- Even the best companies have difficulties with implementing AI. Because of this, there must be great care, diligence, and planning. It’s also important to realize that failure is common.
- There are two main ways to use AI in a company: through a vendor’s software application or an in-house model. The latter is much more difficult and requires a major commitment from the organization.
- When using off-the-shelf AI applications, there is still much work to be done. For example, if the employees are not correctly inputting the data, then the results will likely be off.
- Education is critical with an AI implementation, even for experienced engineers. There are excellent online training resources to help out with this.
- Be mindful of the risks of AI implementations, such as bias, security, and privacy.
- Some of the key parts of the AI implementation process include the following: identify a problem to solve; put together a strong team; select the right tools and platforms; create the AI model; and deploy and monitor the AI model.
- When developing a model, look at how the technology relates to people. The fact is that people can be much better at certain tasks.
- Forming the team is not easy, so do not rush the process. Have a leader who has a good business or operational background, with a mix of technical skills.
- It’s good to experiment with the various AI Tools. However, before doing this, make sure you do an IT assessment.
- Some of the popular AI Tools include TensorFlow, PyTorch, Python, Keras, and the Jupyter Notebook.
- Automated machine learning or autoML tools help to deal with processes like data prep and feature selection for AI models. The focus is on those who do not have technical skills.
- Deployment of the AI model is more than just scaling. It’s also critical to have the system easy to use, so as to allow for much more adoption.
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