The Resource Packaging Machine Learning Models with Docker, Deza, Alfredo
Packaging Machine Learning Models with Docker, Deza, Alfredo
Resource Information
The item Packaging Machine Learning Models with Docker, Deza, Alfredo represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Merrimack Valley Library Consortium.This item is available to borrow from 1 library branch.
Resource Information
The item Packaging Machine Learning Models with Docker, Deza, Alfredo represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Merrimack Valley Library Consortium.
This item is available to borrow from 1 library branch.
- Summary
- One of the important aspects of MLOps, also known as Machine Learning Operations or Operationalizing Machine learning, is to package ML models. How exactly do you package ML models? In this video I show you exactly what that means, and go through the process of packaging an ONNX model taken from the ONNX Model Zoo. I end up with a docker container that can be shared, exposing an API that is ready to consume and perform live predictions for sentiment analysis. Topics include: * What are the concepts behind packaging Machine Learning Models * Create a sentiment analysis API tool with Flask * Define dependencies and a Dockerfile for packaging * Create a container with an ONNX model that can be deployed anywhere with an HTTP API A few resources that are helpful if you are trying to get started with SBOMs, generating them and using them to capture vulnerabilities: * The RoBERTa ONNX Model * Schema labeling concetps for Docker containers * The Practical MLOps code respository full of examples
- Language
-
- eng
- eng
- Edition
- 1st edition
- Extent
- 1 online resource (1 video file, approximately 37 min.)
- Label
- Packaging Machine Learning Models with Docker
- Title
- Packaging Machine Learning Models with Docker
- Statement of responsibility
- Deza, Alfredo
- Language
-
- eng
- eng
- Summary
- One of the important aspects of MLOps, also known as Machine Learning Operations or Operationalizing Machine learning, is to package ML models. How exactly do you package ML models? In this video I show you exactly what that means, and go through the process of packaging an ONNX model taken from the ONNX Model Zoo. I end up with a docker container that can be shared, exposing an API that is ready to consume and perform live predictions for sentiment analysis. Topics include: * What are the concepts behind packaging Machine Learning Models * Create a sentiment analysis API tool with Flask * Define dependencies and a Dockerfile for packaging * Create a container with an ONNX model that can be deployed anywhere with an HTTP API A few resources that are helpful if you are trying to get started with SBOMs, generating them and using them to capture vulnerabilities: * The RoBERTa ONNX Model * Schema labeling concetps for Docker containers * The Practical MLOps code respository full of examples
- Characteristic
- videorecording
- http://library.link/vocab/creatorName
- Deza, Alfredo
- http://library.link/vocab/relatedWorkOrContributorName
-
- Gift, Noah
- O'Reilly Media Company
- Label
- Packaging Machine Learning Models with Docker, Deza, Alfredo
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Color
- multicolored
- Content category
- two-dimensional moving image
- Content type code
-
- tdi
- Content type MARC source
- rdacontent
- Dimensions
- unknown
- Edition
- 1st edition
- Extent
- 1 online resource (1 video file, approximately 37 min.)
- Issuing body
- Made available through: O'Reilly Media Company.
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Reproduction note
- Electronic reproduction.
- Specific material designation
- remote
- System control number
- (CaSebORM)50116VIDEOPAIML
- System details
- Mode of access: World Wide Web
- Label
- Packaging Machine Learning Models with Docker, Deza, Alfredo
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Color
- multicolored
- Content category
- two-dimensional moving image
- Content type code
-
- tdi
- Content type MARC source
- rdacontent
- Dimensions
- unknown
- Edition
- 1st edition
- Extent
- 1 online resource (1 video file, approximately 37 min.)
- Issuing body
- Made available through: O'Reilly Media Company.
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Reproduction note
- Electronic reproduction.
- Specific material designation
- remote
- System control number
- (CaSebORM)50116VIDEOPAIML
- System details
- Mode of access: World Wide Web
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.mvlc.org/portal/Packaging-Machine-Learning-Models-with-Docker/s9CF9-xWTWU/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.mvlc.org/portal/Packaging-Machine-Learning-Models-with-Docker/s9CF9-xWTWU/">Packaging Machine Learning Models with Docker, Deza, Alfredo</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.mvlc.org/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.mvlc.org/">Merrimack Valley Library Consortium</a></span></span></span></span></div>
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.mvlc.org/portal/Packaging-Machine-Learning-Models-with-Docker/s9CF9-xWTWU/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.mvlc.org/portal/Packaging-Machine-Learning-Models-with-Docker/s9CF9-xWTWU/">Packaging Machine Learning Models with Docker, Deza, Alfredo</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.mvlc.org/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.mvlc.org/">Merrimack Valley Library Consortium</a></span></span></span></span></div>
