AWS Machine Learning Blog

OCTOBER 24, 2024

Conventional ML development cycles take weeks to many months and requires sparse data science understanding and ML development skills. Business analysts’ ideas to use ML models often sit in prolonged backlogs because of data engineering and data science team’s bandwidth and data preparation activities.

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Smart Data Collective

FEBRUARY 3, 2022

The Right Use of Tools To Deal With Data. Business teams significantly rely upon data for self-service tools and more. Businesses will need to opt for data preparation and analytics tasks, ranging from finance to marketing. Therefore, businesses use tools that will ease the process to get the right data.

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IBM Journey to AI blog

JULY 30, 2024

Data Engineer: A data engineer sets the foundation of building any generating AI app by preparing, cleaning and validating data required to train and deploy AI models. They design data pipelines that integrate different datasets to ensure the quality, reliability, and scalability needed for AI applications.

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AWS Machine Learning Blog

AUGUST 21, 2024

In the following sections, we provide a detailed, step-by-step guide on implementing these new capabilities, covering everything from data preparation to job submission and output analysis. This use case serves to illustrate the broader potential of the feature for handling diverse data processing tasks.

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Ocean Protocol

NOVEMBER 28, 2024

Yunus focused on building a robust data pipeline, merging historical and current-season data to create a comprehensive dataset. Yunus secured third place by delivering a flexible, well-documented solution that bridged data science and Formula 1 strategy.

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AWS Machine Learning Blog

MARCH 1, 2023

Continuous ML model retraining is one method to overcome this challenge by relearning from the most recent data. This requires not only well-designed features and ML architecture, but also data preparation and ML pipelines that can automate the retraining process.

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Pickl AI

NOVEMBER 4, 2024

Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. They are crucial in ensuring data is readily available for analysis and reporting.

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AWS Machine Learning Blog

JUNE 18, 2024

If useful, it can be further extended to a data lake platform that uses AWS Glue (a serverless data integration service for data preparation) and Amazon Athena (a serverless and interactive analytics service) to analyze and visualize data.

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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. Flyte Flyte is a platform for orchestrating ML pipelines at scale.

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AWS Machine Learning Blog

SEPTEMBER 18, 2023

Amazon SageMaker Pipelines allows orchestrating the end-to-end ML lifecycle from data preparation and training to model deployment as automated workflows. We set up an end-to-end Ray-based ML workflow, orchestrated using SageMaker Pipelines. This allows building end-to-end data pipelines and ML workflows on top of Ray.

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Pickl AI

AUGUST 1, 2023

The primary goal of Data Engineering is to transform raw data into a structured and usable format that can be easily accessed, analyzed, and interpreted by Data Scientists, analysts, and other stakeholders. Future of Data Engineering The Data Engineering market will expand from $18.2

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IBM Journey to AI blog

AUGUST 12, 2024

It includes a range of technologies—including machine learning frameworks, data pipelines, continuous integration / continuous deployment (CI/CD) systems, performance monitoring tools, version control systems and sometimes containerization tools (such as Kubernetes )—that optimize the ML lifecycle.

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phData

AUGUST 2, 2024

Snowflake AI Data Cloud is one of the most powerful platforms, including storage services supporting complex data. Integrating Snowflake with dbt adds another layer of automation and control to the data pipeline. Snowflake stored procedures and dbt Hooks are essential to modern data engineering and analytics workflows.

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Flipboard

NOVEMBER 24, 2023

JuMa is tightly integrated with a range of BMW Central IT services, including identity and access management, roles and rights management, BMW Cloud Data Hub (BMW’s data lake on AWS) and on-premises databases. Furthermore, the notebooks can be integrated into the corporate Git repositories to collaborate using version control.

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Flipboard

AUGUST 17, 2023

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development. Above all, this solution offers you a native Spark way to implement an end-to-end data pipeline from Amazon Redshift to SageMaker.

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DataRobot Blog

APRIL 1, 2018

In Nick Heudecker’s session on Driving Analytics Success with Data Engineering , we learned about the rise of the data engineer role – a jack-of-all-trades data maverick who resides either in the line of business or IT. DataRobot Data Prep. 3) The emergence of a new enterprise information management platform. Free Trial.

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ODSC - Open Data Science

MAY 10, 2023

Many mistakenly equate tabular data with business intelligence rather than AI, leading to a dismissive attitude toward its sophistication. Standard data science practices could also be contributing to this issue. One might say that tabular data modeling is the original data-centric AI!

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phData

DECEMBER 4, 2023

Knowing this, you want to have data prepared in a way to optimize your load. Data Pipelines “Data pipeline” means moving data in a consistent, secure, and reliable way at some frequency that meets your requirements. It might be tempting to have massive files and let the system sort it out.

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ODSC - Open Data Science

OCTOBER 7, 2024

Understanding the MLOps Lifecycle The MLOps lifecycle consists of several critical stages, each with its unique challenges: Data Ingestion: Collecting data from various sources and ensuring it’s available for analysis. Data Preparation: Cleaning and transforming raw data to make it usable for machine learning.

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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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Iguazio

JULY 31, 2023

By taking this step, organizations ensure they have high quality data that is available for model training, feature engineering, and analysis. MLOps includes the creation and management of data pipelines, as well as automating the data ingestion process, to ensure data is high quality, consistent, and reliable so that it can be used.

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IBM Journey to AI blog

JULY 17, 2023

Visual modeling: Delivers easy-to-use workflows for data scientists to build data preparation and predictive machine learning pipelines that include text analytics, visualizations and a variety of modeling methods.

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Pickl AI

OCTOBER 17, 2024

Automation Automation plays a pivotal role in streamlining ETL processes, reducing the need for manual intervention, and ensuring consistent data availability. By automating key tasks, organisations can enhance efficiency and accuracy, ultimately improving the quality of their data pipelines.

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Kaggle

JULY 29, 2020

David: My technical background is in ETL, data extraction, data engineering and data analytics. I spent over a decade of my career developing large-scale data pipelines to transform both structured and unstructured data into formats that can be utilized in downstream systems.

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IBM Journey to AI blog

AUGUST 11, 2023

Because the machine learning lifecycle has many complex components that reach across multiple teams, it requires close-knit collaboration to ensure that hand-offs occur efficiently, from data preparation and model training to model deployment and monitoring. How to use ML to automate the refining process into a cyclical ML process.

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Snorkel AI

MARCH 2, 2023

Data preparation, train and tune, deploy and monitor. We have data pipelines and data preparation. In the data pipeline phase—I’m just going to call out things that I think are more important than the obvious. So the basic ones: you collect and validate and prepare data.

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Snorkel AI

MARCH 2, 2023

Data preparation, train and tune, deploy and monitor. We have data pipelines and data preparation. In the data pipeline phase—I’m just going to call out things that I think are more important than the obvious. So the basic ones: you collect and validate and prepare data.

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phData

SEPTEMBER 25, 2024

This setting ensures that the data pipeline adapts to changes in the Source schema according to user-specific needs. Fivetran’s pre-built data models are pre-configured transformations that automatically organize and clean the User’s synced data, making it ready for analysis.

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Iguazio

FEBRUARY 8, 2024

Continuous monitoring of resources, data, and metrics. Data Pipeline - Manages and processes various data sources. ML Pipeline - Focuses on training, validation and deployment. Application Pipeline - Manages requests and data/model validations. Collecting feedback for further tuning.

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Pickl AI

FEBRUARY 8, 2023

Data Manipulation The process through which you can change the data according to your project requirement for further data analysis is known as Data Manipulation. The entire process involves cleaning, Merging and changing the data format. This data can help in building the project pipeline.

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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.

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Pickl AI

JULY 24, 2024

Predictive data quality models, enabled by AI, can anticipate potential issues before they materialise, allowing for proactive interventions. Automated data cleansing, anomaly detection, and root cause analysis, powered by Machine Learning, will streamline data preparation processes and improve accuracy.

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phData

FEBRUARY 7, 2024

Snowpark Use Cases Data Science Streamlining data preparation and pre-processing: Snowpark’s Python, Java, and Scala libraries allow data scientists to use familiar tools for wrangling and cleaning data directly within Snowflake, eliminating the need for separate ETL pipelines and reducing context switching.

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Pickl AI

SEPTEMBER 5, 2024

Data Preparation: Cleaning, transforming, and preparing data for analysis and modelling. Data Scientists can use Azure Data Factory to prepare data for analysis by creating data pipelines that ingest data from multiple sources, clean and transform it, and load it into Azure data stores.

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DagsHub

APRIL 21, 2024

A traditional machine learning (ML) pipeline is a collection of various stages that include data collection, data preparation, model training and evaluation, hyperparameter tuning (if needed), model deployment and scaling, monitoring, security and compliance, and CI/CD.

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DagsHub

MARCH 5, 2024

Preprocess data to mirror real-world deployment conditions. Utilization of existing libraries: Utilize package tools like sci-kit-learn in Python to effortlessly apply distinct data preparation steps for various datasets, particularly in cross-validation, preventing data leakage between folds.

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Mlearning.ai

JUNE 21, 2023

We then go over all the project components and processes, from data preparation, model training, and experiment tracking to model evaluation, to equip you with the skills to construct your own emotion recognition model. Refer to this repository as we walk through the project.

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