AI and Data Science Are Growing Fastest “Every Successful AI Team Follows A Workflow, Here’s Yours…”, in the coming weeks and days you might start learning your favorite programming language or training ML algorithms or use the favorite framework. Many of the people starting AI or ML may miss understanding the entire workflow that every Data Scientist, Machine Learning Engineer, AI engineer, or Data analyst in the industry follows. Believe me, this is one of the most critical parts of building an AI project successfully.
What actually happens, is when you work for some organisation or client.
Most of the time, the problem statements are not properly given. What I have seen is usually people given data and asked to find “insights”. They expect them to make sense of it and bring the value in a project. An AI project doesn’t run by picking up a machine learning model and starting training.
It follows a defined lifecycle that often begins with understanding the problem to deploying a model, and finally its maintenance.
In Part 4 of my AI Course, I am going to cover the entire process which includes an AI project lifecycle followed by the data professionals. If you aspire to build your career in the field of Artificial Intelligence then you must know the lifecycle of an AI project. Why understanding the AI project workflow is so important in Artificial Intelligence?
Most students think that the only thing in AI is the ML algorithms or model building. Even in numerous online training programs, most time is devoted to the training of models and very little importance is given to how the projects are executed in the real industry. When you get an internship or job in a company and meet the client, mostly you don’t get a “well-defined problem statement”.
On the contrary, the client just shares a dataset and asks a single question: “What can you find out from this data?”
To answer this, we follow a process that ranges from doing some preliminary research and analysis to conducting experiments and applying iterative improvement on a constant basis. By understanding this workflow, you will be able to: Build professional projects Build problem-solving ability Work with clients with great efficacy Save project success rates Be career-ready To put it simply, you will be ready to work in industry.
Let’s understand each step of the lifecycle of an AI project in detail.
Step 1: Problem Understanding In every AI project, the most significant task is the understanding of the problem statement.
Often, beginners assume that the clients have well-defined problems that require nothing more than simple implementation of technology.
On the contrary, in the real world, clients provide the datasets and the technical team should be able to derive opportunities from those. Say for example, a retail business has shared its historical data and has asked you to create a forecasting model. Here, a retail company might have provided sales data, a hospital might have shared patients record data, a real estate firm might have provided the data of housing prices, etc.
As a data professional, your job is to explore these datasets and understand the hidden problem, need and requirements of the client. Before even touching the dataset, the data professionals start working on: Conducting domain research Understanding business goals and objectives studying market research, research papers, etc Studying existing solutions/competitor analysis etc. The more you know about the domain, the more valuable you are.
Step 2: Finding The Data Source Once you know and understand the problem, the next logical step should be data collection.
However, before collecting data, it is important to locate the appropriate source. The information or the dataset could be scattered across many different databases within the organization, spread out across different formats like excel sheets, cloud storage applications, databases, website APIs and CRM platforms. One of the most significant challenges for data scientists, AI engineers, and ML engineers is finding all the different data sources within an organization that may hold valuable information. This is primarily due to the lack of documentation, internal fragmentation, and clients having tight schedules.
Clients or business executives seldom want to explain a process that is often highly informal and ad-hoc in nature.
It’s here that good asking and communication skills help you figure out. Identifying the right source of data is paramount for a project and can even make or break the entire effort.
Step 3: Data Collection Once the relevant data source or sources have been identified, it's time to actually collect the data.
For the large enterprises or bigger organizations, this task is typically done by Data Engineers. The role of a Data Engineer usually involves getting the data from multiple systems, connecting different databases, retrieving data from the cloud, gathering data from mobile applications, pulling the data from CRM systems etc. All of this collected data is then stored together in a single location so that Data Scientists, Analysts etc. Can easily work on it.
Without the correct and complete data collection process, no AI model can generate accurate results, even the most complex ones.
Step 4: Data Cleaning- The Single Most Crucial PartAsk experienced data scientists where 80% of the time in an AI project goes, and they will tell you the exact same thing: Data Cleaning. An astonishing 70% of the time of industry professionals is spent cleaning and preparing the data.
Why does this phenomenon occur?
The world doesn’t store data in a way that’s easy to work with.
Here are the most common types of issues with raw data that must be dealt with:Missing valuesInconsistent dataDuplicate recordsInvalid dataOutliersIncorrect labelsWhen you receive a massive dataset with millions of records and hundreds of columns, it’s rarely clean or usable immediately. Not all the columns will be useful. Some fields may contain junk, or simply no valuable information to the project. So professionals have to spend time and effort removing the unneeded columns, cleaning up incorrect formatting, treating the missing values, standardizing fields, validating records, and so forth.
Machine learning models won’t perform optimally with messy data so this remains one of the most critical part of the data science pipeline.
Step 5: Exploratory Data Analysis (EDA)Once you’ve finished tidying the data, the next thing is to understand it. This phase is called Exploratory Data Analysis (EDA). In this process, data scientists analyze the dataset using statistical and visual tools such as charts and plots to identify patterns.
For instance, through EDA, one may identify:Monthly sales trendsConsumer buying patternsSeasonal impact on product demand User activity patterns Visual exploration helps unveil patterns that might not be readily obvious from simple tables of raw data. EDA may highlight opportunity and guide future modeling strategies.
Step 6: Feature Engineering Feature Engineering is the process where you transform raw, unorganized data into features, which are variables that will feed into your machine learning model.
For example, for predicting the price of a house, you might have the following features: location, number of rooms, size of the house, age of the building, nearby amenities and the target variable will be the price of the house.
In feature engineering, data scientists endeavor to identify features that are most influential in prediction and may: discard irrelevant features, create new ones by combining existing variables, convert categoric features into numerical representations and scale numerical values. Better features mean better models.
Step 7: Choosing the Right Machine Learning Model many students believe selecting the right ML algorithm is the main challenge in the AI project lifecycle. However, once the previous steps are done thoroughly, selecting an appropriate algorithm comes as a natural consequence.
Professional data scientists may opt for one or more of the following types of ML algorithms depending on the problem:Regression Models – used to predict continuous numerical variables such as House Prices or revenue forecast Classification Models – used to predict categorical variable like Spam email or disease diagnosis Clustering Models – used to group similar data points without predefined labels.
Step 8: Model Comparison Many professional data scientists would never use the first model they ever train.
They often test several algorithms and then select the one that perform best based on performance metric such as accuracy, precision, recall, F1 score or RMSE. Step 9: Model Deployment Once the model has been finalized and verified to be working optimally, it is deployed in the production environment so that it is accessible to end-users, websites, apps and businesses. Step 10: Documenting the Model What the majority of the students seem to skip, and something crucial for any professional: documentation.
The model needs to be properly documented explaining all the details such as data used, steps taken to clean it, feature engineering and selection processes used, algorithms tested, performance evaluation, etc. This ensures that the team or an individual from outside the project can pick up on the ongoing work without any difficulty. Step 11: Maintenance and Monitoring The last step in the lifecycle of any AI project is to monitor the model after its deployment and maintain it.
The business environments, user behaviours, and the underlying data can change with time, thereby affecting the accuracy of the model.
Therefore, ongoing monitoring of the model’s performance and continuous improvements to the existing dataset and features are necessary. Final thoughts An AI project extends much beyond just developing and training machine learning models. While focusing on coding, it’s imperative to not overlook other phases of the workflow, which are equally, if not more important for any real-world project. Following such a process ensures a clean, efficient, and scalable outcome.
By following this process, you’ll be far more equipped to succeed on your journey to becoming a great data scientist.
Welcome to part 4 of the AI course where we shift from abstract concepts to real-life problems.
