Ritesh Vajariya

I think content strategy, email marketings and SEO optimization are all good but also consider: - paid advertising to draw the traffic (it helps!) - maybe add a chatBot to your website and integrate that with Amazon Bedrock or chatGPT. Let it do it's own magic. - Consider user interface changes. Many websites are clunky and that maybe one of the reason users don't stick to your site. Apply some AI based UI changes!

What about dynamic pricing and promotional strategies? AI can optimize pricing strategies in real-time based on market demand, customer behavior, and inventory levels, maximizing profitability. It can also automate and personalize promotional campaigns, offering tailored discounts and deals to individual customers. Many e-commerce websites have started doing that and maybe it's good strategy to attract more customers - as long as you don't do price gouging - thats illegal in most countries.

There are good model metrics are highlighted here. One thing I would add is potentially leveraging automated evaluation framework which reduces the burden on data scientist when doing the model evaluation. Another thing to consider is doing human evaluation of model where human provide the feedback and can be used to retrain the models - a similar mechanism is applied in NLP domain via RLHF/DPO.

This is the most important phase of the pipeline. While there are some good points added, few important aspects are: - potentially using distributed training mechanism.. in this case, image datasets are typically larger than your GPU memory and does requires distributing across multiple GPUs and some time multiple nodes. - to support that PyTorch has Distributed Data Parallel mechanism that can be utilized. - Additionally, Amazon SageMaker does offer PyTorch compatible library called SageMaker Distributed Data Parallel, which enhances performance up to 40% and may reduce the training time significantly.

I think the model building starts with choosing the base architecture. Since we are discussing image models, it's it's often beneficial to use established architectures like CNNs (Convolutional Neural Networks) with proven architectures like ResNet, Inception, or VGG. Lately YOLO has evolved a lot and offers better alternatives than other architectures. You can start with TensorFlow, PyTorch and other framework to build, it's advisable to see if one can use transfer learning. Transfer learning can significantly speed up the training process and improve model performance. Set the right hyperparameters (learning rate, batch size, optimizer) and apply appropriate regularization techniques.

for image preprocessing, it's mainly about remove duplicate images first, identify which images are relevant for the project and then start on resizing/crop etc. Some times it does require transformation of image, such as rotation or more of photometric changes such as adjusting brightness, hue etc. While it can be part of data preprocessing or separate step but feature engineering is the most important aspect in such project. Once you have features created, make sure to split the dataset in Training and Testing.