Friday, December 9, 2022
Published 2 Months Ago on Monday, Oct 03 2022 By Adnan Kayyali
Machine learning applications have become such a prevalent part of out lives that we barely notice them in our daily lives anymore. Whether asking Siri for the best burger place around us or getting music recommendations on Spotify, ML is everywhere. As an aspiring developer, you might be wondering how to build machine learning applications to get in on the trend early in your career.
Can we use this ML capability for our own mobile applications? In this blog post we will give you an overview of what you need to know to add machine learning capabilities to your mobile app.
Before you start thinking of building a machine learning app, first you have to build an app in general. ML is more of an add-on on top of a traditional application, and so choose your language and get building.
Depending on whether you want to build an iOS or Android app, you should learn the languages and frameworks for that platform first.
After that, you will need to learn the fundamental of machine learning and libraries. Thankfully there are all kinds of free and open courses online where you can begin your learning journey, with MOOC being a good platform to start.
Here are all the steps to take on your path to building a machine learning application from scratch.
Here we talk business first. What is the purpose of using machine learning in this app? How will it benefit customers? Can the customer’s issue be solved without machine learning? If there is no added value to using ML in your app or the task can be fulfilled with regular algorithms then there is no need to go dabbling in ML.
Starting a machine learning application is a difficult undertaking, therefore it only makes sense to spend enough time thinking about alternate solutions first.
To create a machine learning mobile application, you will need more than just data analysts/scientists who handle the data models. Yes, they are the core, bit for a successful project you will need a team of app developers, full-stack backend developers, designers, and QA engineers.
Nowadays, developers have more choice as to where to place the machine learning component of your app since modern smartphones include faster CPUs, GPUs, and specialized ML chips.
When deciding how to build machine learning applications for your business, it is vital to consider these options. Your main choices are:
It is the easiest according to many ML developers, however that does not decrease form the complexity of building ML infrastructure, it simply moves it to the cloud. Choose cloud-ML-hosting when:
Alternatively, there are boons to keeping the machine learning process on the device itself.
Although the hybrid option has the best user experience, it is also somewhat expensive. Take for example a translation app that does most of its language processing on the cloud but allows users to converse offline after the ML module has been downloaded.
You may decide whether to employ pre-made machine learning components or develop a totally customized solution while building a machine learning software from start.
Naturally, building from scratch provides more flexibility. The tech stack and tools you use to build the AI core of your mobile app are completely up to you. At the same time, pre-built ML services take much of the work out of creating unique ML capabilities.
The availability of data is precisely the reason why My developers often question if they have enough data, and will be addressing such issues for a while.
This is purely because the foundation of AI features is data analysis: the more data you gather for processing, the better the outcomes.
However, Making a machine learning program requires more than simply collecting data. Before using data to build models, we must pass it through a number of pre-processing steps:
Data can originate from several sources. It must thus be purified and given a consistent format. These processes involve a variety of manipulations, including the conversion of all data to numerical values, the elimination of statistical noise and mistakes, the identification of the most pertinent input variables, etc.
Making ML models is the next stage in designing a machine learning mobile app. The actual magic of ML takes place there.
I wish I could tell you more, but the truth is that it’s an extremely difficult topic that would likely require a lengthy reading to even scratch the surface of. And for that reason, you require a development team made up of knowledgeable data science engineers.
They experiment with learning methods and build models that produce the required outcomes using the tools I’ve listed above, such as PyTorch or TensorFlow. In the process, they assess many models, combine them into one, or select one that performs noticeably better than the others.
When determining which class each item belongs, data scientists develop either classification models ( when determining continuous values), or regression models whenever continuous values need to be predicted.
From the standpoint of the app, creating APIs, accessing a server where models are housed, adding a mobile SDK, or integrating the models directly to code are all straightforward ways to integrate machine learning.
However, things are a little bit more challenging when looking at them from the rear end. You need to set up an ML pipeline when you have several models and a group of data scientists working continuously to improve them.
The workflow necessary to create a machine learning model, from data extraction and preprocessing through model training and deployment, may be automated with the use of ML pipelines. Data scientists may easily scale the solution and optimize models going ahead using such end-to-end machine learning processes.
Finally, you need to update the applications to adapt new OS capabilities and fix bugs as they come up in the ML infrastructure after publishing AI-powered mobile apps to the App Store and Google Play.
There are a variety of ML operations tools you can use including Algorithmia, Metaflow, and Domino Data Lab.
In order to maintain your models’ peak performance, these technologies assist with automating model versioning, implementing pipelines, scaling models on-demand, and other machine learning application development tasks.
Hopefully this brief overview gave you an idea on how to build machine learning applications, and will help streamline your future efforts as you tackle the challenging world of AI and ML integration.
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