Successful AI Development: 3 Critical Stages You Need To Know
The power of AI is undebatable. Basic AI embeds human cognitive capabilities, advanced AI is even more capable than a human. With all the hype related to this powerful technology, have you known about the three steps to successfully develop AI products?
As a leading software development company provides a range of IT services including development outsourcing of AI solutions and digital transformation, we’d like to give you some insider’s insights.
The AI development process generally involves three parts: Big Data, Machine Learning, and Deep Learning.
Big Data
When taking the AI journey, there are still many businesses don’t realize the importance of data management. Businesses bring data scientist experts to extract, consolidate, clean, and analyze data is indeed a critical step in the process. By analyzing data, useful and essential information is kept, organized, and used appropriately.
Having your data finely sorted will help you avoid null values, duplicated, and disparate data sources in the future.
We can apply Big Data into many industries, the education industry is just one of many. We can customized programs and schemes for each individual needs according to their learning history and goals. Not only the learning programs can be tailored but the course material can also be reframed. 
Machine Learning
Although machine learning is indeed a type of AI, there are some notions that separate them apart. AI is a much bigger concept of creating intelligent machines that can imitate human behavior and thinking ability while machine learning is a subset of AI that indicates a machine learning from data without being programmed.
To be precise, machine learning allows computers to make predictions and decisions according to structured and semi-structured big data. But the machine learning algorithm only works for specific domains, for instance, we are creating a machine learning model to detect pictures of cars; it will only give out the result for car images.
Machine learning is like how marketers use and compare data to predict trends and come up with strategies influence customer behaviors. We can see different marketing campaigns that attract the correct audience based on demographics, psychographics, and sociographic. Similarly, developers can train computers to identify patterns in data and conclude which action one company should take.
From cleaning to analyzing data and defining KPIs.
Virtual assistants like Siri, Alexa, and Google are the three most well-known products of machine learning. Another example of machine learning is email spam and malware filtering detected automatically.
Deep Learning
Because machine learning’s capability still relies on the data available, human actions are still needed when there is an absence of information or when machine learning makes an error. This is where deep learning jumps in. Deep learning is also a subfield of AI, and it powers the most human-like artificial intelligence. Deep learning and machine learning share some similarities; however, what makes them different is its ability to establish where their prediction is accurate or inaccurate. Deep learning has the capability to reflect on the decision it made and improve the skills based on past experiences.
Deep learning is the critical technology behind voice control in consumers’ smart devices and driverless cars. Its applications are widely used in various industries like automated driving, medical devices, aerospace, and defense, industrial automation, and electronics, etc.
This cutting edge technology brings multiple benefits, one of which is the ability to create new features and establish advanced analysis capabilities. Deep learning algorithms can create new tasks to solve the issues it’s facing now. Data scientists can save much time when working with big data.
One of the clear examples of Deep Learning is Chatbot. This tool is incredibly useful to help customers more effectively and efficiently. It recommends all necessary answers for almost every confusion.
Conclusion
With all the applications these three technologies bring, no wonder there is so much hype. The importance of these three technologies lies in how one business uses it. While advanced technology like these is there to help businesses taking the leap, businesses should follow a clear path in order to successfully implement AI development process.
At TP&P Technology, we offer AI solutions development with our team of skilled Machine Learning engineers in Vietnam. Contact our expert now to get an idea of how we can be your trusted outsourcing partner.
