Machine Learning (ML) has gained widespread adoption due to its ability to learn from data and perform intelligent tasks. However, the development and deployment of ML systems come with several key challenges:
1. Ill-posed Problems
- ML works best for well-posed problems, where specifications are clearly defined, and sufficient data is available.
- Ill-posed problems lack proper structure or complete data, making it difficult to train an accurate model.
Example:
Consider a dataset with input values (x): 1, 2, 3 and corresponding output (y): 1, 4, 9.
This could fit multiple functions likey = x²,y = x + x, ory = x × x.
The model can’t decide which one is correct without more examples, showing ambiguity in the problem.
2. Requirement of Huge and Quality Data
- ML systems depend heavily on data quality.
- Issues like missing values, incorrect entries, and insufficient samples can mislead the model.
- Gathering large-scale, clean, and balanced datasets is a challenge.
3. High Computational Power
- Processing large datasets and training complex models (e.g., Deep Learning) require advanced hardware like:
- GPUs (Graphics Processing Units)
- TPUs (Tensor Processing Units)
- The time complexity of ML algorithms increases with data and model complexity, making high-performance computing essential.
4. Algorithm Complexity and Selection
- Choosing the right algorithm for a specific problem is crucial.
- ML professionals must understand:
- How algorithms work
- When to use them
- How to compare and evaluate their performance
- Managing algorithm complexity is a technical challenge.
5. Bias-Variance Tradeoff
- One of the most critical challenges in ML is maintaining the right balance between bias and variance.
| Term | Description |
|---|---|
| Overfitting | Model performs well on training data but poorly on test data due to high variance. |
| Underfitting | Model fails to learn the training data itself due to high bias. |
- A good model must generalize well, i.e., perform accurately on both known and unseen data.