Ways To Go About Machine Learning App Development
We’re enclosing the Features, Benefits, Steps and Technology Stack of an ML-based MVP in brief!
Artificial Intelligence zeroes down to machine learning. It is one way to hunt patterns and anomalies in human behaviour, understand customer behaviour, and check their past purchase history, preferences, and satisfaction levels.
Popular examples include: How Facebook automatically tags you on your friend’s photo? Or why does Spotify replenish its “Discover Weekly’s”?
AI has become so general that we don’t realize that we are making use of it all the time. Google search is able to give accurate search results with long tail keywords; Alexa; Siri and Facebook feed gives content based on human interest.
Machine Learning vs. Deep Learning vs. Artificial Intelligence
But Machine learning, deep learning, artificial intelligence, and data science are all different, yet interconnected. Machine learning and deep learning aids artificial intelligence by providing a set of algorithms and neural networks to solve data-driven problems.
Artificial Intelligence (AI) is the science of getting machines to mimic the behaviour of humans.
Machine Learning (ML) is a subset of AI that focuses on getting machines to make decisions by feeding them data.
Deep Learning (DL) is a subset of machine learning that uses the concept of neural networks to solve complex problems.
AI covers a vast domain including natural language processing, object detection, expert system, robotics, and computer system. They can be structured along three evolutionary stages – Artificial Narrow Intelligence, Artificial General Intelligence, and Artificial Super Intelligence.
Artificial Narrow Intelligence (ANI) also known as weak AI involves applying AI only to specific tasks. Most of the systems that claim to use AI are based on weak AI. For example – Alexa operates within a set of predefined functions. There is no genuine intelligence or self-awareness. Apple iPhone faces verification, and Autopilot features at Tesla, the social humanoid Sophia (built at Hanson Robotics), and finding the optimal path through Google Maps.
Artificial General Intelligence also known as strong AI, involves machines that possess the ability to perform any intellectual task that a human being can. Machines don’t possess human-like abilities. They are not yet capable of thinking and reasoning like a human. Stephan Hawkings warned that “Strong AI would take off on its own, and re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete and would be superseded.”
There has been a rapid expansion of AI/ML in medical applications, finance applications, logistic/supply chain applications, customer-facing applications, metaverse, gaming applications, data science assets and investments, IBM Watson, Twitter, Google Assistant, and Tesla Cars. Machine Learning Systems scrutinize data, learn from that data, and make decisions. Examples: NFT Streaming, Snapchat’s Filters, Google Maps, Uber and Lyft, Financial Applications, Dango, Healthcare, robotics, marketing, and business analytics.
How to build a machine learning app?
The simplest way to harness the potential of machine learning in mobile applications is by making use of ready-made ML services from Apple like Core ML, and ready-made ML services from Google like ML Kit or Firebase ML. You have the option to keep all the functionality in the cloud, or to keep all the functionality in the mobile app.
To start with, design the prototype and verify it. Code and test your MVP. Deploy and maintain the app. Let’s look into it in detail:
Define the problem: What do you wish to achieve with your ML-based app? How will it benefit the customers? Can it solve any task without using ML? If you are creating an AI-based chatbot app with well-defined options where all options are already coded, it won’t touch the AI aspect. But it won’t fulfill much of your business needs either, with all the regular algorithms. It will be incapable of dealing with any unknown challenges.
While sketching the outline of your AI project, consider any challenges that come through. What will happen when ML algorithms no longer work to rectify a specific situation? Do you have enough data to train a sustainable model? Will your ML models evolve over time?
Align the right professionals who are skilled and experienced in business, development, and testing.
Secure powerful servers with ML infrastructure, data analysis, cloud hosting with APIs, on-device SDKs or custom libraries, or a hybrid approach.
You need to discern if your target audience uses old devices, slower phones or networks. It needs to train the data non-stop in real-time. ML developers need to check if the ML model is too large to fit on a mobile device. ML models may require more than 100MB of disk space, making downloads less likely to happen. Your ML AI model should combine with other data pulled from third-party APIs.
If you are building an AI App like Netflix, you would want to fetch information from IMDB about your reviews and ratings, to serve more movie recommendations, solely based on your activity in the Netflix app.
Technology Stack for Machine Learning App
When you create a machine learning app from scratch, you may use a custom solution to align off-the-shelf machine learning components. This approach can be more flexible. But using canned ML services can remove a lot of burdens associated with ML functionality. Decipher the frameworks, technologies, tools, and development environment at hand to create ML apps. Python is the most frequently used programming language that ML development companies use in AI App Development. Use NLTK, Scikit – learn AI/ML Library. Use TensorFlow, PyTorch, Caffe, Scikit, Keras, Pandas, Numpy, and MXNet – ML Frameworks. In detail:
- Programming Languages: Python, C++, Golang, R, SQL, Java/Scala
- Frameworks: TensorFlow, PyTorch, MXNet, Caffe2, Keras, SciKit
- Data Warehousing: Hadoop, Spark, Ray, BigQuery, Redshift, Snowflake
- Libraries: Pandas, NumPy, DeskML, Keras, NLTK, SciPy
- Cloud Services: AWS SageMaker, Google Cloud AI Platform, Azure ML Studio, IBM Watson, Azure DataBricks, AWS Glue
- DevOps: CometML, Kuberflow, MLFlow, HopsWorks, Docker
- Tools: Jupiter Notebooks, Google Colab, Airflow, Google Data Lab, Hevo, SQL Alchemy
Besides the steps that we followed here, a slight diversion will allow you to host the code of your project as a Jupiter Notebook. You can also build a web app for your project. You can make your code publically available by making use of Github, or Jupyter Notebook Viewer. The project will have to be downloaded and run on the notebook on the local machine or run notebook online in Jupyter Notebook Viewer. You can also make use of HTML or JavaScript and Flask to build a web app around the data science project and then run the project through the app. To avoid the intricate details of HTML, CSS, and Javascript, enter Stremlit. You may use Tweepy as a Python wrapper, Vader sentiment as a rule-based sentiment analysis library, and deploy on Heroku.
Conclusive
Access the URL and check the app. It becomes a lot easier to deploy the ML App with Streamlit and Heroku at absolutely no cost. Streamlit is also deployable on AWS, Google Cloud or any other cloud app. That was a relatively easier implementation of ML Apps in AI. Acquiring AI Skills like AIOps can simplify the process further. Follow up with ITFirms and application development companies for the best AI Blogs, most in-demand AI skills, how AI and ML are emerging etc.
Related Topic: Selecting Top AI Developers: A Comprehensive Guide