AI Development Life Cycle | Explained

Ever wondered how AI Solutions are built and what could be the secret recipe for building a high-performing AI solution?

If yes, this article would help you to understand the AI development lifecycle in step by step manner. Throughout the article, we might use the terms machine learning and AI interchangeably. Though machine learning is a subset of AI, they are not the same. However, for the sake of simplicity and understanding, we will give ourselves the benefit of the doubt in this case. Also, the step by step process defined in this article is not the only possible segregation of steps and it is obvious that you may find some other article where the no. of steps or their clubbing is not same as this one. However, the crux of the process remains the same. With these clarifications/ disclaimers, let's begin with the main topic.

The first step to solving any problem, AI or otherwise, is to understand what we are dealing with. That brings us to the business problem understanding.

1. Understanding Business Problem

This is almost always the first step in solving any data science or AI-related problem.

A data scientist looks for crucial words and phrases when speaking with a line-of-business specialist about a business issue. The data scientist deconstructs the issue into a procedural flow that always includes an understanding of the business problem, an understanding of the necessary data, and an understanding of the various artificial intelligence (AI) and data science tools that can address the issue. Together, this data fuels a series of iterative thinking experiments, modeling strategies, and assessments against the business objectives.

By far, the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.

—Eliezer Yudkowsky        

The business must remain the main priority. When technology is introduced too soon, it may become the focus of the solution, leaving the real business issue unresolved or incompletely addressed.

Moving on to the next phase brings us to data collection.

2. Data Collection

All of this discussion about model performance and evaluation is important but quite futile if this step of the process is overlooked.

Without quality data, no AI model can solve our business problems effectively. AI models need to be trained on quality data and for that, a disciplined data collection pipeline is a must.

The subject of data collection is endless. However, for the uninitiated, it can be understood?as the process of gathering model-specific data in order to improve the training of AI algorithms and enable them to make proactive decisions on their own

Focusing on the quality of data fueling AI systems will help unlock its full power. 

- Andrew Ng        

As a result, you can imagine, if there is biased or lazy data collection, the next steps can get compromised resulting in the ineffectiveness of the AI model.

3. Data Analysis

Now that we have adequate data with us, we can proceed on to the next step of the process.

Data can be messy if it has not been appropriately maintained, leading to errors that easily corrupt the analysis. These issues can be values set to null when they should be zero or the exact opposite, missing values, duplicate values, and many more. We need to go through the data and check it for problems to get more accurate insights.

The most common errors that we can encounter and should look out for are:

• Missing values
• Corrupted values like invalid entries
• Time zone differences

There may be other logical errors in the data that make no sense. For example, consider date range errors that make their age greater than their parent’s age. These errors ideally should be eradicated during the data collection phase but in case they still persist, they should be solved in this phase.

The analysis phase also involves employing various graphical and non-graphical methods to make sense of the data, put simply. This is also known as Exploratory Data Analysis. The objective of this phase is to enable the analyst to make sense of the data through visualizations and intuitive insights.

Once we see that our data makes sense, we move on to the next step in our AI life cycle process, we come to perhaps the most important step in the entire process.

4. Feature Engineering

A feature is any measurable input used in a predictive model. Feature engineering is a machine learning technique that leverages data to create new variables that aren’t in the training set. With the aim of simplifying data transformations while also improving model accuracy, it may generate new features for both supervised and unsupervised learning.

Feature engineering is perhaps the most important step in the AI development process because it helps in creating the desired features that will be used by the model to improve its performance and make it optimal.

There are many feature engineering techniques used such as missing value imputation, handling outliers, transformation, encoding, normalization, and standardization.

The primary objective of the feature engineering process is to transform the data into a form that is easily understood by the machine.

Next up, we come to the phases where the magic happens.

5. Algorithm Selection

Most of us are experts at one thing and not so much at other things. If we are asked to do tasks that we are not familiar with, we would usually struggle to get optimal results. It is the same case for AI algorithms. There is no one-size-fits-all when it comes to AI algorithms.

For example, a time series forecasting task that follows a linear trend cannot be effectively predicted with a classification algorithm such as a decision tree.

As a professional, it is therefore the AI engineer’s task to perform algorithm selection optimally in order to ensure optimal results.

Once we have selected the algorithm that we think will work best for our business problem, we move on to, what is perhaps the most interesting phase of the entire AI life cycle.

6. Model Training & Testing

Let us consider an analogy to understand this phase better. Think of an examination. We train ourselves to appear for the examination by learning questions and the answers along with those. We learn the patterns in the questions and acquire the knowledge to identify such patterns.

This is similar to the training phase of an algorithm where it goes through labeled data (in the case of supervised learning) to figure out patterns in the data to help its future predictions.

Now, during the examination, we are given a question paper with completely unseen questions. However, we are good. We have studied and trained ourselves to see patterns and formulate answers. We do

This is similar to the testing phase of an algorithm. It is made to make predictions on unseen data and check for the accuracy of its predictions. The percentage of correct predictions it makes is termed the accuracy of the model. The higher the accuracy, the better is our model at predicting under real-world situations.

But let us consider the situation where our examination did not go well. What do we do? We go back home and figure out where we went wrong and focus on those parts to, make little changes to our studying techniques or routines in order to avoid the mistakes we made in the previous examination.

That brings us to the optimization phase of our process.

7. Optimization

When developing a machine learning model, model optimization is a crucial component of obtaining accuracy in a real-world setting. The goal is to adjust the model configuration to increase precision and efficiency. Additionally, models can be improved to suit certain objectives, jobs, or use cases. Optimization is the process of reducing the degree of inaccuracy that machine learning models will inevitably have.

This is usually done through something called hyperparameter optimization. They are not learned or developed by the model on their own but are configurations or model settings that are usually set by the expert. It is found that often tweaking these values can result in massive performance boosts of the model.

As a result, optimization encapsulates lots of tweaking and tuning in the model design with the aim of making it more effective and accurate.

Now that we have a finely tuned and accurate model at our disposal, how do we use it? We don’t make an AI model just to test its accuracy and feel good. We have to find ways of using the machine’s predictions in our business problem, which was our main goal.

This brings us to the model deployment phase.

8. Deployment

Deploying an ML model simply refers to integrating the model into an already-existing production environment that can accept an input and produce a useful output for business decision-making.

Large companies make their resources available to us to help deploy our models and make them available to the general public. Some examples to consider are Amazon Web Services (AWS) and Microsoft Azure.

9. Continuous Monitoring

If you thought our job ends at deployment, you thought wrong. Only deploying the model is not enough, it needs continuous maintenance and monitoring in order to identify gaps and make further improvements. This is just as important as any other step in the AI life cycle, if not more.

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That brings us to the end of the article. Hope it was helpful for you to understand the process involved in AI development. We would be coming up with many more exciting article about AI, Data, and Career Opportunities in this field. If you are new to this newsletter and have not subscribed to it yet, do consider subscribing it.

See you in the next one! Until then, have a good time and stay curious!