AI development plays more and more important role in various industries and life, so there are many businesses that want to develop it but they do not know where to start? In this article, we will explain everything of AI development and what businesses need to prepare, how to get successful with AI development.
1. What is AI development?
What is AI? – Source: colocationamerica
Artificial Intelligence development (AI) is an expansive part of informational technology related to developing smart machines that are able to conduct missions like human intelligence. From that, developers use this as a platform to evolve smart softwares, systems.
Purposes of AI development is:
- Creating the professional systems – that are computer applications developed to solve complicated issues in a specific field, at the level of human’s intelligence and expertise.
- Mastering human intelligence in machines – Creating systems that can understand, think, learn, and behave as a real human.
2. AI development process (workflow)
AI process – Source: Devteam.space
2.1. Design phase
The design phase purposes for AI solutions, where developers and project managers should control on sides:
- Process
- Informational sources
- Target actions and more
They can conduct AI development when they meet the following demand:
- ML
- NLP
- Professional systems
- Automation
- Imagination
- Language
And then they also consider the AI development in this period including:
- Microsoft Azure
- Google Cloud
- IBM Waston
- BigML
- Infosys Nia
This is the basic things that businesses should do in design phase for AI development.
2.2. PoC phase – Proof of concept
In this phase, businesses should form a type of mock-up for AI development, and they will take into consideration whether or not their concept is possible with technique.
5 steps to conduct PoC AI development:
2.2.1. Identiy opportunities
Businesses must set the main goals of what they want to achieve for AI development, why AI development plays the key role in their business, and what results they expect for. If a business cannot determine its opportunities with AI development, so they should consider:
- What other businesses are doing with AI
- Understand what AI solves in your field, and the value of AI development brings your business.
- Businesses should work with the specialist in AI development with businesses’ experience, skills.
2.2.2. Issue and data description
Once businesses have identified and tested opportunities, the next step is to understand and summarize problems in detail, then classify them into categories including arguments, perceptions or computer sense.
2.2.3. Building and deploying solutions
In this step, businesses need to have:
- Basic items and facilities systems
- AI development software
- Allow AI to support the intended solution
- Display and front-end software/hardware
And this step also includes:
- Model building:
Modeling is a major job involving AI development. Data science experts must use training data and parameter management to conduct experiments. In this way, new businesses can check the model’s original accuracy before training and making further adjustments.
- Training and adjustment: This is the AI development part that requires the most in-depth computation. Here, data science experts decide in which parameter their model is most effective with available training data.
2.2.4. Appraisal of enterprise value
The factors that businesses should consider:
- Engineers
- These elements can be designed
- Measurable
- Continuous testing
And other factors also can evaluate:
- Accuracy: Is the solution giving results and analysis right? Do they repeat?
- Level of completion: Is the solution properly leveraging all data sources?
- Time: Are analyses delivered at the right time?
- Techniques: Is it easy to fix the wrong output bug from a training model?
- Compatibility: Does the solution integrate with third-party data sources and services?
2.2.5. Expand Scale of AI development
At this step, business can do somethings to ensure the success of PoC:
- Extend the ability to reason
- Expand facilities
- Adjust and optimize the PoC solution
- Broaden businesses’ perspective
- Management and operation planning.
2.3. Application phase
The application phase must be conducted and verified in the PoC with the completed mockup. And then the AI system must have the completed output with speed and it can be applied in reality.
The workflow in the application phase includes:
- Requirements definition
- Learning machine completion
- Design
- Development
- Test
In AI development, the most difficult thing is how to design or develop a system that is able to maintain a certain level of processing speed despite collecting an amount of data at the design phase.
Not only the AI system, but the reason why the system development fails is said to be “No detailed description of specification. In addition, at the design phase and development phase, building a system that can maintain a certain processing speed while collecting large amounts of data is a difficult point.
2.4. Running phase
The running phase is the next step of AI development that takes to the production line. There are main points in this phase that businesses should consider:
- Maintain and inspect for stability of system
- KPI monitoring based on goal at the beginning
- Revising AI learning machine model
3. What need to pay attention to succeed in AI software development.
How to get successful? – Source: Knowllence
3.1. Focus on technique instead of data science
There are a little data technicians who have experience in conducting a good system, and this can lead to problems.
So data scientists can connect with IT technician who can collect data science faster. In this situation, businesses can try to work with a technician with more 5 experienced years.
3.2. Reduce risks
This step is very important because it decides whether or not your AI development project fails. There are some points that should take into account:
- Complete paradigm: the ideas must complete from 1 day to 2 weeks
- Trial testing for system: check the model and data from 2 to 4 weeks
- Official testing for system: complete the model and check it out by online from 2-4 weeks
- Go live: automatically update data, practice model, and development from 2-4 weeks
- Constant updates: 1 year
3.3. Don’t take algorithm as priority
Which means that businesses should have the technique for choosing algorithm including:
- Get more and more relevant data
- Process data in better ways in advance
- Make right decision of algorithm and revise it
For algorithms, businesses should choose the suitable one that works, and upgrade it endlessly. But the output can be different from businesses’ expectations.
This is all of the basic things of AI development when a business wants to develop, and the important thing is that they should have a right partner to go along with.
For BAP, we have experience in developing from AI, Big Data, Blockchain,.. for various businesses so we can have your business a giving hand from the beginning to the output.
