Body: <p>I've designed a machine learning model for the predictive maintenance of machines. The data used for training and testing the ML model is the data from various sensors connected to various parts of the machines. Now, I'm searching for a good approach for deploying the model in the real-time environment <a href="https://stackoverflow.blog/2020/10/12/how-to-put-machine-learning-models-into-production/">as explained here</a>. I did some research and found some information about using real-time data for prediction such as using <a href="https://www.confluent.io/blog/streaming-machine-learning-with-tiered-storage/" rel="nofollow noreferrer">Kafka</a>. I have some questions unanswered regarding the deployment of the ML model. Following are the details of my system:</p>
  <ul>
  <li>The sensors (pressure, temperature, flow, vibration, etc) are deployed across the parts of the machines.</li>
  <li>The ML model is trained with historical data.</li>
  <li>For predictive maintenance (anomaly detection), streams of data will be available via MQTT. As there are 3000 machines, the volume of data will be very high.</li>
  </ul>
  <p>My questions are:</p>
  <ul>
  <li>Where will be the best place to perform prediction operation, at the factory premice where machines are located (edge computing), at our office (that designs ML model), or at cloud server? I want to know it in regard to operational cost.</li>
  <li>Is there any way to estimate the effectiveness of the complete system (full-stack ML architecture)?</li>
  </ul>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-05T16:07:25.777Z
Comments.Text: These seem to be more-so questions of machine learning engineering than machine learning theory. ML engineering is a cross between software engineering and data science, and the role (of the so-called 'machine learning engineer') is more-so one of *implementing* machine learning models (than conceiving of statistical models to be used for some purpose, as the data scientist does). I doubt you'll get answers to such questions on this website. ML engineering itself is relatively new and does not have much formal theory (compared to, say, pure software engineering). [...]
Comments.UserDisplayName: The Pointer
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-07-05T16:27:41.247Z
Comments.Text: [...] This http://www.mlebook.com/wiki/doku.php is a recently published textbook on ML engineering (that I have purchased but not read) by Andriy Burkov; I suggest you read this as a starting point.
Comments.UserDisplayName: The Pointer
Tags: <machine-learning><data-science>
Title: Taking a machine learning model to production\deployment
UserDisplayName: santobedi
ThreadText: # Taking a machine learning model to production\deployment
  
  (Asked by: username_0 on 2021-06-25)
  
  I've designed a machine learning model for the predictive maintenance of machines. The data used for training and testing the ML model is the data from various sensors connected to various parts of the machines. Now, I'm searching for a good approach for deploying the model in the real-time environment [as explained here](https://stackoverflow.blog/2020/10/12/how-to-put-machine-learning-models-into-production/). I did some research and found some information about using real-time data for prediction such as using [Kafka](https://www.confluent.io/blog/streaming-machine-learning-with-tiered-storage/). I have some questions unanswered regarding the deployment of the ML model. Following are the details of my system:
  
  * The sensors (pressure, temperature, flow, vibration, etc) are deployed across the parts of the machines.
  * The ML model is trained with historical data.
  * For predictive maintenance (anomaly detection), streams of data will be available via MQTT. As there are 3000 machines, the volume of data will be very high.
  
  My questions are:
  
  * Where will be the best place to perform prediction operation, at the factory premice where machines are located (edge computing), at our office (that designs ML model), or at cloud server? I want to know it in regard to operational cost.
  * Is there any way to estimate the effectiveness of the complete system (full-stack ML architecture)?
  
  **Tags:** machine-learning, data-science
  > **Comments:**
  > **username_1**: These seem to be more-so questions of machine learning engineering than machine learning theory. ML engineering is a cross between software engineering and data science, and the role (of the so-called 'machine learning engineer') is more-so one of *implementing* machine learning models (than conceiving of statistical models to be used for some purpose, as the data scientist does). I doubt you'll get answers to such questions on this website. ML engineering itself is relatively new and does not have much formal theory (compared to, say, pure software engineering). [...]  
  > **username_1**: [...] This http://www.mlebook.com/wiki/doku.php is a recently published textbook on ML engineering (that I have purchased but not read) by Andriy Burkov; I suggest you read this as a starting point.  
  
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  ## 0 answers
  
  