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...

1. Identification of Business Case
2. Collection of Data
3. Preparation of Data
4. Building the Model
5. Testing the Model
6. Validate the Model
7. Deploy the Model
8. Govern the Model

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...

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...

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...

• 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
...

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...

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.

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...

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...

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...