Based on our record, Google Cloud Functions seems to be a lot more popular than Azure Machine Learning Service. While we know about 41 links to Google Cloud Functions, we've tracked only 4 mentions of Azure Machine Learning Service. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
One of the issues developers can encounter when developing in Cloud Functions is the time taken to deploy changes. You can help reduce this time by dynamically loading some of your Python classes. This allows you to make iterative changes to just the area of your application that you’re working on. - Source: dev.to / 5 months ago
I've been looking at Google Secret Manager which sounds promising but I've not been able to find any examples or tutorials that help with the actual practical details of best practice or getting this working. I'm currently reading about Cloud Functions which also sound promising but again, I'm just going deeper and deeper into GCP without feeling like I'm gaining any useful insights. Source: 6 months ago
Serverless computing was also introduced, where the developers focus on their code instead of server configuration.Google offers serverless technologies that include Cloud Functions and Cloud Run.Cloud Functions manages event-driven code and offers a pay-as-you-go service, while Cloud Run allows clients to deploy their containerized microservice applications in a managed environment. - Source: dev.to / 9 months ago
Lambda is made for your use case :). It doesn’t have to be AWS there are plenty of other serverless computing services like: - Google cloud functions - Azure functions Etc. Source: 11 months ago
Once you have some basic familiarity with programming, try deploying one of your Python programs to the cloud. Start with Cloud Functions, because that doesn't require any knowledge of Linux server administration. Source: 11 months ago
Building an AI solution requires more than just one person. You need a team of experts who can work together efficiently and creatively. That’s why you need a platform that supports collaboration and communication among your AI team members. Azure Machine Learning Studio is not only a powerful infrastructure for computation and technical tasks, but also a management tool that helps you organize and streamline your... - Source: dev.to / 10 months ago
I'm biased, but giving my honest personal opinion here, I think this sounds like a bad idea. I'm not optimistic about Databricks long term. They are a data prep company masquerading as a data science company. Nothing wrong with that, but Spark resources are expensive compared with SQL, and they are at risk from all fronts (Cloud providers, Snowflake, AI/ML platform players, etc.). I see their Databricks controlled... Source: about 2 years ago
Azure Machine Learning An enterprise-grade service for the end-to-end machine learning life cycle that allows you to build models at scale. - Source: dev.to / about 2 years ago
Azure Machine Learning (specifically for Energy and Manufacturing. Source: about 3 years ago
Google App Engine - A powerful platform to build web and mobile apps that scale automatically.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Salesforce Platform - Salesforce Platform is a comprehensive PaaS solution that paves the way for the developers to test, build, and mitigate the issues in the cloud application before the final deployment.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Dokku - Docker powered mini-Heroku in around 100 lines of Bash
Dataiku - Dataiku is the developer of DSS, the integrated development platform for data professionals to turn raw data into predictions.