Machine learning projects can be complex and challenging, especially if you don't have a clear scope and plan. A well-defined scope can help you set realistic goals, avoid scope creep, allocate resources, and measure progress. In this article, you will learn what steps you can take to determine the scope of a machine learning project.
The first step is to understand the problem that you want to solve with machine learning. You should ask yourself questions such as: What is the business or research objective? What are the expected outcomes and benefits? Who are the stakeholders and users? What are the constraints and risks? How will you evaluate the performance and success of the solution? By answering these questions, you can define the problem statement and the scope boundaries.
Adding on this. One of the key questions before all these questions is: “Do you need Machine Learning to solve the problem in hand?” A lot of times, problems can be solved using statistical functions, or probabilistic approaches. The idea is, to not come with the most fanciest approach to solve a problem, the idea is to come up with a solution to the the problem.
The next step is to explore the data that you have or need to collect for the machine learning project. You should perform some data analysis and visualization to understand the characteristics, quality, and distribution of the data. You should also identify the features and labels that are relevant for the machine learning task. You may need to do some data cleaning, preprocessing, and transformation to make the data suitable for machine learning. By exploring the data, you can determine the data scope and the feasibility of the project.
Also, an important step while doing Exploratory Data Analysis (EDA) is to use your domain understanding to determine the most relevant features and generating an appropriate hypothesis to solve the problem. Remember, Data Science is all about the “data”, not about “modelling” ??
The third step is to choose the machine learning method that best suits the problem and the data. You should consider the type of machine learning task (such as classification, regression, clustering, etc.), the complexity and size of the data, the availability of labeled data, the computational resources, and the interpretability of the results. You may need to do some literature review, benchmarking, or experimentation to compare different methods and select the most appropriate one. By choosing the method, you can define the technical scope and the requirements of the project.
From experience, let me tell you one thing in Data Science. Majority of ML problems can be solved using linear/logistic regression, decision trees and XGBoost. Always do resort to these approaches, they may become goldmine for you.
The fourth step is to plan the process of developing, testing, and deploying the machine learning solution. You should follow a structured and iterative approach, such as CRISP-DM or OSEMN, that covers all the stages of a machine learning project, from data understanding to model evaluation to business deployment. You should also define the roles and responsibilities of the team members, the tools and platforms that you will use, the deliverables and milestones that you will produce, and the quality standards and best practices that you will follow. By planning the process, you can establish the operational scope and the timeline of the project.
A small addition, Data Science is not all about “modelling”, its also about giving the end-user an actionable interface to interact with the model. Hence, comes Software Engineering. Trust me when I say this, you can’t be the best Data Scientist in the room, if you aren’t even mediocre in Software Development ??
The final step is to communicate the scope of the machine learning project to all the stakeholders and users. You should document the scope in a clear and concise way, using diagrams, tables, or charts if necessary. You should also explain the assumptions, dependencies, and limitations that affect the scope. You should seek feedback and approval from the stakeholders and users, and make sure that they understand and agree on the scope. By communicating the scope, you can ensure the alignment and expectations of the project.