AWS Machine Learning Blog
MAY 5, 2023
The following figure shows an example diagram that illustrates an orchestrated extract, transform, and load (ETL) architecture solution. For example, searching for the terms “How to orchestrate ETL pipeline” returns results of architecture diagrams built with AWS Glue and AWS Step Functions. join(", "), }; }).catch((error)
The MLOps Blog
MARCH 15, 2023
In the case of our CI/CD-MLOPs system, we stored the model versions and metadata in the data storage services offered by AWS i.e ” Hence the very first thing to do is to make sure that the data being used is of high quality and that any errors or anomalies are detected and corrected before proceeding with ETL and data sourcing.
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AWS Machine Learning Blog
JULY 6, 2023
The open-source Custom Connector SDK enables the development of a private, shared, or public connector using Python or Java. SaaS platform SDK – If the SaaS platform has an SDK (Software Development Kit), such as a Python SDK, this can be used to access data directly from a SageMaker notebook.
The MLOps Blog
JANUARY 23, 2023
More useful resources about DVC: Versioning data and models Data version control with Python and DVC DVCorg YouTube DVC data version control cheatsheet At this point, one question arises; why use DVC instead of Git? It provides options for tracking, organizing, and storing metadata from machine learning experiments. neptune.ai
Applied Data Science
MAY 6, 2021
To keep myself sane, I use Airflow to automate tasks with simple, reusable pieces of code for frequently repeated elements of projects, for example: Web scraping ETL Database management Feature building and data validation And much more! Note that we can use the core python package datetime to help us define our DAGs.
The MLOps Blog
JANUARY 26, 2024
A feature store typically comprises a feature repository, a feature serving layer, and a metadata store. The metadata store manages the metadata associated with each feature, such as its origin and transformations. All of them are written in Python. It can also transform incoming data on the fly.
AWS Machine Learning Blog
MARCH 1, 2023
To solve this problem, we build an extract, transform, and load (ETL) pipeline that can be run automatically and repeatedly for training and inference dataset creation. The ETL pipeline, MLOps pipeline, and ML inference should be rebuilt in a different AWS account. But there is still an engineering challenge.
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