Answers.Body: <blockquote>
  <p>Machine learning is an approach to (1) learn (2) complex patterns from (3) existing data and use these patterns to make (4) predictions on (5) unseen data.</p>
  </blockquote>
  <p>Then there are 9 use cases here:</p>
  <ol>
  <li><p>Learn: the system has the capacity to learn</p>
  </li>
  <li><p>Complex patterns: there are patterns to learn, and they are complex ML</p>
  </li>
  <li><p>Existing data: data is available, or it’s possible to collect data</p>
  </li>
  <li><p>Predictions: it’s a predictive problem</p>
  </li>
  <li><p>Unseen data: Unseen data shares patterns with the training data</p>
  </li>
  <li><p>It’s repetitive</p>
  </li>
  <li><p>The cost of wrong predictions is cheap</p>
  </li>
  <li><p>It’s at scale</p>
  </li>
  <li><p>The patterns are constantly changing</p>
  </li>
  </ol>
  <blockquote>
  <p>However, even if ML can’t solve your problem, it might be possible to break your problem into smaller components, and use ML to solve some of them.</p>
  </blockquote>
  <p>References:</p>
  <ol>
  <li><a href="https://rads.stackoverflow.com/amzn/click/com/1098107969" rel="nofollow noreferrer">Designing Machine Learning Systems</a></li>
  </ol>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-07-31T08:49:07.090Z
Answers.LastActivityDate: 2022-07-31T12:50:55.587Z
Answers.LastEditDate: 2022-07-31T12:50:55.587Z
Answers.UserDisplayName: Lerner Zhang
Body: <p>I learned that 87% of machine learning projects fail due to these five pitfalls:</p>
  <ol>
  <li>the scope of the project is too big;</li>
  <li>the project’s scope increased in size as the project progressed—e.g., scope creep;</li>
  <li>the model couldn’t be explained, hence there was lack of trust in the solution;</li>
  <li>the model was too complex; and</li>
  <li>the project solved the wrong problem.</li>
  </ol>
  <p>And I learned that rules and heuristic approaches may be a better choice than machine learning, since the development of machine learning takes more time(and more costly) and its explanation is also harder.</p>
  <p>Then I wonder when we should just use rules and heuristics and when we should bravely take the machine learning approach?</p>
  <p>References:</p>
  <ol>
  <li><a href="https://rads.stackoverflow.com/amzn/click/com/1636390382" rel="nofollow noreferrer" rel="nofollow noreferrer">Why Ai/Data Science Projects Fail</a></li>
  </ol>
  
Tags: <machine-learning><mlops><production-systems>
Title: When is it better to utilize machine learning over heuristics?
UserDisplayName: Lerner Zhang
ThreadText: # When is it better to utilize machine learning over heuristics?
  
  (Asked by: username_0 on 2022-07-31)
  
  I learned that 87% of machine learning projects fail due to these five pitfalls:
  
  1. the scope of the project is too big;
  2. the project’s scope increased in size as the project progressed—e.g., scope creep;
  3. the model couldn’t be explained, hence there was lack of trust in the solution;
  4. the model was too complex; and
  5. the project solved the wrong problem.
  
  And I learned that rules and heuristic approaches may be a better choice than machine learning, since the development of machine learning takes more time(and more costly) and its explanation is also harder.
  
  Then I wonder when we should just use rules and heuristics and when we should bravely take the machine learning approach?
  
  References:
  
  1. [Why Ai/Data Science Projects Fail](https://rads.stackoverflow.com/amzn/click/com/1636390382)
  
  **Tags:** machine-learning, mlops, production-systems
  
  ---
  
  ## 1 answers
  
  ### Answer by username_0 on 2022-07-31. Score: 0
  
  > Machine learning is an approach to (1) learn (2) complex patterns from (3) existing data and use these patterns to make (4) predictions on (5) unseen data.
  
  Then there are 9 use cases here:
  
  1. Learn: the system has the capacity to learn
  2. Complex patterns: there are patterns to learn, and they are complex ML
  3. Existing data: data is available, or it’s possible to collect data
  4. Predictions: it’s a predictive problem
  5. Unseen data: Unseen data shares patterns with the training data
  6. It’s repetitive
  7. The cost of wrong predictions is cheap
  8. It’s at scale
  9. The patterns are constantly changing
  
  > However, even if ML can’t solve your problem, it might be possible to break your problem into smaller components, and use ML to solve some of them.
  
  References:
  
  1. [Designing Machine Learning Systems](https://rads.stackoverflow.com/amzn/click/com/1098107969)
  
  
  ---
  
  