AWS Machine Learning Blog

AUGUST 21, 2024

Amazon DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. The data lake environment is required to configure an AWS Glue database table, which is used to publish an asset in the Amazon DataZone catalog.

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AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

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AWS Machine Learning Blog

MARCH 1, 2023

Flywheel creates a data lake (in Amazon S3) in your account where all the training and test data for all versions of the model are managed and stored. Periodically, the new labeled data (to retrain the model) can be made available to flywheel by creating datasets. The data can be accessed from AWS Open Data Registry.

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IBM Data Science in Practice

JANUARY 9, 2024

However, in a real use case you could receive this data from third party DBs which could be connected directly to IoT Platform. Once, the sensors data is stored in the Data Lake (DB2Warehouse), this information is then passed on to MAS Manage in step 2 using the same pmlib library.

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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. Data comes from disparate sources in a number of formats.

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IBM Journey to AI blog

DECEMBER 7, 2023

OLAP database systems have evolved from specialized analytical tools into comprehensive data analytics platforms, empowering businesses to make informed decisions based on insights from large and complex datasets. Organizations can expect to reap the following benefits from implementing OLAP solutions, including the following.

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Tableau

APRIL 18, 2022

Tableau helps strike the necessary balance to access, improve data quality, and prepare and model data for analytics use cases, while writing-back data to data management sources. Analytics data catalog. Review quality and structural information on data and data sources to better monitor and curate for use.

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AWS Machine Learning Blog

MAY 31, 2024

The General Data Protection Regulation (GDPR) right to be forgotten, also known as the right to erasure, gives individuals the right to request the deletion of their personally identifiable information (PII) data held by organizations. Example: customer information pertaining to the email address art@venere.org.

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Tableau

APRIL 18, 2022

Tableau helps strike the necessary balance to access, improve data quality, and prepare and model data for analytics use cases, while writing-back data to data management sources. Analytics data catalog. Review quality and structural information on data and data sources to better monitor and curate for use.

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DataRobot Blog

OCTOBER 3, 2017

Many announcements at Strata centered on product integrations, with vendors closing the loop and turning tools into solutions, most notably: A Paxata-HDInsight solution demo, where Paxata showcased the general availability of its Adaptive Information Platform for Microsoft Azure.

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Alation

FEBRUARY 13, 2020

A Data Catalog is a collection of metadata, combined with data management and search tools, that helps analysts and other data users to find the data that they need, serves as an inventory of available data, and provides information to evaluate fitness data for intended uses. What is a Data Catalog?

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AWS Machine Learning Blog

JUNE 18, 2024

Through evaluations of sensors and informed decision-making support, Afri-SET empowers governments and civil society for effective air quality management. The attempt is disadvantaged by the current focus on data cleaning, diverting valuable skills away from building ML models for sensor calibration.

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Pickl AI

FEBRUARY 21, 2023

Data mining is often used in conjunction with other data analytics techniques, such as machine learning and predictive analytics, to build models that can be used to make predictions and inform decision-making. Data mining can be applied to many data types, including customer, financial, medical, and scientific data.

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Alation

SEPTEMBER 23, 2021

What Is a Data Catalog? A data catalog is a centralized storage bank of metadata on information sources from across the enterprise, such as: Datasets. The data catalog also stores metadata (data about data, like a conversation), which gives users context on how to use each asset. Visualizations.

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AWS Machine Learning Blog

JUNE 22, 2023

In LnW Connect, an encryption process was designed to provide a secure and reliable mechanism for the data to be brought into an AWS data lake for predictive modeling. All the names in the table are anonymized to protect customer information.) Increasing the number of bins preserves more temporal information.

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Flipboard

NOVEMBER 24, 2023

With that, the need for data scientists and machine learning (ML) engineers has grown significantly. These skilled professionals are tasked with building and deploying models that improve the quality and efficiency of BMW’s business processes and enable informed leadership decisions.

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ODSC - Open Data Science

JUNE 12, 2023

Then we have some other ETL processes to constantly land the past 5 years of data into the Datamarts. No-code/low-code experience using a diagram view in the data preparation layer similar to Dataflows. Therefore, Datamarts are not a replacement for Dataflows. A replacement for datasets.

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phData

SEPTEMBER 28, 2023

Dataflows represent a cloud-based technology designed for data preparation and transformation purposes. Dataflows have different connectors to retrieve data, including databases, Excel files, APIs, and other similar sources, along with data manipulations that are performed using Online Power Query Editor.

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AWS Machine Learning Blog

AUGUST 4, 2023

Package the SageMaker Data Wrangler container and the trained recommendation model container in an inference pipeline so the inference request can use the same data preparation steps you created to preprocess the training data. For more information, refer to Creating roles and attaching policies (console).

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Alation

FEBRUARY 20, 2020

Self-service analytics environments are giving rise to the data marketplace. Unlike the data warehouses of the past, an enterprise data marketplace (EDM) doesn’t just store, process, or manage information by IT. Even something like gamification may emerge as a way to fully engage data shoppers as a community.

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IBM Journey to AI blog

JULY 17, 2023

These encoder-only architecture models are fast and effective for many enterprise NLP tasks, such as classifying customer feedback and extracting information from large documents. While they require task-specific labeled data for fine tuning, they also offer clients the best cost performance trade-off for non-generative use cases.

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Precisely

JULY 18, 2024

Without access to all critical and relevant data, the data that emerges from a data fabric will have gaps that delay business insights required to innovate, mitigate risk, or improve operational efficiencies. You must be able to continuously catalog, profile, and identify the most frequently used data.

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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. If you want to do the process in a low-code/no-code way, you can follow option C.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Mai-Lan Tomsen Bukovec, Vice President, Technology | AIM250-INT | Putting your data to work with generative AI Thursday November 30 | 12:30 PM – 1:30 PM (PST) | Venetian | Level 5 | Palazzo Ballroom B How can you turn your data lake into a business advantage with generative AI?

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Pickl AI

NOVEMBER 4, 2024

The goal is to ensure that data is available, reliable, and accessible for analysis, ultimately driving insights and informed decision-making within organisations. Role of Data Engineers in the Data Ecosystem Data Engineers play a crucial role in the data ecosystem by bridging the gap between raw data and actionable insights.

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Pickl AI

APRIL 9, 2025

It provides insights into considerations for choosing the right tool, ensuring businesses can optimize their data integration processes for better analytics and decision-making. Introduction In todays data-driven world, organizations are overwhelmed with vast amounts of information.

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The MLOps Blog

JUNE 27, 2023

See also Thoughtworks’s guide to Evaluating MLOps Platforms End-to-end MLOps platforms End-to-end MLOps platforms provide a unified ecosystem that streamlines the entire ML workflow, from data preparation and model development to deployment and monitoring. Can you debug system information? Can you compare images?

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Alation

FEBRUARY 13, 2020

Data Literacy—Many line-of-business people have responsibilities that depend on data analysis but have not been trained to work with data. Their tendency is to do just enough data work to get by, and to do that work primarily in Excel spreadsheets. Who needs data literacy training? Who can provide the training?

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phData

FEBRUARY 24, 2023

Alteryx provides organizations with an opportunity to automate access to data, analytics , data science, and process automation all in one, end-to-end platform. Its capabilities can be split into the following topics: automating inputs & outputs, data preparation, data enrichment, and data science.

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phData

DECEMBER 4, 2023

With the phData Provision Tool, you can: Quickly ramp up new projects through the reuse of information architecture Speed user onboarding and simplify access management Quickly apply new changes to Snowflake. For further information or assistance, phData recommends having deeper conversations with our technical experts.

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Pickl AI

SEPTEMBER 5, 2024

Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage. Key features and benefits of Azure for Data Science include: Scalability: Easily scale resources up or down based on demand, ideal for handling large datasets and complex computations.

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Pickl AI

OCTOBER 10, 2024

Informatica’s AI-powered automation helps streamline data pipelines and improve operational efficiency. Common use cases include integrating data across hybrid cloud environments, managing data lakes, and enabling real-time analytics for Business Intelligence platforms.

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Snorkel AI

MARCH 2, 2023

Large language models can be used to analyze medical records and extract relevant information like a diagnosis or treatment plan, but accuracy is important so that it can cite information of why it made that diagnosis or why the treatment plan is recommended. Data preparation, train and tune, deploy and monitor.

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Snorkel AI

MARCH 2, 2023

Large language models can be used to analyze medical records and extract relevant information like a diagnosis or treatment plan, but accuracy is important so that it can cite information of why it made that diagnosis or why the treatment plan is recommended. Data preparation, train and tune, deploy and monitor.

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The MLOps Blog

OCTOBER 20, 2023

References : Links to internal or external documentation with background information or specific information used within the analysis presented in the notebook. Introduction & background : Put the task into context, add information about the key business precedents around the issue, and explain the task in more detail.

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AWS Machine Learning Blog

JANUARY 26, 2024

Being aware of risks fosters transparency and trust in generative AI applications, encourages increased observability, helps to meet compliance requirements, and facilitates informed decision-making by leaders. You might also find benefit in understanding your overall cloud readiness by participating in an AWS Cloud Readiness Assessment.

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The MLOps Blog

MAY 9, 2023

The pipelines are interoperable to build a working system: Data (input) pipeline (data acquisition and feature management steps) This pipeline transports raw data from one location to another. Model/training pipeline This pipeline trains one or more models on the training data with preset hyperparameters.

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