Understanding Machine Learning Project Success

🔍 Why 87% of Machine Learning Projects Fail

According to a 2019 VentureBeat study, most ML projects fail—not due to tech issues alone, but due to poor problem selection.

Three Key Screening Questions:

✅ 1. Is there a real problem to solve?

• Many projects fail because no meaningful problem exists.
• Find problems by:
  • Listening to users: 1:1 interviews or focus groups to identify pain points.
  • Observing users in context: Shadow users to discover latent or overlooked problems.
• Also ask: How are users solving this today?

🧠 2. Can ML realistically solve this problem?

• Feasibility depends on:
  • What ML can do today (e.g., image classification = easy; novel writing = hard).
  • How tech evolves—what's impossible today might be easy tomorrow.
• Data availability is crucial:
  • Do you have enough quality data?
  • Can you access or collect the needed data?

💰 3. Does solving this matter to someone?

• Business impact matters as much as feasibility.
• Prioritize problems that:
  • Are feasible to solve with ML.
  • Have high value or urgency for users.

🎯 Takeaway

Successful ML projects start with picking the right problem: one that exists, can be solved with ML, and truly matters to someone.

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🧠 Understanding the Problem Before Using ML

1. State the Goal

• Define the goal clearly in non-ML terms.

Example Goals:

• Weather app: Predict precipitation.
• Fashion app: Generate shirt designs.
• Mail app: Detect spam.
• Financial app: Summarize financial news.

2. Decide if ML is the Right Tool

• Not all problems need ML; prefer simple solutions first (heuristics, rules).
• Types of ML:
  • Predictive ML: Makes predictions.
  • Generative AI: Creates content.
• Benchmark against a non-ML solution to evaluate need.

3. Verify Data Availability (for Predictive ML)

• Good data must be:
  • Abundant
  • Consistent and reliable
  • Trusted
  • Available at prediction time
  • Correct
  • Representative of real-world conditions

4. Check for Predictive Power

• Features must strongly correlate with what you're trying to predict.
• Techniques:
  • Manual experimentation
  • Pearson correlation
  • Adjusted Mutual Information
  • Shapley values

5. Link Predictions to Actions

• Predictions must lead to user-impacting actions.

Example:

• Rain prediction should update the weather app forecast.

📖 Example Case Study

• Scenario: Predict wait times in a call center.
• Evaluation:
  • Heuristic may not be accurate enough.
  • Dataset has predictive features.
• Decision: Use ML.

🎯 Takeaway

Always start by understanding the goal, verifying if ML is needed, checking your data quality, and ensuring predictions drive real actions.

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🧠 From Problem Definition to Solution: How to Find the Right Approach

🔬 Run Many Small Experiments

• Use the scientific method:
  • Formulate a hypothesis.
  • Test it with users.
  • Analyze feedback.
  • Refine or pivot.
  • Repeat until convergence.

🖼️ Use Visual Mockups for Better Feedback

• Start simple:
  • Wireframes
  • Screenshots
  • Interactive demos
• Iterate mockups ➔ feedback ➔ improve ➔ repeat.

✅ When Are You Ready to Develop?

• Problem is validated.
• Existing solutions are understood.
• Solution is converged.
• Technical feasibility is screened.

🎯 Takeaway

Good products emerge through small experiments, user feedback, visual mockups, and technical validation.

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🧠 When and Why to Use Machine Learning in Products

🚫 Bad Reasons to Use ML

• Executive pressure.
• "Because it's cool."

✅ Good Reasons to Use ML

ML should add business value through:

1. Automation

• Benefits:
  • Reduces repetitive tasks.
  • Improves quality.
• Example: Pizza quality control with computer vision.
• Risks:
  • Poor adaptability to changes.
  • Lack of ethics.
  • Accountability issues.

2. Prediction

• Benefits:
  • Supports better decision-making.
• Example: Forecasting next-day power demand.
• Risks:
  • Environmental changes can degrade performance.
  • High cost of wrong predictions.

3. Personalization

• Benefits:
  • Tailors user experiences.
• Example: Netflix movie recommendations.

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🔀 Heuristics vs Machine Learning

🧩 What Are Heuristics?

• Simple rule-of-thumb solutions.

Examples:

• Predict sales by average.
• Recommend the highest-rated item.

⚖️ Comparison Table

Aspect	Heuristics	Machine Learning
Ease	Easy to create and maintain	Complex and costly
Cost	Minimal computational cost	High computational cost
Interpretability	Simple to explain	Often opaque
Adaptability	Manual updates	Retrain with new data
Scope	Good for simple problems	Handles complex, large data

🛠️ Best Practice When Starting an ML Project

• Start with a heuristic baseline.
• Solve the problem with simple rules.
• Transition to simple ML models if needed:
  • Linear regression
  • Logistic regression
  • Decision trees
• Compare ML performance to heuristic baseline.

🎯 Takeaway

Machine learning is powerful but costly. Use it only when it adds clear business value, and always benchmark against simple solutions first.

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