Subscribe • Previous Issues Generative AI: Navigating the Challenges of Enterprise Adoption As we reach the halfway mark of 2024, it's a prime opportunity to evaluate how companies are progressing in their efforts to leverage the power of generative AI. To mark this occasion, I've combed through numerous surveys and spoken with people from several companies across various industries
When choosing a deployment path for generative AI applications, businesses should consider six dimensions: knowledge data, development investment, data security, output content control, project budget, and computing resources.
1. Knowledge Data: This refers to the industry-specific or proprietary data that a company needs to prepare for building the application.
2. Development Investment: This entails assessing the required technical research and development personnel.
3. Data Security: Generative AI applications pose a risk of information leakage, so companies need to anonymize data to reduce the identifiability of personal information.
4. Output Content Control: This involves evaluating the accuracy, consistency, and compliance of the model's outputs.
5. Project Budget: This assesses the financial investment needed for the deployment of the application.
6. Computing Resources: This refers to the evaluation of GPU resources needed for model training and inference.
The democratization of Generative AI has its downfalls. Certainly, those rushing in based upon hype without understanding quite what they are doing or what is involved will lead to increased failure rates. Always need to have the experienced Data Scientists on staff to guide the way.
When choosing a deployment path for generative AI applications, businesses should consider six dimensions: knowledge data, development investment, data security, output content control, project budget, and computing resources.
1. Knowledge Data: This refers to the industry-specific or proprietary data that a company needs to prepare for building the application.
2. Development Investment: This entails assessing the required technical research and development personnel.
3. Data Security: Generative AI applications pose a risk of information leakage, so companies need to anonymize data to reduce the identifiability of personal information.
4. Output Content Control: This involves evaluating the accuracy, consistency, and compliance of the model's outputs.
5. Project Budget: This assesses the financial investment needed for the deployment of the application.
6. Computing Resources: This refers to the evaluation of GPU resources needed for model training and inference.
The democratization of Generative AI has its downfalls. Certainly, those rushing in based upon hype without understanding quite what they are doing or what is involved will lead to increased failure rates. Always need to have the experienced Data Scientists on staff to guide the way.