IBM Data Science in Practice

JANUARY 9, 2024

In this blog, we delve into 4 different “on-ramps” we created in a MAS Accelerator to offer a straightforward path to harnessing the power of AI in MAS, wherever you may be on your MAS AI/ML modernization journey. In our scenario, the data is stored in the Cloud Object Storage in Watson Studio.

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IBM Journey to AI blog

DECEMBER 7, 2023

Increased operational efficiency benefits Reduced data preparation time : OLAP data preparation capabilities streamline data analysis processes, saving time and resources. IBM watsonx.data is the next generation OLAP system that can help you make the most of your data.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

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AWS Machine Learning Blog

MARCH 8, 2023

Data collection and ingestion The data collection and ingestion layer connects to all upstream data sources and loads the data into the data lake. Therefore, the ingestion components need to be able to manage authentication, data sourcing in pull mode, data preprocessing, and data storage.

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AWS Machine Learning Blog

JUNE 18, 2024

The solution addressed in this blog solves Afri-SET’s challenge and was ranked as the top 3 winning solutions. This post presents a solution that uses a generative artificial intelligence (AI) to standardize air quality data from low-cost sensors in Africa, specifically addressing the air quality data integration problem of low-cost sensors.

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Alation

FEBRUARY 13, 2020

Figure 1 illustrates the typical metadata subjects contained in a data catalog. Figure 1 – Data Catalog Metadata Subjects. Datasets are the files and tables that data workers need to find and access. They may reside in a data lake, warehouse, master data repository, or any other shared data resource.

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Pickl AI

FEBRUARY 21, 2023

Businesses require Data Scientists to perform Data Mining processes and invoke valuable data insights using different software and tools. What is Data Mining and how is it related to Data Science ? Let’s learn from the following blog! What is Data Mining? are the various data mining tools.

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IBM Journey to AI blog

MAY 9, 2023

It offers its users advanced machine learning, data management , and generative AI capabilities to train, validate, tune and deploy AI systems across the business with speed, trusted data, and governance. It helps facilitate the entire data and AI lifecycle, from data preparation to model development, deployment and monitoring.

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IBM Journey to AI blog

MARCH 14, 2024

Whether it’s for ad hoc analytics, data transformation, data sharing, data lake modernization or ML and gen AI, you have the flexibility to choose. Integrated solutions for zero-ETL data preparation: IBM databases on AWS offer integrated solutions that eliminate the need for ETL processes in data preparation for AI.

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Alation

SEPTEMBER 23, 2021

Data Catalogs for Data Science & Engineering – Data catalogs that are primarily used for data science and engineering are typically used by very experienced data practitioners. It also catalogs datasets and operations that includes data preparation features and functions.

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phData

SEPTEMBER 28, 2023

Dataflows allow users to establish source connections and retrieve data, and subsequent data transformations can be conducted using the online Power Query Editor. In this blog, we will provide insights into the process of creating Dataflows and offer guidance on when to choose them to address real-world use cases effectively.

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IBM Journey to AI blog

JULY 17, 2023

In this blog, I will cover: What is watsonx.ai? sales conversation summaries, insurance coverage, meeting transcripts, contract information) Generate: Generate text content for a specific purpose, such as marketing campaigns, job descriptions, blogs or articles, and email drafting support. What capabilities are included in watsonx.ai?

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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. Check out the AWS Blog for more practices about building ML features from a modern data warehouse.

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AWS Machine Learning Blog

MAY 31, 2024

Data preparation Before creating a knowledge base using Knowledge Bases for Amazon Bedrock, it’s essential to prepare the data to augment the FM in a RAG implementation. Data discovery and findability Findability is an important step of the process.

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Alation

FEBRUARY 20, 2020

Even something like gamification may emerge as a way to fully engage data shoppers as a community. Behind the scenes, ‘backroom services” will power the storefront, performing such tasks as data acquisition, data preparation, data curation and cataloging, and tracking. Building the EDM.

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ODSC - Open Data Science

NOVEMBER 19, 2024

ODSC West 2024 showcased a wide range of talks and workshops from leading data science, AI, and machine learning experts. This blog highlights some of the most impactful AI slides from the world’s best data science instructors, focusing on cutting-edge advancements in AI, data modeling, and deployment strategies.

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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. Data preprocessing and feature engineering In this section, we discuss our methods for data preparation and feature engineering.

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AWS Machine Learning Blog

AUGUST 4, 2023

Train a recommendation model in SageMaker Studio using training data that was prepared using SageMaker Data Wrangler. The real-time inference call data is first passed to the SageMaker Data Wrangler container in the inference pipeline, where it is preprocessed and passed to the trained model for product recommendation.

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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? You must bring your laptop to participate.

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Alation

FEBRUARY 13, 2020

In a recent blog, titled Collaboration and Crowdsourcing with Data Cataloging , I discussed the importance of participation by all data stakeholders as a key to getting maximum value from your data catalog. Their tendency is to do just enough data work to get by, and to do that work primarily in Excel spreadsheets.

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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.

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phData

FEBRUARY 24, 2023

Data must be available at the right moment for consumption and it might not be the easiest task to develop a strategy around the continuous pipelines and the integrated applications to set up your stack. Alteryx and the Snowflake Data Cloud offer a potential solution to this issue and can speed up your path to Analytics.

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Pickl AI

SEPTEMBER 5, 2024

Summary: This blog provides a comprehensive roadmap for aspiring Azure Data Scientists, outlining the essential skills, certifications, and steps to build a successful career in Data Science using Microsoft Azure. Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage.

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Snorkel AI

MARCH 2, 2023

In media and gaming: designing game storylines, scripts, auto-generated blogs, articles and tweets, and grammar corrections and text formatting. Data preparation, train and tune, deploy and monitor. We have data pipelines and data preparation. It can cover the gamut.

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Snorkel AI

MARCH 2, 2023

In media and gaming: designing game storylines, scripts, auto-generated blogs, articles and tweets, and grammar corrections and text formatting. Data preparation, train and tune, deploy and monitor. We have data pipelines and data preparation. It can cover the gamut.

Machine Learning 52

Machine Learning Machine Learning Data Preparation AI 52

phData

DECEMBER 4, 2023

This blog was originally written by Erik Hyrkas and updated for 2024 by Justin Delisi This isn’t meant to be a technical how-to guide — most of those details are readily available via a quick Google search — but rather an opinionated review of key processes and potential approaches.

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The MLOps Blog

OCTOBER 20, 2023

Placing functions for plotting, data loading, data preparation, and implementations of evaluation metrics in plain Python modules keeps a Jupyter notebook focused on the exploratory analysis | Source: Author Using SQL directly in Jupyter cells There are some cases in which data is not in memory (e.g.,

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AWS Machine Learning Blog

JANUARY 26, 2024

Also consider using Amazon Security Lake to automatically centralize security data from AWS environments, SaaS providers, on premises, and cloud sources into a purpose-built data lake stored in your account.

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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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AWS Machine Learning Blog

AUGUST 15, 2024

Importing data from the SageMaker Data Wrangler flow allows you to interact with a sample of the data before scaling the data preparation flow to the full dataset. This improves time and performance because you don’t need to work with the entirety of the data during preparation.

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Alation

FEBRUARY 20, 2020

Data lakes, while useful in helping you to capture all of your data, are only the first step in extracting the value of that data. We recently announced an integration with Trifacta to seamlessly integrate the Alation Data Catalog with self-service data prep applications to help you solve this issue.

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DataRobot Blog

OCTOBER 3, 2017

This highlights the two companies’ shared vision on self-service data discovery with an emphasis on collaboration and data governance. 2) When data becomes information, many (incremental) use cases surface.

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