DevOps ETL ML 
AI News
AUGUST 29, 2023
The operationalisation of data projects has been a key factor in helping organisations turn a data deluge into a workable digital transformation strategy, and DataOps carries on from where DevOps started. So that’s on the vendor side.
AWS Machine Learning Blog
FEBRUARY 21, 2025
Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks. Inadequate data security and DevOps support The previous solution lacked robust security measures, and there was limited support for development and operations of the data science work.
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AWS Machine Learning Blog
JANUARY 5, 2024
This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.
AWS Machine Learning Blog
FEBRUARY 2, 2024
The embeddings are captured in Amazon Simple Storage Service (Amazon S3) via Amazon Kinesis Data Firehose , and we run a combination of AWS Glue extract, transform, and load (ETL) jobs and Jupyter notebooks to perform the embedding analysis. Set the parameters for the ETL job as follows and run the job: Set --job_type to BASELINE.
AWS Machine Learning Blog
SEPTEMBER 1, 2023
ML operationalization summary As defined in the post MLOps foundation roadmap for enterprises with Amazon SageMaker , ML and operations (MLOps) is the combination of people, processes, and technology to productionize machine learning (ML) solutions efficiently.
The MLOps Blog
SEPTEMBER 7, 2023
This situation is not different in the ML world. Data Scientists and ML Engineers typically write lots and lots of code. From writing code for doing exploratory analysis, experimentation code for modeling, ETLs for creating training datasets, Airflow (or similar) code to generate DAGs, REST APIs, streaming jobs, monitoring jobs, etc.
Mlearning.ai
APRIL 24, 2023
But, it does not give you all the information about the different functionalities and services, like Data Factory/Linked Services/Analytics Synapse(how to combine and manage databases, ETL), Cognitive Services/Form Recognizer/ (how to do image, text, audio processing), IoT, Deployment, GitHub Actions (running Azure scripts from GitHub).
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