Artificial Intelligence and Machine Learning refer to the science of making computers do things that require intelligence when done by humans. AI can be used for many purposes, including decision-making and problem solving; it’s also sometimes called heuristic programming or machine intelligence to reflect its focus on manageable tasks with physical limitations like perception, prediction & understanding human communication.
When it comes to AI, there are a lot of considerations that need to be made when selecting the right platform. The five key considerations we will focus on in this blog post are:
- Identify and Solve a Real Business Problems
- Build a Data Strategy
- Create Customer experience (CX) environments
- Develop in a cloud environment for greater Agility and Easy to Scale and Deploy production model
- Monitor results and fine tune the models as needed
Identify and Solve a Real Business Problems
When it comes to selecting an AI ML platform, the first thing you need to ask yourself is what real business problems do you want to solve? Not every company needs or wants an AI platform. Some companies may only need basic machine learning algorithms for data analysis. However, if you are looking to use AI for tasks such as image recognition, natural language processing (NLP), predictive analytics, or recommendations then you will definitely need a good AI service platform. A good AI ML platform should be able to identify and solve these types of real business problems.
Build a Data Strategy
In order for your AI ML platform to be successful, you also need to have a good data strategy. The AI platform will need access to high quality data in order to learn and improve its algorithms along with a plan for managing your data including Cleaning, Storing, Securing, Preparing and Monitoring. You need to consider how data will be shared among the teams in the development pipeline, such as through a common platform or a hybrid cloud approach. also need to make sure that the data is properly labelled and organized so that the AI can easily find and analyze it.
Create Customer experience (CX) environments
In order for your AI ML platform to be truly effective, you also need to create customer experience (CX) environments. These are simulated environments where customers can interact with your products or services using natural language interactions. This will allow the AI platform to learn how customers interact with your products and services, which will help it generate better recommendations and predictions.
Develop in a cloud environment for greater Agility and Easy to Scale and Deploy production model
When it comes to developing an AI ML platform, you also need to consider the cloud. Developing in the cloud has a lot of advantages such as increased agility, easy scalability, and the ability to deploy your product quickly. By developing in the cloud, you can save time and money while still achieving maximum performance.
Monitor results and fine tune the models as needed
Once your AI ML platform is up and running, you will need to continuously monitor its results. You may need to fine tune the models or add new data sets in order to improve its accuracy. This is an important part of using any AI service platform and should not be overlooked.
Hopefully this gives you a good idea of what to look for when selecting an AI ML platform. Keep these five considerations in mind and you should be able to find the right platform for your needs. Thanks for reading!
Looking for an AI ML platform? Look no further than DT4o. We are experts at helping companies identify and solve real business problems with our award winning AI service platform.