A solution to the problem of facing a problem properly, without letting feel a problem as a problem.
Problem at a glance
A lot of difficulties are faced by beginners in AIML and Data Science. They don’t get proper directions on how to deal with a specific problem and hence they get frustrated and are not able to enter the data science community and contribute to it. There are many brilliants roaming around but due to improper vision, the AI and Data Science communities are not able to get the benefits of their brains (Everyone thinks differently and everyone solves the problem differently). Even been worth it, they don’t get the opportunity to prove themself. But what if by chance they get a chance and enter the industry?
Will their next chapters be easy or difficult?
Reducing deep learning model cost. Really?
One of the biggest challenges, A startup or an evolving entity faces when it enters the AI industry is the deployment with efficiency. Isn’t it?
Let’s dive a bit deeper
Everyone starts with the idea, follows the ML pipeline, and gets stuck somewhere near the deployment part. Let us suppose, speech-to-text has to be integrated into software but the person implementing it knows that there are two ways it can be done. i.e using deep-learning techniques or using speech-to-text APIs. But he or she will not be able to decide what exactly should be done.
Moving a step further
With a confused mind, the person will try both ways parallelly. But after entering the APIs marketplace the guy will literally get shocked by the pricing per second, so the next, solution that will be visible to the guy will be using deep learning (Here comes the madness).
The solution will be classified into 2 problematic ways.
- Using pre-trained models and finetuning them
- Using fine-tuned models
- Creating from scratch
But even after adopting any one of these, there will be a problem with the accuracy and inference timing. These two things matter the most for a product to get sold. Somewhere between these problems, the cherry on the top will be the model size due to which the delay occurs, which completely disappoints the user. There is no professional or expert in connection with that person who can at least show some glimpse of the solution. All hopes are loosened. Finally, we are stuck in a web of problems, and no solutions will be visible, in such a frustrating situation.
The perfect solution would be to forget everything, take a nap and read further
Hold a cup of coffee
Here are the ways, we can handle the situation:
- Firstly, plot the problem and the solutions on the paper, and be clear and know what exactly you are doing and what has to be done. Plan and move accordingly. The one (You) who is reading and (me) knows no one will be killing you if you don’t complete what you are supposed to complete. (Just keep such a mentality but don’t stop trying)
- Try researching more about the solutions with respect to the problem and give it some more time(Time Heals everything). Believe me, at the last, you will get whatever you want and all of your tasks will get completed with time.
We took the instance of speech-to-text into this article while creating the pressurizing scenario, Let’s also see how we can solve this problem if this occurs in our case.
If APIs are affordable to you, then you don’t need to read further (Thanks for visiting). But for the ones who don’t find them affordable (similar to my case) you should use pre-trained, finetuned models that are made deployment ready by several companies like Torch, TensorFlow, and Caffe. You will find TensorFlow hub, Torch hub, and many others that provide this kind of model. The next is you can find a way to deal with the size of the model, you can use various model quantization and pruning kind of techniques that are usually ignored by many. This will automatically reduce the cost and increase the performance with an increase in acceleration and reducing the size of the model.
The problem also arises if the deployment is on CPU-based devices like edge devices (ex. IoT Devices). ONNX platform and many others are available, that boost the inference time of a model on a CPU-based machine. This overall strategy will optimize the project to 70%. Optimization is done in terms of speed but what about the accuracy of the flow?
You should fine-tune the model on a specific task if you expect customer care service from it, you should finetune it on the same data, and on the same kind of data. If the model is trained on domain-specific data it will deliver a good number of accuracy
Finetuning the model regularly or several times, you’ll notice a lag in the accuracy and this is nothing new it’s a common issue faced by people. There are a number of research done on the model retraining approach and its challenges, they show that retraining a model will make the model forget the data it has been trained in the past and gets trained on the new data (Don’t ask from the past). This phenomenon is called data drift. This happens due to the change in the distribution of the data. There are some techniques as well as precautions that should be applied to it to avoid such behavior of a model.
Finally, we put the model into production. This is not the end there is more to monitoring that would exist, but what we can do is just try to reduce the problems and make the pipeline error-free to enhance the user experience.
Conclusion
Even if it has been a long since AI has evolved, it has been difficult for companies to implement it and apply it in a proper direction. Even today there are recurring problems for which only 60% of the solutions are available. The problems are not completely vanished but are under research and hence they are a bit complex to bring into use.
Even if solutions are available to a problem over the internet still it takes time for an individual to implement them properly, we come across a lot of obstacles (especially in terms of a beginner). We get used to something when it is done ‘n’ a number of times in different ways.
My suggestions
Not the forums, but a separate platform should be made completely free to yield more Elon’s. This will just help the guy to acquire the proper knowledge and the world will for sure experience something new.
Where knowledge meets worthiness, magic happens.
References
You won’t find any references, this has been recorded in my mind for a long time, even though I have experienced this kind of thing. Things get better when we want them to. Just an external force is required and that is you. Believe in yourself. Just a quick motivation.
Get to work. All the best.
Happy Diwali
