Row 56228

Row ID: 56228 | Dataset Entry | Axioma AXP Content Repository

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Data Engineer build ETL pipelines (extract transform and load) for example extracting data from a database like microsoft sql server, transforming it with pandas, and loading it to a data warehouse like BigQuery. Then you might schedule that pipeline to run at a specific interval, i.e. everyday at midnight, with a tool like apache airflow or cron.

Machine Learning Engineer sources the data (maybe from a data warehouse or other sources), performs data preparation, feature engineering, hyperparameter tuning, and model selection. Then they have to deploy the model. Many companies do this in the cloud now. For example, an mle could train a model using Google AutoML with data that he has in BigQuery, and then deploy to and endpoint in vertex AI (Azure and AWS have their similar product offerings). There is also model retraining in some cases.

Take a look at AWS or Google Cloud certifications for example and see what their certifications talk about for these roles: aws has machine learning and data engineering certifications and so does Google Cloud: [https://aws.amazon.com/certification/exams/](https://aws.amazon.com/certification/exams/)

[https://cloud.google.com/learn/certification?hl=en](https://cloud.google.com/learn/certification?hl=en)

Finally, you will notice that neither Google Cloud and AWS offer a data scientist certificate, but they do offer Machine Learning and Data Engineering certificates...

A data scientist is a bit more loosely defined depending on the company. Some define it as the people developing the models and performing machine learning research, but at others places these are called simply machine learning researchers...

FieldValue
text Data Engineer build ETL pipelines (extract transform and load) for example extracting data from a database like microsoft sql server, transforming it with pandas, and loading it to a data warehouse like BigQuery. Then you might schedule that pipeline to run at a specific interval, i.e. everyday at midnight, with a tool like apache airflow or cron. Machine Learning Engineer sources the data (maybe from a data warehouse or other sources), performs data preparation, feature engineering, hyperparam…
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-23
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Raw Record

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  "text": "Data Engineer build ETL pipelines (extract transform and load) for example extracting data from a database like microsoft sql server, transforming it with pandas, and loading it to a data warehouse like BigQuery. Then you might schedule that pipeline to run at a specific interval, i.e. everyday at midnight, with a tool like apache airflow or cron.\n\nMachine Learning Engineer sources the data (maybe from a data warehouse or other sources), performs data preparation, feature engineering, hyperparameter tuning, and model selection. Then they have to deploy the model. Many companies do this in the cloud now. For example, an mle could train a model using Google AutoML with data that he has in BigQuery, and then deploy to and endpoint in vertex AI (Azure and AWS have their similar product offerings). There is also model retraining in some cases.\n\nTake a look at AWS or Google Cloud certifications for example and see what their certifications talk about for these roles:  \naws has machine learning and data engineering certifications and so does Google Cloud:  \n[https://aws.amazon.com/certification/exams/](https://aws.amazon.com/certification/exams/)\n\n[https://cloud.google.com/learn/certification?hl=en](https://cloud.google.com/learn/certification?hl=en)\n\nFinally, you will notice that neither Google Cloud and AWS offer a data scientist certificate, but they do offer Machine Learning and Data Engineering certificates...\n\nA data scientist is a bit more loosely defined depending on the company. Some define it as the people developing the models and performing machine learning research, but at others places these are called simply machine learning researchers...",
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