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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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. A data scientist team orders a new JuMa workspace in BMW’s Catalog.

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IBM Data Science in Practice

JANUARY 9, 2024

By Carolyn Saplicki , IBM Data Scientist Industries are constantly seeking innovative solutions to maximize efficiency, minimize downtime, and reduce costs. All data scientists could leverage our patterns during an engagement. In our scenario, the data is stored in the Cloud Object Storage in Watson Studio.

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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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IBM Journey to AI blog

MAY 9, 2023

At IBM, we believe it is time to place the power of AI in the hands of all kinds of “AI builders” — from data scientists to developers to everyday users who have never written a single line of code. A data store built on open lakehouse architecture, it runs both on premises and across multi-cloud environments.

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Pickl AI

FEBRUARY 21, 2023

Business organisations worldwide depend on massive volumes of data that require Data Scientists and analysts to interpret to make efficient decisions. Understanding the appropriate ways to use data remains critical to success in finance, education and commerce. What is Data Mining and how is it related to Data Science ?

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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. Check out the Kubeflow documentation.

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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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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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IBM Journey to AI blog

JULY 17, 2023

Within watsonx.ai, users can take advantage of open-source frameworks like PyTorch, TensorFlow and scikit-learn alongside IBM’s entire machine learning and data science toolkit and its ecosystem tools for code-based and visual data science capabilities.

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IBM Journey to AI blog

MARCH 14, 2024

With newfound support for open formats such as Parquet and Apache Iceberg, Netezza enables data engineers, data scientists and data analysts to share data and run complex workloads without duplicating or performing additional ETL.

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ODSC - Open Data Science

NOVEMBER 19, 2024

Despite the rise of big data technologies and cloud computing, the principles of dimensional modeling remain relevant. This session delved into how these traditional techniques have adapted to data lakes and real-time analytics, emphasizing their enduring importance for building scalable, efficient data systems.

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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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Pickl AI

AUGUST 1, 2023

It involves the design, development, and maintenance of systems, tools, and processes that enable the acquisition, storage, processing, and analysis of large volumes of data. Data Engineers work to build and maintain data pipelines, databases, and data warehouses that can handle the collection, storage, and retrieval of vast amounts of data.

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Pickl AI

NOVEMBER 4, 2024

They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes. Their work ensures that data flows seamlessly through the organisation, making it easier for Data Scientists and Analysts to access and analyse information.

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Snorkel AI

MAY 26, 2023

And that’s really key for taking data science experiments into production. And one of the biggest challenges that we see is taking an idea, an experiment, or an ML experiment that data scientists might be running in their notebooks and putting that into production.

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Snorkel AI

MAY 26, 2023

And that’s really key for taking data science experiments into production. And one of the biggest challenges that we see is taking an idea, an experiment, or an ML experiment that data scientists might be running in their notebooks and putting that into production.

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The MLOps Blog

OCTOBER 20, 2023

Jupyter notebooks have been one of the most controversial tools in the data science community. Nevertheless, many data scientists will agree that they can be really valuable – if used well. Data on its own is not sufficient for a cohesive story. in a pandas DataFrame) but in the company’s data warehouse (e.g.,

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AWS Machine Learning Blog

JANUARY 26, 2024

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

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The MLOps Blog

MAY 9, 2023

The platform typically includes components for the ML ecosystem like data management, feature stores, experiment trackers, a model registry, a testing environment, model serving, and model management. Model/training pipeline This pipeline trains one or more models on the training data with preset hyperparameters.

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Dataconomy

FEBRUARY 26, 2025

KDD provides a structured framework to convert raw data into actionable knowledge. The KDD process Data gathering Data preparation Data mining Data analysis and interpretation Data mining process components Understanding the components of the data mining process is essential for effective implementation.

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