While theoretical skills about machine learning is vital, most companies value integrating these skills in developing useful solutions. And it goes without saying that the world of Machine Learning (ML) and web development is evolving at a rapid pace. To tackle the future challenges, you should build applied skills through project-based learning.
Learning the theories of ML wouldn’t be enough to assist you in your endeavors to build a better career. There is no denying that we all have across applications that use machine learning. For instance, YouTube and Netflix leverage ML to customize your experience. They use ML to recommend you with new and upcoming content based on your browsing behaviors.
Even you can develop such an application in which you’ll feed the input data. Integrating the web app with ML will allow it to provide the desired output. You can put the ML models into better use by integrating them into an application.
This not underlines your knowledge about ML, but also showcases your skills in the web app development projects. A top web development company is well aware of the intricacies involved in integrating ML models into a web app. Here are some top ways in which you can integrate machine learning into web development.
Develop your custom machine learning model
The best way to integrate machine learning into web development is by building a model. As a developer, you can also leverage the use of built-in linear regression. After building the machine learning model with Python, ensure to save it properly. You can use this file to predict the output when new input data is offered to the web app.
Create the web application with Flask and integrate the machine learning model
After creating the model, you should start developing the web app with Flask. In case you don’t know, Flask is a lightweight WSGI web app framework. It is developed to make the developers feel at ease with web development projects.
One of the key highlights of Flask is its ability to scale rapidly to complex web apps. Flask is the most developer-friendly framework out there. This is because it offers suggestions and doesn’t enforce any type of dependencies. To install Flask, simply use the command ‘pip install flask’. In the next step, you should import all the necessary libraries and initialize the flask app.
You should also load the ML model. Always remember to define the route of the app for the default page of the web app. Here the routes refer to the URL patterns of a particular app. You should develop a new app route to redirect the API properly. Start the server of the flask to ensure that there are no issues.
Deploy the web application on Heroku
After testing the web application on the local server, you should deploy the application on the cloud server. To make the app responsive, it is always a great idea to deploy it on the cloud. However, to deploy the app on cloud, you need two files named as Procfile and requirements.txt. And before creating Procfile, you should install Gunicorn.
The purpose of the Procfile is that it specifies the commands executed by the cloud. You should also create the requirements.txt file. This file comprises all the dependencies on the flask app. In the next step, you should upload all these files to the GitHub repository.
Hone your machine learning programming skills with Google search analysis
Most Google users conduct more than 4 billion search queries per day. And if you are wondering what people are Googling, you should refer to Google Trends. The capabilities of Google Trends are made possible through an API called ‘pytrends’ that provides access to its vast database. You can use this API to locate the countries with the highest number of search traffic.
You can also recognize the total number of machine learning search engine queries that have changed over time. To integrate machine learning into web development, you should have the ability to develop your own data visualization. In machine learning, data visualization is the most important aspect.
The widespread adoption of machine learning
Today, companies and organizations are increasingly adopting machine learning for various uses. ML has currently become a potent tool that has enabled organizations to stand out with their competitors, ease tasks and reduce the probability of making errors.
The agricultural sector has not been left out in using machine learning. Considering that the agricultural sector significantly contributes to most countries’ economies, both developing and developed countries, ML can be potentially used to improve both productivity and effectiveness. We can also comprehend what the widespread use of machine learning could mean for the global economic situation.
Ways in which ML is helping companies and industries
Factors including climate change and demographic issues are some of the concerns that greatly encourage the adoption of ML to improve food security and seek improved ways of protecting crops. However, it is notable that as humans increasingly opt for machine learning and machine learning, human labor is slowly becoming replaced, which is causing a threat to human work. Some applications of ML that are rapidly replacing human labor include using agricultural robots.
Reducing human labor to a great extent
An example is the recent invention of a robotic drone to spray pesticides. Its main aim is to control and oversee the spraying of weeds and cotton plants. This robotic machine has significantly reduced human labor. Additionally, the agricultural field has developed applications for monitoring crop and soil health, formerly done by humans. Machine algorithms have also been increasingly adopted in predictive analysis to monitor crop yields. Agricultural companies are coming up with their own set of inventions to monitor crop yields.
A brief on neural networks and their role in ML
Neural networks fall under the subset category of deep learning and in the machine learning class. Every day huge amounts of data are being collected daily, and this means it is now easy to train the models in deep learning with higher accuracy, given that more data increases the chance of accuracy in models. Neural networks are developed to operate the same as the human brain. However, given the speed and the accuracy comparison of the machine and the human brain, it can be concluded that the human brain is more complex, possess high parallel computing power as well as is nonlinear. You can know more about neural networks.
The human brain is capable of recognizing objects incompatibly quickly in comparison to the fastest computer in the world. However, there have been numerous advancements aimed at achieving the level of processing power the brain has in machine learning. As we explore the use of ML in agricultural industries, it is no doubt that human will increasingly lose their jobs as the agricultural sector competes for higher volumes and pace compared to human laborers.
