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Big Data LDN
Big Data LDN 2019 | ML in Production: Serverless and Painless | Oliver Gindele
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Big Data LDN 2019 | ML in Production: Serverless and Painless | Oliver Gindele
Read more
Big Data LDN 2019 | ML in Production: Serverless and Painless | Oliver Gindele
Productionising machine learning pipelines can be a daunting and difficult task for Data Scientists. Fortunately, many novel tools and technologies have become available in the past years to address this issue and make it easier than ever to deploy ML models into production, without the need to configure servers. In this session, Oliver Gindele will walk through some of the best serverless options on how to operationalise ML pipelines within the Tensorflow ecosystem and on Google Cloud Platform based on actual case studies. One of these real-life case studies will dive into the journey of a global cosmetics brand to become packaging-free with the help of ML. The first step towards this goal allows customers to view product information simply by taking a picture. This completely eliminates the need for packaging and labels in stores. However, in order to do this effectively, an accurate image classification model, accessible on mobile phones, is needed. This session will cover the details of the end-to-end machine learning pipeline that was created to deliver and update performant ML models to mobile users.
This course is no longer available. Find a similar course by searching these:
serverless architecture
end-to-end ml pipelines
serverless machine learning
machine learning pipelines
image classification models
ml productionization
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What's inside
Syllabus
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Focuses on operationalising ML pipelines within the Tensorflow ecosystem and on Google Cloud Platform based on actual case studies
Develops serverless options for operationalising ML pipelines in real-world scenarios
Oliver Gindele, an industry professional, is the instructor of this course
Examines the journey of a global cosmetics brand to become packaging-free with the help of ML
Applicable to learners interested in implementing ML solutions in a production environment
Caution: The course assumes familiarity with the Tensorflow ecosystem
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Career center
Learners who complete ML in Production: Serverless and Painless will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for developing and maintaining machine learning pipelines, which are used to automate the process of building and deploying machine learning models. This course provides a comprehensive overview of the serverless options available for operationalising ML pipelines within the Tensorflow ecosystem and on Google Cloud Platform. By taking this course, you will gain the skills and knowledge necessary to successfully deploy and manage ML models in production, which is a key responsibility of Data Scientists.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. This course provides a deep dive into the technical aspects of operationalising ML pipelines, including topics such as model deployment, monitoring, and scaling. By taking this course, you will gain the skills and knowledge necessary to successfully build and deploy ML models in production, which is a key responsibility of Machine Learning Engineers.
Cloud Architect
Cloud Architect
Cloud Architects are responsible for designing and managing cloud-based infrastructure. This course provides a comprehensive overview of the serverless options available for operationalising ML pipelines on Google Cloud Platform. By taking this course, you will gain the skills and knowledge necessary to successfully design and manage cloud-based infrastructure for ML pipelines, which is a key responsibility of Cloud Architects.
DevOps Engineer
DevOps Engineer
DevOps Engineers are responsible for bridging the gap between development and operations teams. This course provides a deep dive into the technical aspects of operationalising ML pipelines, including topics such as continuous integration and continuous delivery (CI/CD). By taking this course, you will gain the skills and knowledge necessary to successfully build and deploy ML models in production, which is a key responsibility of DevOps Engineers.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. This course provides a comprehensive overview of the serverless options available for operationalising ML pipelines within the Tensorflow ecosystem. By taking this course, you will gain the skills and knowledge necessary to successfully build and deploy ML models in production, which is a key responsibility of Software Engineers.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data. This course provides a deep dive into the technical aspects of operationalising ML pipelines, including topics such as data preprocessing and feature engineering. By taking this course, you will gain the skills and knowledge necessary to successfully build and deploy ML models in production, which is a key responsibility of Data Analysts.
Business Analyst
Business Analyst
Business Analysts are responsible for understanding the business needs of an organization and translating them into technical requirements. This course provides a comprehensive overview of the serverless options available for operationalising ML pipelines within the Tensorflow ecosystem and on Google Cloud Platform. By taking this course, you will gain the skills and knowledge necessary to successfully translate business needs into technical requirements for ML pipelines, which is a key responsibility of Business Analysts.
Product Manager
Product Manager
Product Managers are responsible for managing the development and launch of new products. This course provides a deep dive into the technical aspects of operationalising ML pipelines, including topics such as product roadmap development and stakeholder management. By taking this course, you will gain the skills and knowledge necessary to successfully manage the development and launch of new ML-powered products, which is a key responsibility of Product Managers.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. This course provides a comprehensive overview of the serverless options available for operationalising ML pipelines within the Tensorflow ecosystem and on Google Cloud Platform. By taking this course, you will gain the skills and knowledge necessary to successfully plan, execute, and close projects involving ML pipelines, which is a key responsibility of Project Managers.
Technical Writer
Technical Writer
Technical Writers are responsible for creating and maintaining technical documentation. This course provides a deep dive into the technical aspects of operationalising ML pipelines, including topics such as documentation standards and best practices. By taking this course, you will gain the skills and knowledge necessary to successfully create and maintain technical documentation for ML pipelines, which is a key responsibility of Technical Writers.
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Time
33 hours
Level
Intermediate
Via
Pluralsight
Instructor
Big Data LDN
Language
English
This course belongs to a
collection
called
Big Data LDN 2019 . It's comprised of 101 courses:
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