Detecting and Fixing your AI Mess - Part 1
Warning signs that an AI projects might fail and how to fix/prevent that from happening
In my last issue, I discussed problems faced by AI teams and the kind of impact they can cause over the course of the project and in some rare cases - on the organization as a whole. And as promised, this post covers the more practical aspects of these problems - how to detect that your team/project is in the middle of navigating each issue and how to think through fixing it.
Each problem manifests differently in different teams - which can depend on the skill constitution of the team, maturity of the project, organizational objectives and something as overlooked as “culture”.
Please note that this post by no means say that AI projects must never face any of these problems.
Most projects do face one or more of these problems at a time - and it’s okay. The idea is to help you realize that something needs fixing before it gets too late and impact the project detrimentally.
Whether you're just starting your AI journey or trying to rescue a struggling project, this analysis can provide valuable insights into navigating these common issues.
1. Data Quality and Governance Issues
Detection Signals
You're likely facing data quality issues if:
Data scientists spend more than 70% of their time cleaning and preprocessing data
Impact: This indicates a substantial burden on the team, reducing their efficiency and delaying model development and deployment. It suggests that the raw data is highly inconsistent, incomplete, or contains significant noise.
There's no clear documentation of data sources and transformations needed
Impact: Without proper documentation, understanding the lineage and transformations of data becomes challenging. This can lead to misunderstandings, errors in data processing, and difficulty in onboarding new team members.
Data catalog is basically just tribal knowledge
Impact: Reliance on informal, undocumented knowledge for data management leads to inconsistencies and data silos. This can result in inefficiencies and increased risk of data misinterpretation or misuse when key personnel are unavailable.
Several “copies” of the same data points with slightly different schemas
Impact: This situation leads to data redundancy and inconsistency. Managing and reconciling different versions of the same data can be time-consuming and error-prone, causing confusion and potential data integrity issues.
Different teams have conflicting versions of the "same" data
Impact: This causes misalignment in analyses and decision-making processes. Conflicting data can lead to disputes, mistrust in data accuracy, and inefficiencies as teams spend time reconciling discrepancies.
No way or complex pathways to figure out the true source of some data
Impact: Ambiguities in data sourcing can lead to mistrust and hesitation in using certain datasets, affecting overall data-driven decision-making processes.
Heavy reliance on manual processes for data handling and integration
Impact: Manual processes are prone to errors and inefficiencies, increasing the risk of data quality issues and slowing down data operations.
Inadequate access controls and data governance policies - anyone can easily access (or worse - update) the production data without much oversight
Impact: This can lead to unauthorized access, data breaches, and compliance risks. It also complicates tracking data usage and ensuring data privacy.
Queries take too long to get the necessary metrics - that people start to avoid running them or the compute runs out of memory
Impact: Timely decision-making is hampered, and real-time analytics capabilities are compromised, affecting operational efficiency and responsiveness.
Your model performance varies significantly between training and production
Impact: Such variation suggests issues with data consistency, distribution shifts, or differences in feature engineering between the training and production environments. It can undermine the reliability of the models and affect business outcomes.
Prevention and Solutions
Coming up with and working through a data strategy is a complex and demanding process, but here are some highlights:
Implement robust data governance frameworks:
Establish clear data ownership and access roles
Create standardized data quality metrics and monitoring
Develop comprehensive data documentation practices
Invest in data quality tools and processes:
Deploy automated data validation pipelines
Implement data versioning systems
Create feedback loops for continuous data quality improvement
Foster data-aware culture:
Include all stakeholders in discussions on data quality importance
Create incentives for maintaining high data quality standards
Establish clear communication channels between data producers and consumers
For a more detailed post on data strategy, read What comes first - AI or Data Strategy?
2. The AI Expectation Gap
Detection Signals
Watch for these warning signs:
Stakeholders using phrases like "just like ChatGPT" or "similar to DALL-E"
Impact: These comparisons can set unrealistic expectations, assuming that custom AI solutions can easily match the capabilities of advanced, highly specialized models. This overlooks the substantial resources, data, and expertise required to develop and fine-tune such models.
Project timelines that don't account for data collection, experimenting and/or model training iterations
Impact: Underestimating the time needed for these crucial phases can lead to rushed and suboptimal outcomes. It reflects a lack of understanding of the iterative nature of AI development, risking incomplete or flawed models.
Resistance to discussing limitations or edge cases
Impact: Avoiding conversations about the limitations of AI models prevents stakeholders from fully understanding the model's capabilities and constraints. This can result in overconfidence in the model and potential failure in real-world applications where edge cases are prevalent.
Expectations of close to perfect accuracy or human-level performance
Impact: Such expectations are often unrealistic, as AI models typically have inherent limitations and may not achieve perfect accuracy, especially in complex or ambiguous tasks. This can lead to disappointment and mistrust in AI solutions when they fall short of these high expectations.
Stakeholders believe the AI can operate fully autonomously without human input - even in critical functions.
Impact: Ignoring the need for human oversight can result in unmonitored AI decisions that might be flawed or biased. Human intervention is crucial for validating AI outputs, handling exceptions, and ensuring ethical use of AI in some critical features.
Not considering scaling hurdles in favor of better accuracy
Impact: Focusing solely on accuracy without addressing scalability can hinder the deployment and operationalization of AI models. Practical challenges like computational resources, integration with existing systems, and data throughput need to be considered to ensure the model can perform effectively in a production environment.
Stakeholders expect AI to solve problems outside its designed scope or capability.
Impact: Misalignment between the AI's actual capabilities and stakeholder expectations can result in the misapplication of the technology, leading to ineffective solutions and wasted resources.
Prevention and Solutions
Education and Communication:
Conduct AI literacy workshops for stakeholders
Share case studies of both successful and failed AI projects
Create detailed documentation of system limitations and requirements
Expectation Management:
Start with smaller, achievable pilots
Set clear performance metrics and acceptable ranges
Regularly communicate and demonstrate progress and challenges
Use proof-of-concepts to demonstrate real-world limitations
Stakeholder Engagement:
Involve business users early in the development process
Create feedback loops for continuous alignment
Document and share lessons learned from each iteration
3. Technical Debt in AI Systems
Detection Signals
Your project might be suffering from AI technical debt if:
There's no standardized process for model deployment
Impact: Without a consistent deployment process, models may be deployed haphazardly, leading to inconsistencies in performance, difficulties in scaling, and challenges in debugging and updating models. This increases the risk of errors and reduces the reliability of the AI system.
Documentation is sparse or outdated
Impact: Poor documentation makes it difficult for team members to understand the system, reproduce results, or maintain the models. It can lead to a loss of valuable knowledge when team members leave and hinder the onboarding of new team members, increasing the likelihood of introducing further technical debt.
Lack of a clear version control strategy for models and code.
Impact: Without proper version control, it's difficult to track changes, revert to previous versions, or collaborate effectively. This can lead to confusion, duplication of work, and difficulty in identifying the source of errors or performance issues.
Data preprocessing steps are inconsistent
Impact: Inconsistent preprocessing can result in data discrepancies and variations in model performance. It makes it difficult to ensure that models are trained and evaluated on data that is processed in the same manner, leading to unpredictable and unreliable outcomes.
Model monitoring is manual or absent
Impact: Without automated monitoring, it's challenging to track model performance in real-time, detect issues promptly, and ensure that models are operating as expected. This can result in undetected drifts, performance degradation, and potential failures in production environments.
Code is poorly structured with numerous quick fixes
Impact: Code that is cluttered with quick fixes and lacks proper structure is difficult to maintain, debug, and extend. It increases the risk of introducing new bugs and makes it challenging to implement improvements or scale the system. This can lead to longer development cycles and higher maintenance costs.
Insufficient or no automated testing for models and data pipelines.
Impact: Lack of robust testing increases the risk of undetected bugs and errors making it to production. This can lead to unreliable model predictions, increased technical debt, and reduced trust in the AI system.
Technical debt is accumulated without plans for repayment or refactoring.
Impact: Accumulating technical debt without addressing it leads to a growing burden of maintenance issues and system inefficiencies. Over time, this can significantly slow down development, reduce system performance, and increase the risk of critical failures.
Prevention and Solutions
Establish MLOps Practices:
Implement automated testing and deployment pipelines
Create standardized model development workflows
Use version control for both code and models
Maintain comprehensive documentation
Design for Maintainability:
Create modular, reusable components
Implement proper logging and monitoring
Design for model updates and retraining
Build with scalability in mind
Regular Maintenance:
Schedule regular code and model reviews
Allocate time for refactoring and optimization
Create technical debt dashboards
Set clear criteria for when to rebuild vs. maintain
4. Cross-Functional Collaboration Challenges
Detection Signals
Collaboration issues are present when:
There's frequent miscommunication (or minimal communication) between technical and business teams
Impact: Miscommunication can lead to misunderstandings about project goals, requirements, and priorities. This results in misaligned efforts, project delays, and potential rework.
Domain experts feel excluded from the development process
Impact: Excluding domain experts can result in a lack of critical industry-specific knowledge, leading to solutions that may not fully address business needs or are not practical for end users. Involvement of domain experts ensures that the technical solutions are relevant, accurate, and actionable.
Software engineers struggle to implement data science models
Impact: Difficulties in implementation can stem from a lack of understanding of data science methodologies, insufficient collaboration, and/or technical barriers. This hinders the deployment and operationalization of data science models, reducing their impact and value to the business.
Different teams have conflicting priorities and timelines
Impact: Conflicting priorities and timelines can create bottlenecks, causing delays and frustration. It prevents teams from working cohesively towards common goals, leading to inefficiencies and potential project failures.
Knowledge sharing is limited or ineffective
Impact: Ineffective knowledge sharing results in siloed information, duplicated efforts, and missed opportunities for leveraging existing expertise. It impedes innovation and slows down problem-solving processes. Establishing robust knowledge sharing practices promotes learning, efficiency, and continuous improvement.
Teams show resistance to adopting new methods, tools, or collaborative practices.
Impact: Resistance to change can stifle innovation and slow down progress. Encouraging a culture of openness to new ideas and continuous improvement is essential for adapting to evolving business needs and technological advancements.
Leadership struggles to effectively facilitate collaboration and resolve conflicts.
Impact: Ineffective leadership can lead to unresolved conflicts, poor team dynamics, and a lack of direction.
Prevention and Solutions
Establish Clear Communication Channels:
Regular cross-functional meetings (but only with a definitive agenda in mind)
Shared documentation and knowledge bases
Standardized reporting formats
Clear escalation paths for issues
Create Collaborative Processes:
Joint planning sessions
Cross-functional review/feedback meetings
Shared success metrics
Build Cross-Functional Collaborations:
Embed domain experts in AI teams
Assign liaison roles between departments
Rotate team members across functions
5. Model Monitoring and Maintenance Challenges
Detection Signals
Look for these warning signs:
No systematic monitoring of model performance
Impact: Without systematic monitoring, it's difficult to track how models perform over time, identify issues early, and ensure they continue to meet business objectives. This can lead to undetected performance degradation and potential failures in production.
Unclear thresholds for model retraining
Impact: If there are no predefined thresholds to indicate when a model should be retrained, it can result in models being used beyond their effective lifespan. This leads to decreased accuracy, relevance, and reliability of predictions.
Absence of data drift detection
Impact: Data drift occurs when the statistical properties of input data change over time, leading to model performance deterioration. Without detection mechanisms, data drift can go unnoticed, causing the model to make increasingly inaccurate predictions.
No plan for handling model failures
Impact: A lack of contingency plans for model failures means that when issues arise, they can cause significant disruptions and require ad-hoc, potentially ineffective responses. This can lead to prolonged downtimes and loss of trust in the system.
Limited explainability of model decisions
Impact: Without transparency into how models make decisions, it becomes challenging to explain outcomes, identify biases, and gain stakeholder trust. Limited visibility hinders the ability to troubleshoot and improve models effectively.
Reactive rather than proactive maintenance
Impact: Reactive maintenance approaches result in addressing issues only after they have caused problems. This can lead to higher costs, more downtime, and reduced model performance.
Inconsistent use of monitoring tools and practices across models and teams.
Impact: Inconsistency in monitoring practices can lead to gaps in oversight, with some models being more closely monitored than others. This inconsistency can result in uneven performance and reliability across different models and projects - especially when these models have interdependencies in a solution pipeline.
Limited resources allocated for ongoing model maintenance.
Impact: Insufficient resources for maintenance can lead to delayed updates, unaddressed issues, and overall neglect of model upkeep. This undermines the long-term success and reliability of AI systems.
No automated alerting systems to notify of performance issues or anomalies.
Impact: Without automated alerts, issues may go unnoticed until they significantly impact operations. Automated alerting systems enable timely responses to performance drops and anomalies, minimizing negative effects.
No metrics established for evaluating model performance post-deployment.
Impact: Without clear evaluation metrics, it's difficult to assess whether models are meeting their intended objectives in production. This lack of evaluation can lead to unnoticed under-performance and misalignment with business goals.
Prevention and Solutions
Implement Comprehensive Monitoring:
Deploy automated performance monitoring
Set up data drift detection
Create alerting systems for anomalies
Track business impact metrics
Establish Maintenance Protocols:
Define clear retraining triggers
Create model update procedures
Implement A/B testing frameworks
Maintain version control for models
Build Robust Infrastructure:
Deploy model monitoring tools
Create fallback mechanisms
Implement automated testing
Maintain comprehensive logging
That’s it for this issue. I’m not going to overwhelm you with all that could be going wrong in your AI project - in a single sitting. I just don’t have the heart to do that ;)
But, anyway, comment below if you’ve faced any of these issues and how did you resolve them or what impact they had on the overall project. We’ll discuss the next set of issues in the next post.
Until then - stay calm and keep building with AI. Sayonara.