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Airflow for Orchestrating REST API Applications

Analytics Vidhya

Introduction to Apache Airflow “Apache Airflow is the most widely-adopted, open-source workflow management platform for data engineering pipelines. It started at Airbnb in October 2014 as a solution to manage the company’s increasingly complex workflows.

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What Is DataOps? Definition, Principles, and Benefits

Alation

DataOps is a set of technologies, processes, and best practices that combine a process-focused perspective on data and the automation methods of the Agile software development methodology to improve speed and quality and foster a collaborative culture of rapid, continuous improvement in the data analytics field. Source: Google Trends.

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Using Matillion Data Productivity Cloud to call APIs

phData

Matillion’s Data Productivity Cloud is a versatile platform designed to increase the productivity of data teams. It provides a unified platform for creating and managing data pipelines that are effective for both coders and non-coders. Check out the API documentation for our sample.

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Explain text classification model predictions using Amazon SageMaker Clarify

AWS Machine Learning Blog

Solution overview SageMaker algorithms have fixed input and output data formats. But customers often require specific formats that are compatible with their data pipelines. Option A In this option, we use the inference pipeline feature of SageMaker hosting.

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Top 5 Use Cases of phData’s Advisor Tool

phData

Founded in 2014 by three leading cloud engineers, phData focuses on solving real-world data engineering, operations, and advanced analytics problems with the best cloud platforms and products. Over the years, one of our primary focuses became Snowflake and migrating customers to this leading cloud data platform.

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How to Manage Unstructured Data in AI and Machine Learning Projects

DagsHub

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date. Our model achieves 28.4 after training for 3.5

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7 Best Machine Learning Workflow and Pipeline Orchestration Tools 2024

DagsHub

Image generated with Midjourney In today’s fast-paced world of data science, building impactful machine learning models relies on much more than selecting the best algorithm for the job. Data scientists and machine learning engineers need to collaborate to make sure that together with the model, they develop robust data pipelines.