Power to develop AI lead solutions in hands of a Common Person

Artificial intelligence and machine learning has changed the way our world works in an unprecedented way. From complex brain surgeries to a simple alarm clock, from assisting a pilot to fly the plane, to helping a little kid improve their pronunciation, the “machine brain” has found its place in every nook and corner of our lives.

But is this wonder of technology democratically available? With great hardware requirements and heavy reliance on big data, this may be a debatable issue. However armed with the right tools, one can develop their own AI/ML solutions.

One of the biggest hurdle one faces during the development and training of their machine learning model is the hardware resources that the model requires. Especially now when GPU prices are at an all-time high, it is a big entry barrier for many machine learning enthusiasts. A viable solution to this problem is using Google Colab and Jupyter notebook. Jupyter is an open-source community supported service that provides all its services for free. Jupyter notebook doesn't offer cloud-based hardware assistance but one can run their Jupyter notebook on the cloud using alternate methods that are easy to implement and free plans. Google colab has inbuilt cloud support which helps devices with weak hardware to develop and train their models using the cloud. It can be defined as a free Jupyter notebook environment that runs entirely in the cloud. Google colab is a freemium service that has free as well as paid plans for its users. The free plan offers 12 GB of RAM (extendible up to 25 GB).

After solving the hardware issues, one can look forward to the development process, but writing machine learning programs that are best suited and most efficient logically, is a tedious and long task. One can take the help of powerful, robust and easy to use built-in libraries. These libraries are capable of handling tasks starting from the preprocessing of data to the fine-tuning of the precision of the model. For preprocessing, working with our dataset and doing the mathematical computations, Numpy and Pandas are some of the best libraries available out there. They have a big community which makes the debugging process simpler and more efficient. To develop deep learning models Pytorch, TensorFlow, Keras and FANN are some of the best libraries. Choosing a suitable library and a machine learning model that is fit for your data is a very crucial stop.?

The final step in all machine learning projects is the deployment of the model. Using a microservice like Django or Flask to deploy the models is becoming an industry practice. For example, when I wanted to deploy my machine learning project, “Stock market analysis and Comparative Study of models, using ANN and RNN LSTM ”, then to run the python scripts, we used the Flask micro-framework. Another issue we encountered was the huge file size of the libraries and the model. We were able to solve the library size issue by uploading only the specific code file used in the development model. There are many services that offer free plans or resource-limited free hosting options that one can use to host their model online.Heroku and pythonanywhere are two such services that get the job done nicely.

Amazing applications, solutions to mind-boggling problems or anything under the sky, can be developed utilizing the power of machine learning. The best time is now, with the knowledge of machine learning let's start our journey to “To infinity and beyond”.

Note: A list of? tools we talked about:

1. Hardware/Development Environment: Google Colab and Jupyter notebook
2. ML/AI libraries: Numpy, Pandas, Pytorch, TensorFlow, Keras and FANN
3. Python web frameworks: Flask and Django
4. Hosting: Github, Heroku and pythonanywhere