Row 71644

Row ID: 71644 | Dataset Entry | Axioma AXP Content Repository

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As far as I've seen, a DL/ML **"Engineer"** is mostly involved in shipping/deployment, operations, and maintaining the ML models and products. Not that they'll not need the theory, but that won't come up in job as much.

The best way to see what tools are used in such roles is to screen LinkedIn/Indeed job descriptions for them. You'll see Pytorch/TF is only one of the many important reqs. They mostly require Cloud dev skills such as AWS, GCP, Databricks, snowflake, ... as well as some MLOps and data management tools such as MLFlow, KubeFlow, AirFlow, Spark, etc.. Still, it really depends on the company and the team. The definition of the role can vary for different companies, but as shown in the name, they involve more **"Engineering".**

On the other hand, the DL course you mentioned with lots of theory involved, while still needed for the above roles, would mainly work best for a career in data science and research science. Data/research scientists are usually the ones who do the modeling and define the models. It involves more math/theory, experimentation, and reasoning than engineering. You can see other names for such roles like applied scientist or ML/AI scientist.

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text As far as I've seen, a DL/ML **"Engineer"** is mostly involved in shipping/deployment, operations, and maintaining the ML models and products. Not that they'll not need the theory, but that won't come up in job as much. The best way to see what tools are used in such roles is to screen LinkedIn/Indeed job descriptions for them. You'll see Pytorch/TF is only one of the many important reqs. They mostly require Cloud dev skills such as AWS, GCP, Databricks, snowflake, ... as well as some MLOps an…
label r/deeplearning
dataType comment
communityName r/deeplearning
datetime 2024-05-24
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{
  "text": "As far as I've seen, a DL/ML **\"Engineer\"** is mostly involved in shipping/deployment, operations, and maintaining the ML models and products. Not that they'll not need the theory, but that won't come up in job as much. \n\nThe best way to see what tools are used in such roles is to screen LinkedIn/Indeed job descriptions for them. You'll see Pytorch/TF is only one of the many important reqs. They mostly require Cloud dev skills such as AWS, GCP, Databricks, snowflake, ... as well as some MLOps and data management tools such as MLFlow, KubeFlow, AirFlow, Spark, etc.. Still, it really depends on the company and the team. The definition of the role can vary for different companies, but as shown in the name, they involve more **\"Engineering\".**\n\nOn the other hand, the DL course you mentioned with lots of theory involved, while still needed for the above roles, would mainly work best for a career in data science and research science. Data/research scientists are usually the ones who do the modeling and define the models. It involves more math/theory, experimentation, and reasoning than engineering. You can see other names for such roles like applied scientist or ML/AI scientist.",
  "label": "r/deeplearning",
  "dataType": "comment",
  "communityName": "r/deeplearning",
  "datetime": "2024-05-24",
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