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More and more engineers are applying Artificial Intelligence Models into their daily workflow to perform repetitive tasks, optimize design, solve complex mathematical equations, automate processes and systems or help make better informed decisions.
When you decide to use AI in your engineering practise, in most fields of engineering, you have two options: one is to purchase a software platform with an integrated AI model such as many CAD software applications or to build an AI model from scratch using an AI Framework and integrate it into your workflow.
However, all AI Models must be trained to perform the specific tasks you require in your engineering practise. In addition there are different types of AI frameworks, models, machine and deep learning algorithms and different techniques for training an AI Model.
AI Frameworks contain the tools and algorithms necessary to build an AI Model.
Machine and Deep Learning Algorithms are processes for performing or solving a class of tasks and problems.
In software platforms with integrated AI, the AI model algorithms will begin to learn your engineering parameters from your daily workflow eventually performing simple or repetitive tasks, graduating to more complex tasks such as optimizing your designs and solving problems. However, depending on the complexity of your engineering parameters and the type of machine and deep learning algorithms in your model, training an AI model from workflow can be a slow process requiring an enormous amount of your workflow data.
If you wish to speed up the training process or if you want your AI model to perform tasks outside your normal workflow you need to further train your AI Model.
Building an AI model from scratch and integrating it into your daily workflow will provide you with a model better customized to the tasks required. There are AI Frameworks available that have tools and data libraries that can assist you in building an AI Model. Still, building a customized AI model is a complex process often requiring the expertise of an AI Engineer.
But even if you choose to employ an AI Engineer, your involvement as the end user, in building and training your AI model is essential. It is you that must determine the tasks you wish your AI Model to perform thereby determining the type and architecture of your model, the data relevant to training your model and in the end validating and updating your model to ensure its continued accuracy and efficiency.
Training an AI Model requires an enormous amount of data. Data management can be laborious, as it involves everything from collection, cleaning, integration, organization, labeling, and cataloging. But AI-driven solutions are increasingly being applied to deal with some of these labor-intensive tasks. In fact, AI has been shown to increase the quality, security, and accessibility of data. For instance, AI can classify data from different sources, including documents, designs, process plans, and more. Furthermore, it can catalog this data, helping users to locate it much faster.
1. Define Your Objective: Identify the specific design objectives and problems you want to solve using AI.
2. Model Selection: Choose an appropriate AI Model based on the nature and complexity of the data and tasks required. If you are purchasing an “off the shelf” software with an integrated AI model be sure the type of model can perform as required.
If you are building a model from scratch select an AI Framework with the training tools and algorithms necessary to build a model that meets your specifications. Also compatibility with your existing workflow software application is always a consideration.
3. Collect data: Whether training an AI model from workflow or from scratch, AI models rely on data-driven approaches, where large datasets are used to train machine and deep learning algorithms. These algorithms learn the underlying patterns in the data allowing the model to perform tasks and make decisions.
4. Preprocess data: Clean and preprocess the data to make it suitable for training your AI model. This may include removing extraneous information, normalizing the data, and splitting it into training, validation, and testing sets.
5. Train the AI model: Use the preprocessed data to train your AI model using the tools available in your integrated software or AI Framework.
6. Test and validate: Test the AI model to ensure that it works as expected. Validate the results against a set of benchmark data to ensure that the AI model is accurate and reliable.
7. Iterate and improve: As with any AI system, it is important to continuously monitor and improve the system over time. Use feedback from users and other stakeholders to identify areas for improvement and iterate on the design.
All eMETA AI courses have a section on how to train an AI Model including: techniques for training AI Models, types of algorithms, managing datasets plus source lists of specialized integrated software platforms, AI Frameworks, Models, Algorithms and Datasets specific to the topic of the course i.e. Fluid Dynamics.
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