Strategies to Reduce Risks in Machine Learning Projects
- Posted by GM, Digital Solutions
- On February 2, 2021
Even as AI makes a transformative impact throughout the business world, companies must strive to mitigate risks in machine learning projects. After all, it takes a measure of time for any innovation to fully mature and best practices to be developed. Any organization hoping for a positive result from adopting AI and machine learning needs a well-considered approach.
If your small to mid-sized enterprise (SME) is either considering or in the middle of its first project using machine learning, understanding the inherent risks becomes paramount. An initial failure might give executives or stakeholders the wrong impression of AI’s potential. However, your organization’s success in a competitive business world depends on embracing tech innovations like AI. That’s why preparing for the inherent risks in machine learning is so important in order to prove the value of ML projects for your SME.
Throughout this article, we take a look at the typical risks in machine learning projects, with an eye towards helping companies craft a successful project. It provides an analysis of the ML project lifecycle, as well as an overview of a tool used to effectively collaborate throughout the effort.
The SME Must Consider The Risks of Machine Learning Projects
When implementing new tech innovations like ML, the margin of error for SMEs is minuscule compared to large enterprises. Simply put, SMEs don’t possess the massive budgets and the technical experience in ML like larger firms. This hard truth remains a major reason the SME must closely analyze the risks of any machine learning initiative. As noted earlier, a project failure might prevent the organization from fully adopting ML and its myriad of benefits.
In addition to smaller financial and technical resources, Other ML project risks include low overall data quality, poorly-defined project goals, and a lack of buy-in from business stakeholders. Once again, managing these risks becomes critical for smaller organizations hoping to take advantage of the promise of machine learning.
When the project begins, foster experimentation and an iterative approach to achieve the goals of the initiative. Additionally, make sure machine learning actually solves the relevant business problem. It’s important for executives to understand that AI isn’t “magic dust” leading to success by itself. In the end, awareness of these risks during the project planning stage helps set realistic deadlines, identifies the right talent, and eventually ensures a successful outcome.
Understanding The Phases of an ML Project Lifecycle is Critical
Any AI or machine learning project tends to follow a similar (but different) lifecycle to other software development initiatives. SMEs new to this practice must understand these concepts – and how risk fits in – before undertaking an ML effort. So, let’s look at the project lifecycle phases where mitigating risk matters.
Early in the process, in the assessment phase, clearly defining the project’s ultimate goals is vital. Failure to do so is a significant risk to success. Next, the discovery phase helps identify risks while fostering trust in the project data. A significant risk includes data privacy. Therefore, any collected data must have a purpose, proper consent, and permission to use it.
However, the real substance of the project’s effort lies within the Data Science & Data Engineering phase:
- Business Understanding: Ensuring the technical project team understands the business requirements and ultimate goals of the project at this point is critical.
- Data Prep & Understanding: Data quality matters. The data prep and understanding phase ensures enough data exists for the ultimate goal of the end deliverable, and confirms that it’s the right data. Not understanding the underlying metadata is another notable risk. In short, the wrong data is as bad as not having any data at all.
- Machine Learning Modeling: The machine learning modeling phase focuses on crafting the actual models used in the end deliverable. The process for creating ML models is definitely maturing, as software engineers gain more experience with AI. Expect to allocate time for iterations back to earlier phases to ensure the models, data, and business goals all align.
- Evaluation: The evaluation phase vets that the model measures the right items as expressed in the project’s goals, AKA your KPIs. The best ML model in the world becomes useless if the underlying business problems remain unsolved. Evaluate the efficacy of the models in relation to your goals and make enhancements as necessary.
- Production: The deployment phase moves the model into production. Following software development best practices helps manage the possible risks in this phase. Verification of the model’s performance in a real-world environment leads to ongoing maintenance, which sometimes includes revisiting earlier phases in the project lifecycle.
- Continuous Improvement: The ultimate goal remains continuous improvement as the team determines whether the ML model achieves the goal of the project. Crafting a roadmap for the next iteration of the project usually happens at this point.
Using ML Canvas to Assess Your Company’s Risk
The right tools play an important role in successfully implementing any machine learning project. Machine Learning Canvas is one such tool that fosters communication and understanding between the project team and customer stakeholders throughout an ML initiative. ML Canvas provides the means for users to concisely define the project’s goals and any potential problems.
Additionally, the ML Canvas functions as a communication conduit; ensuring all members of the project team remain on the same page throughout the project lifecycle. This approach allows the visualization and framing of any issues that arise during the project. Notably, it reduces the risk of misalignment between the ML models built and the underlying business issues those models hope to solve.
As experts in the software engineering process, Daitan can help your company take full advantage of the promise of AI. If you are interested in learning more about the Machine Learning project lifestyle or how we use ML Canvas to reduce risk on complex machine learning projects, check out our case study at the following link.
Louis Dorard is the author of the Machine Learning Canvas, founder and chairman of PAPIs.io, and holds a PhD in Machine Learning from University College London.