98 points by tensorflow_enthusiast 6 months ago flag hide 10 comments
serverless_user 6 months ago next
Great article! I am impressed by the idea of creating a serverless image recognition system using TensorFlow.js. This is definitely a new approach to the machine learning domain. Looking forward to learning more about this from the community.
serverless_user 6 months ago next
@ai_expert Pre-trained models is an excellent suggestion. The article already mentioned the usage of TensorFlow.js's pre-trained image recognition models, which can be fine-tuned for a specific task. This will help in saving resources and training time.
ai_expert 6 months ago prev next
Indeed, a very interesting project! I think the serverless approach could bring numerous benefits, such as enhanced scalability and reduced operational costs. One thing I would suggest is to consider using pre-trained models in order to decrease the burden of model training time. What are your thoughts on this?
ml_enthusiast 6 months ago next
Very inspiring indeed. But I want to understand the infrastructure and cloud functions costs in this setup. Confused if it will be affordable for smaller-scale projects or startups with a limited budget.
serverless_user 6 months ago next
@ml_enthusiast Costs are always a concern while working on the cloud. For smaller projects, one can reduce the cost by opting for a smaller function instance size and setting appropriate limits on the maximum execution time. Additionally, while the article used AWS for demonstration purposes, you can also achieve similar results using other affordable cloud platforms.
cloud_architect 6 months ago prev next
Stellar project showcasing both image recognition and serverless domain knowledge! I would be concerned with the cold start issues that could occur with a serverless implementation, especially if there's heavy user traffic. How have you addressed this challenge?
serverless_user 6 months ago next
@cloud_architect You have raised a valid concern. This challenge was handled using techniques like function warm-up, predictive scaling, and providing a higher timeout limit for the function launches. More details regarding this are provided in the article.
quantum_developer 6 months ago prev next
That's a fascinating piece of work! Just curious, what constraints or limitations did you face when integrating TensorFlow.js with the cloud functions? It seems both have a lot of potential with limitations.
serverless_user 6 months ago next
@quantum_developer Indeed, there were certain constraints. Most significantly, TensorFlow.js requires an HTML file or a node.js-based application to run; otherwise, it would be hard to load models and execute them. However, by creating lightweight custom node.js implementations and hosting the HTML frontend on S3, this problem was resolved.
devops_master 6 months ago prev next
Great work, leveraging TensorFlow and cloud functions. Did you face any limitations while unit testing the functions after deployment?