Many developers use tox as a solution to standardize and automate testing in Python. However, using the tool only for test automation severely limits its power and the full scope of what you could achieve. For example, tox is also a great solution for the “it works on my machine” problem. There are several reasons for this, such as: In addition, and most importantly…
]]>This series looks at the development and deployment of machine learning (ML) models. In this post, you deploy ML models on Google Cloud Platform. Part 1 gave an overview of the ML workflow, considering the stages involved in using machine learning and data science to deliver business value. In part 2, you trained an ML model and saved that model so it could be deployed as part of an ML system.
]]>This series looks at the development and deployment of machine learning (ML) models. In this post, you train an ML model and save that model so it can be deployed as part of an ML system. Part 1 gave an overview of the ML workflow, considering the stages involved in using machine learning and data science to deliver business value. Part 3 looks at how to deploy ML models on Google Cloud Platform…
]]>This series looks at the development and deployment of machine learning (ML) models. This post gives an overview of the ML workflow, considering the stages involved in using machine learning and data science to deliver business value. In part 2, you train an ML model and save that model so it can be deployed as part of an ML system. Part 3 shows you how to deploy ML models on Google Cloud Platform…
]]>Machine learning models are increasingly used to make important real-world decisions, from identifying fraudulent activity to applying automatic brakes in a car. The job of a machine learning practitioner is far from over once a model is deployed to production. You must monitor your models to ensure they continue to perform as expected in the face of real-world activity. However…
]]>Data is one of the most valuable assets that a business can possess. It sits at the core of data science and data analysis: without data, they’re both obsolete. Businesses that actively collect data may have a competitive advantage over those that do not. With sufficient data, organizations can better determine the cause of problems and make informed decisions. There are scenarios where an…
]]>Imagine that you’re working on a machine learning (ML) project and you’ve found your champion model. What happens next? For many, the project ends there, with their models sitting isolated in a Jupyter notebook. Others will take the initiative to convert their notebooks to scripts for somewhat production-grade code. Both of these end points restrict a project’s accessibility…
]]>Deploying an application using a microservice architecture has several advantages: easier main system integration, simpler testing, and reusable code components. FastAPI has recently become one of the most popular web frameworks used to develop microservices in Python. FastAPI is much faster than Flask (a commonly used web framework in Python) because it is built over an Asynchronous Server…
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