<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>

CC BY-SA 4.0
2022-07-31T08:49:07.090Z
2022-07-31T12:50:55.587Z
2022-07-31T12:50:55.587Z
Lerner Zhang
<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>

<machine-learning><mlops><production-systems>
When is it better to utilize machine learning over heuristics?
Lerner Zhang
# 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)


---

