Learn more about artificialintelligence with this collection
Find out the challenges it poses
Learn about the potential impact on society
Understanding the concept of Metaverse
Identifying and collecting data
Ask questions, such as.
- What data is needed to solve the business problem?
- What quantity of data is required?
- Do you have enough data to build a model?
- Do you need more data to extend the existing data?
- How is the data obtained, and where is it stored?
- Can you use pre-trained data?
Consider if your model will operate in real-time to determine if you need to create data pipelines to feed the model.
Consider what form of data is required:
- Structured data in the form of rows and columns.
- Unstructured data, such as images.
- Static data, such as previous sales data.
- Streaming data.
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Model deployment
Analyse if the KPIs and the business objective of the model are achieved. If the parameters are not met, consider changing the model or improving the quality and quantity of the data.
Before deployment:
- Ensure to measure and monitor the model performance continuously...
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Identify the business problem
Define the business problem you are trying to solve.
- What results are you expecting from the process?
- What processes are used to solve this problem?
- Do you see AI improving the current process?
- What are th...
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Preparing the data
This step is the most time-consuming, with ML engineers spending around 80% of the AI model development time in this stage. A significant amount of time is spent cleaning the data and transforming it into the required format.
Things to consider include:
- Trans...
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The AI Model development lifecycle
AI model development involves multiple stages that interconnect to each other.
- Identify the business problem. Instead of asking how to improve your artificial intelligence, ask how to improve your business.
- Identify and co...
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AI adoption
In 2019, near 87% of data science projects did not get into production. However, due to COVID -19, most companies have scaled up their AI adoption and increased their AI investment.
In 2020, almost 50 % of enterprises employed an ML model. But to completely harness the power of AI, multiple...
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Model testing
While the model is trained and tuned using the training and validation data set, the model will behave differently when used in the real world, which is fine.
The main objective is to minimise the change in model behaviour when it is deployed. Three data sets are used when ...
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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.
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Decision Matrix Analysis
A decision matrix is a table that helps you to visualize the best option between your different alternatives.
It works by getting you to list your options as rows on a table, and the factors you need consider as columns. You then score each option/factor combination, weight this scor...
Research planning: Choose your data and methods
At some point, you'll need to consider which data source and analytical strategy are most likely to give the answers you need.
- Consider whether your question would be best addressed by qualitative data (interviews, observations, or historical re...
Big data Hadoop
- Ability to store and process huge amounts of any kind of data, quickly. With data volumes and varieties constantly increasing, especially from social media and the Internet of Things (IoT) , th...
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