Are you AI ready?

AI is the buzz word. Everyone wants to make use of AI for better analysis of the business and to gain more value.

With the people in the loop, it is a general understanding that if there is enough data, organizations can implement AI. 

Everyone expects the path to be

Data > Information > AI > Value

But the AI programs are extremely difficult to implement. The bigger the spread of the data, the more difficult the journey is. Most of the programs get stuck at the first hurdle – creation of a data lake. Even if the scope is limited, the merging of the data sources or making them accessible is a challenge to manage.

Once that happens, the typical steps for an AI program are

Data:

• Sourcing the data
• Selection of the data
• Synthesis of the data

Applying Data Science:

Data Engineering:

• Exploration:  An approach to analyzing data sets to summarize their main characteristics, often with visual methods.

• Cleaning: Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.

• Normalizing: Data Normalization involves tasks that make those data more convenient to customers. It includes processes like clean the data, removing duplicates, and conforming data to a specific data model.

• Feature Engineering: engineering refers to manipulation — addition, deletion, combination, mutation — of your data set to improve machine learning model training, leading to better performance and greater accuracy.

• Scaling:  scaling refers to changing the number of machines or the size of the machine depending on the size of the data to be processed. Increasing the number of machines or the size of the machine is called scaling up , and decreasing them is called scaling down.

That is followed by the following steps.

Modelling:

• Model Selection
• Training
• Evaluation
• Tuning

Extracting Value:

•  Registration
•  Deployment
•  Monitoring
•  Retraining

While all the of the above is happening, let’s not forget the following constraints:

• Legal issues
• Ethical issues
• Historical bias
• Security
• Data authenticity

There are good references available to go into the details of all the above steps.

Before an AI program starts, the organizations need to think through all the above to avoid pitfalls.