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4. Building the Model
- Define the features of the model.
- Use the same features for both Training and Testing the model
- Collaborate closely with Subject Matter Experts
- Be wary of "Curse of Dimensionality" - do not use unnecessary and irrelevant features that reduces the model accuracy
- Choose suitable Algorithms
- Consider Model interpretability - Predictions and Decisions should be clearly explainable
For Improving Results:
- Tune the hyperparameters or algorithm parameters like no. of trees of Random Forest or no. of layers in a Neural Network
- Use pre-trained models, reuse them to build new models
- Version each iteration
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Steps Involved in AI Model Development
- Identification of Business Case
- Collection of Data
- Preparation of Data
- Building the Model
- Testing the Model
- Validate the Model
- Deploy the Model
- Govern the Model
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3. Preparation of Data
It is critical, though time consuming to clean the available data and transforming it into required formats.
Involves segmentation of data sets into training, testing and validation.
- Process the available data
- Clean the data s...
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6. Validate the Model
After testing the model with different datasets, validate the model performance using the business parameters defined in step 1.
Analyze if KPI's and Business Objectives of the model are achieved. If not, consider changing the model, or improving the quality and quantity of the data.
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7. Deploy the Model
After successful validation against all defined parameters, deploy the model onto planned infrastructure - cloud, edge, or on-premises environment.
Before deployment, consider the following
- plan to continuously measure and monitor the model performance
- define a baseline t...
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A Guide to Artificial Intelligence Model Development
Machine Learning (ML) is an integral part of Data Science (DS), that helps computers learn from data.
This process of learning from data through machine learning techniques contributes to Artificial Intelligence (AI).
Here is a quick...
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5. Testing the Model
Primary objective of Model Testing is to improve results and minimize changes in model behavior post-deployment in real world.
Carry out multiple experiments using Training, Validation and Testing Datasets.
If model performs poorly on Training data i...
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Further Considerations
AI models need time to be developed.
A smooth and successful model development involves a combined effort from data engineers, data scientists, ML engineers and DevOps engineers.
Proper resource allocation, project planning and management is crucial to meet the business goals and obje...
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1. Identification of Business Case
Ask the right questions.
- What results are you expecting from the process?
- What processes are being used ?
- What are the KPI's that can help track success?
- What resources are required?
- How do you break down the problem?
Based on your answers, y...
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2. Collection of Data
ML Models are only as accurate as the data fed to them.
Important to identify the right set & format of data to ensure accuracy & relevance of the model.
Ask relevant questions:
- Data required to solve the problem. Eg. Customer data, I...
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8. Govern the Model
When a model is deployed in real-world, the data fed to it is dynamic.
There can also be changes in technology, business goals or drastic real world changes like the pandemic.
It is crucial to analyze how these changes affect the model, so you can reiterate.
Consider monitoring...
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The terminologies might change, the technologies might evolve, but this is the future. And that Future is already here!
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Model building and training
At this step, all the requirements have been collected for the solution modelling to proceed.
ML engineers will define the features of the model, taking the following into account:
- Use the same features for training and testing the model to avoid inaccurate results.
The ML workflow
To develop and manage a production-ready model, you must work through the following stages:
- Source and prepare your data.
- Develop your model.
Train an ML model on your data:
Train model
- Evaluate model accuracy
Tune hyper...
Transfer learning and fine tuning
Transfer learning consists of taking features learned on one problem, and leveraging them on a new, similar problem. For instance, features from a model that has learned to identify racoons may be useful to kick-start a model meant to identify tanukis.
- Take layers from...
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