AI Models

• AI Platform: An AI platform is a set of integrated technologies that allow people to develop, test, deploy, and refresh machine learning (ML) and deep learning models. There are many AI platforms available, i.e. Microsoft Azure. It provides a platform, which is used to create applications for machine translation, conversational AI, and document processing. 

• AI Model: An AI model refers to a software algorithm (set of rules and instructions) designed to simulate intelligent behavior or perform tasks that would typically require human intelligence. These models are created through a process known as machine learning, where the model learns from data to make predictions or decisions without being explicitly programmed to perform the task. 

Application Examples 

• Image and speech recognition 
• Natural language processing
• Autonomous vehicles
• Recommendation systems
• Medical diagnosis

• Conservative estimates suggest there are 'Hundreds of Thousands' of AI Models.

• Ask: "Can a smaller and simpler traditional solution effectively address our needs?

• Remember: The right AI model depends on your unique context, goals, and constraints. Take a holistic approach and choose wisely.

• Online (Cloud) vs. Onsite (Local): Reference the section to the right for advantages and disadvantages of choosing one over the other. 

• Hardware Processing Capabilities: AI Models can vary greatly in the level of computer system(s) needed to run the applications efficiently. The demand continues to increase as models are created and updated.

• Implementation:  Choosing pre-trained models over initiating customized architectures can have a great impact on the resources needed for this stage of development.

• Expertise:  Engaging AI will require different levels of expertise. Basic - used for implementing pre-trained models, or utilizing highly developed Application Programming Interfaces (API).  Intermediate - used for customizing architectures and handling data preprocessing.   Advanced - used for designing unique models and optimizing for specific hardware. 

• Maintenance:  Considerable time could be needed for regular updates and retraining to maintain accuracy. Time will be needed to address issues, optimize performance, and adapt to changing requirements. 

• User  Orientation:  Consideration should be given to the issues around Users being trained to efficiently utilize the new 'tools' available through the AI Application(s).                       

• In-House-Based Programs:  These Programs are installed directly on a user's local computer or device. They run independently of an internet connection. Data is stored locally on the user's machine.
• Cloud-Based Programs:  These Programs reside on remote servers managed by a third-party provider.  The Programs are accessed via a  web browser or dedicated app, requiring an internet connection.   Data is stored in the cloud, which enables access from any device.  

Desktop Advantages  

1. No internet connectivity problems
2. No reliance on internet speed

Desktop Disadvantages  

1. Higher costs (licensing, support 
2. Limited scalability  
3. Updates require manual intervention

Cloud-Based Advantages  

1. Data is accessible anytime, anywhere
   with an internet connection
2. Subscription-based payment model
3. Users can access information from any device

 Cloud Based Disadvantages

1. Performance depends on the internet speed  
2. Network and connectivity issues  may arise  
3. Data vulnerability to hackers and cybersecurity threats  
4. Privacy concerns