From Data to Decisions: A Project Management Framework for Machine Learning Features Implementation - Part 1
In today's data-driven world, machine learning (ML) features are revolutionizing how products deliver value, how businesses operate and enabling them to make more informed decisions.But taking an ML feature from promising idea to impactful product feature requires a blend of traditional project management practices with specific considerations for the unique aspects of machine learning (ML) development.
Managing an ML program is different from building an ML model. This blog dives into a practical framework to guide through the project management journey of launching machine learning features.
Evaluating Machine Learning Feasibility:
Failure rate of ML projects is very high. So it is imperative to choose the right problem to solve using machine learning. Project managers should be involved from the initial phase itself and an effective project manager can make a bigger impact in this phase by bringing together all stakeholders and communicating the ML project impact, risks and high level plan.
A few parameters to be considered while considering the feasibility of machine learning features are: -
Is there a business problem or customer pain point which ML model tries to solve? Does someone care about this problem?
Document the process, results and next steps based on the tools (eg: interview, field observations, through the existing user interface) used.
Is it less effort to use a simpler rule based system to get almost the same business outcome?
Do we require multiple similar decisions to get business outcome
Do we have enough data and access to it?
Organize the Machine learning project
Solid planning is the cornerstone of successful machine learning projects. It guarantees all team members are working in unison towards a shared objective. A few best practices are: -
Document and align on business Impact: A crucial aspect of project management is defining the tangible benefits the machine learning project will bring to the business. While calculating this impact can be challenging for ML initiatives, a Balanced Scorecard can be a valuable tool in this process.
Define Clear Goals and Outcomes in the project plan: Ensure that all stakeholders are communicated with and aligned on the project's goals and outcomes. An essential role in any ML program is the executive or decision-maker. This person is responsible for setting performance objectives, determining values for business scorecard parameters, and prioritizing labels. Identifying and empowering this decision-maker is critical for the program's success.
Data: The Fuel for Machine Learning Success
Data is the foundation upon which successful machine learning features are built. Therefore, collecting, understanding, and preparing the data is an essential and multifaceted process. Acquiring the right data is both a science and an art, and this phase encompasses several tasks, each of which can be considered a mini-project in itself. As a project manager, allocate ample time and effort to this phase. A key learning are outlined below: -
Data Governance plan: Create a data governance plan (if not available already) which will be a framework to stewardship, manage, access and track data across the org. This plan minimizes risks and communication gaps later on.
Data collection: Gather data from various sources, such as internal repositories, sensors, and customer interviews or questionnaires. Key considerations include
Beware of bias
Make sure data is representative of population
Document the metadata, sources of data, data collection process, data collection/updating frequency.
Feature engineering: This critical step involves processing and transforming raw data into essential features for the ML model. Since feature engineering significantly impacts model performance and accuracy, the project manager should give it utmost attention.
Data cleaning: Guarantee a reliable and efficient data pipeline for cleaning and transforming activities.
Data Splitting:Create, store, and control access to various datasets for exploration, testing, training, and validation.
Feature selection process & methods: Document and communicate the methods used for selecting the most relevant features for your model.
Keeping Your Data on Track: Reproducibility, Versioning, and Storage
Reproducibility, versioning, and storage are the cornerstones of robust data management in machine learning projects. Here's why:
Documented Processes: A well-defined approach to data reproducibility and versioning ensures consistent workflows, simplifies debugging, and fosters collaboration between teams.
Versioning Everything: Version control shouldn't just apply to code. It's crucial to track changes in your data, data pipelines, models, and codebase. This allows you to revisit specific versions if needed.
Data Lineage: Implement a data lineage system to meticulously track the journey of your data, from its origin to its use in the machine learning model. This transparency facilitates troubleshooting and builds trust in your data's integrity.
Storage Solutions: Select an effective storage system capable of handling large data volumes and ensuring data accessibility for training, evaluation, and testing your machine learning model.
By prioritizing this data-centric approach, you'll ensure your machine learning project is built on a solid foundation that fuels success and fosters collaboration.