Based on our record, Google Cloud Functions should be more popular than monkeylearn. It has been mentiond 43 times since March 2021. 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.
The first reason is that serverless architectures are inherently scalable and elastic. They automatically scale up or down based on the incoming workload without requiring manual intervention through serverless compute services like AWS Lambda, Azure Functions, or Google Cloud Functions. - Source: dev.to / 27 days ago
The FaaS platform gained a lot of popularity which resulted in many competitors. There was OSS providers like OpenFaaS or Fission. There were of course the commercial versions to like Azure Functions and Google Cloud Functions. - Source: dev.to / about 1 month ago
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 / 7 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: 8 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 / 11 months ago
MonkeyLearn: A platform for text analysis and machine learning, allowing users to train custom models for tasks like sentiment analysis and topic classification. Source: 6 months ago
Monkeylearn.com — Text analysis with machine learning, free 300 queries/month. - Source: dev.to / over 1 year ago
MonkeyLearn supports 11 languages for data analysis (Spanish, Portuguese, German, Russian, Italian, French, Dutch, Chinese, Japanese, Korean and Arabic). But for sentiment analysis, only Spanish seems to be available, I’m not sure about that. Source: over 1 year ago
R3: Used RedditExtractoR in R to download all-time top posts, and ran the resulting .csv through https://monkeylearn.com/. Downloaded the resulting table and deleted top result "OC" - then visualized it with ggplot to give a sense of absolute numbers. Total posts considered in this are 988, the word cloud only looks at the 98 most mentioned words/phrases. Let me know if you have got any questions/concerns! Source: almost 2 years ago
Go to monkeylearn.com and sign up for a free demo. Then cut and paste your blog text into the extractor/classifier. Source: about 2 years ago
Google App Engine - A powerful platform to build web and mobile apps that scale automatically.
Amazon Comprehend - Discover insights and relationships in text
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.
Google Cloud Natural Language API - Natural language API using Google machine learning
AWS Lambda - Automatic, event-driven compute service
spaCy - spaCy is a library for advanced natural language processing in Python and Cython.