Machine Learning’s journey had taken a rapid turn to transformation when it switched from an experimental technology to an applied one. The level of adoption of this technology is also gradually growing in all sectors of the market.
2021 is expected to see revolutionary modifications to Artificial intelligence and Machine Learning technology. Therefore, it is the right time for all the entrepreneurs to get their hands on the extensive market of ML technologies and leverage their use to reach your desired target audience and provide a fantastic user experience.
As per industry research, enterprises prefer to invest in ML primarily to reduce manual work and cost. In comparison, startups use it for customer-related tasks, like gathering insights for refined services and customer retention. ML can also be used to get a competitive edge over other businesses in the market.
You, too, can obtain these benefits of machine learning for your business!
That’s the question! How to develop an application that can elevate your business scope with the power of machine learning?
Well, you’re in luck, now that you’ve arrived at the write-up you were looking for.
Developers are often confused between two robust languages while developing an ML application, i.e., Python and Java.
While developers like Python for its simplicity, Java is liked for its maturity.
In simple words, It is open-source in nature and helps developers use the client-side of an application to deploy and build ML models.
Developers find it exciting to use TensorFlow.js as its use allows them to:
- Retain or run existing, pre-trained ML models
The language is also compatible with its namesake, TensorFlow (Used for python development in ML). This means any model based on TensorFlow can be converted and run in the browser via TensorFlow.js.
The fact that TensorFlow.js can run within a browser opens up many possibilities for business owners worldwide.
Browsers are interactive spaces; they offer access to multiple sensors like microphones or webcams, which can help provide visuals and sounds into an ML model.
The advantages of TensorFlow.js:
What’s more? Its compatibility with the Python library offers it a lower threshold. Therefore, developers who are just starting with ML can effectively use it.
Many times web scripting languages can open up vulnerabilities. However, TensorFlow.js has a reputation for a secure execution environment. This ensures your user’s devices will stay protected when accessing an application.
Let’s now study the cons of TensorFlow.js to make an informed choice.
The Disadvantages of TensorFlow.js:
The framework has limited support for hardware acceleration. However, this aspect is improving significantly since the language evolves beyond v1.0.
Despite all the advantages TensorFlow.js has, it still does not have default access to the file system in the host browser environment. This puts restrictions on file sizes as it can limit the data resources that are available to the app developer.
However, a developer can tackle this if a s/he yields the main thread for enhancing the page responsiveness during the algorithm’s training.
And a NodeJS environment ensures developers can queue tasks in the event loop so that they can be handled at the right time, in the right way.
Now we’ve understood what benefits and drawbacks surround the incredible TensorFlow.js. But how to figure out what it actually can do?
Well, here’s what’s possible with TensorFlow.js.
Currently, developers in heavy numbers are moving to use ML in the front-end instead of the back-end of an app.
Thanks to TensorFlow, developers can now run and create ML models using static HTML documents without setting up a database or a server. This enables the services to be hosted entirely on the client-side of your application.
- Offline Game Opponents:
Users can play video games even when they’re offline by playing against an AI-operated opponent.
- Automatic Picture Manipulation:
You can develop art using the conventional neural networks, as done by Google. One can do this by auto-adjusting images based on the predefined rules using a browser-based app.
- Activity Monitoring:
Using ML, you can now create an application to learn usage patterns and predict user behavior to generate more personalized outputs. This can be installed on a local device or network and can be used to detect unusual activities or red flags.
- Object Detection:
You can create a client-side application that detects objects or elements in pictures. For example, this technology is integrated by Airbnb to alert users when they’re uploading personal documents such as driving license or passport details.
Therefore, many big brands are using it to experiment with their applications with machine learning using TensorFlow.js. And with time, the library is also significantly growing to become much more valuable.