source-Google

Basic Life Cycle of an AI Project

About Artificial Intelligence

AI is one of the most popular buzz words in the living tech savvy world today. The main aim of this article here is of course to discuss applied AI stages and it’s implementation cycle. AI is something where a machine is trained to mimic any human take and it’s decision intelligence. So, the real gist is can AI replicate or improve the human roles? Absolutely YES! The main wings of any successful AI project is anticipation and experience.

Understanding AI project life cycle will help to locate the details that need attention. AI project is what which does not stop at implementation stage but follows a cyclic process, hence we name it the AI project cycle. The following are it’s vital stages which flows in a loop -

1. AI Project Scope.

2. Data Collection and Labelling.

3. Design and Training of Model.

4. Deployment to Production.

Now that we explored the nature of AI project cycle briefly, let’s discuss each stage with more depth.

Step 1. AI Project Scope

First fundamental step while starting AI project initiative should be defining the scope for the project and selecting relevant use case that our model is willing to address. Define the objective! Defining business objective by a person who knows the business gist and has sharpest decision making skills is crucial. One should anticipate and analyze if the problem really needs any complex AI approach or just a simple cost efficient solution is a real go for the project.

source-Google

Step 2. Data Collection and Labelling

Data! More Data! Profound Data!

So, the gist here is… Do you trust your data? I will frankly suggest to audit the data before processing it. AI system is capable of tracking patterns and making decisions thanks to the statistical models. While creating effective AI model, challenge is not simply availability of data, but large amount of varied and promising data, which is often ignored and comes with a heavy price to pay in later stages. Poor quality of data prolongs the project and leads to the rise in expenses involved in the project.

Where to dig out our data from? Wondering? No worries ! The key is here-

⦁ Customer Relationship Management, CRM.

⦁ Internet of Things, IoT.

⦁ Third party data providers/government publication platforms.

Labelling

Once our data is collected and filtered, next important step is to tag it. Data labelling refers to the process of detecting and tagging data samples. It simplifies models and helps with identification and understanding the meaning of digital data.

Note — Data from the real source is messy, shabby and incomplete. It’s completely irrelevant when used with the models. We ought to clean the garbage data in order to convert it in the high quality data after which we are good to go further.

source-Google

Step 3. Designing and Training of the Model

Selecting the right model is based on multiple aspects like type of challenge the business faces, results and accuracies we desire, dimensions of the data we got. We need to get our hands dirty with codes to see what actually works for our practical insights.

Training is where we fit our processed data into the models. This process gradually improves the performance of the models and helps us with the modest accuracies. Fine Tuning the parameters is the key!

Pro-tip — Splitting the data into train, test and validation sets is always a good idea!

Step 4. Deployment and Production

Monitor and Maintain…..forever!

Last but not the least!…In fact, it is the most important stage to bring real value to the composed model and profit any organization. Models should be constantly operationalized, optimized and deployed into production in a loop for use in any organization. Deployment stage means we have to implement it in any given environment with web interface where data can show analysis in new interface. It’s critical to assure that we have systems for monitoring models once they are in production level and have ability to quickly train and test the data and implement new models in order to shift required strategies. AI model deployment is basically a team work and a combination of several skills like data analytics, data science, software engineering and many more…

source- Google

Summing Up

1. Launching AI solution once doesn’t infer that the project is done. Monitoring, reviewing and interpretation of desired results is more important.

2. These ways will lead us to plan and prepare successful operation and implementation of any AI solution.

3. Experts say…there is lot more to machine learning than a bunch of mathematical algorithms. Well said!

Happy Coding Peeps!

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passionate Data Scientist and AI reseacher

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neha urade

neha urade

passionate Data Scientist and AI reseacher

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