER 29, 2023

Example: JP Morgan Chase applies the concepts of data engineering that help combine market data and transaction histories, making it feasible for banks to carry out ideal risk management analysis with efficient dashboards created from real-time data.

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Alation

JUNE 21, 2022

The product concept back then went something like: In a world where enterprises have numerous sources of data, let’s make a thing that helps people find the best data asset to answer their question based on what other users were using. And to determine “best,” we’d ingest log files and leverage machine learning.

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Alation

APRIL 14, 2022

This two-part series will explore how data discovery, fragmented data governance , ongoing data drift, and the need for ML explainability can all be overcome with a data catalog for accurate data and metadata record keeping. The Cloud Data Migration Challenge. Data pipeline orchestration.

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AWS Machine Learning Blog

AUGUST 4, 2023

If you are a returning user to SageMaker Studio, in order to ensure Salesforce Data Cloud is enabled, upgrade to the latest Jupyter and SageMaker Data Wrangler kernels. This completes the setup to enable data access from Salesforce Data Cloud to SageMaker Studio to build AI and machine learning (ML) models.

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022.

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022.

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Precisely

JANUARY 18, 2024

And, they’re still a key element of the infrastructure that makes private clouds possible at many organizations. 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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Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. Machine Learning Integration Opportunities Organizations harness machine learning (ML) algorithms to make forecasts on the data.

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Alation

APRIL 7, 2021

It automatically surfaces clues in the data to remove the manual effort of discovery within the huge volume, variety, and veracity of data produced by the modern enterprise. Alation’s data intelligence comes from user behavior. For example, many enterprises find that data workers only use 5 to 10% of all data.

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Alation

AUGUST 11, 2022

Today, the brightest minds in our industry are targeting the massive proliferation of data volumes and the accompanying but hard-to-find value locked within all that data. And now with some of these cloud data warehouses becoming such behemoths, everything is getting centralized again.

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Dataversity

APRIL 1, 2021

Click here to learn more about Amit Levi. In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business.

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AWS Machine Learning Blog

NOVEMBER 1, 2024

Instead, a core component of decentralized clinical trials is a secure, scalable data infrastructure with strong data analytics capabilities. Amazon Redshift is a fully managed cloud data warehouse that trial scientists can use to perform analytics.

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phData

DECEMBER 4, 2023

However, if there’s one thing we’ve learned from years of successful cloud data implementations here at phData, it’s the importance of: Defining and implementing processes Building automation, and Performing configuration …even before you create the first user account.

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Dataversity

MARCH 17, 2021

Click to learn more about author Arsalan Farooq. I do not think it is an exaggeration to say data analytics has come into its own over the past decade or so. What started out as an attempt to extract business insights from transactional data in the ’90s and early 2000s has now transformed into an […].

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