Machine learning techniques, especially deep learning and reinforcement learning, have recently achieved remarkable results in many fields such as computer vision, speech recognition and games. These advances have created huge enthusiasm and expectations in the business world. However, in many business machine learning projects, these expectations are often not met. Based on many projects I led, reviewed or rescued, this article explores the applicability of these advanced machine learning techniques in the business world and how to maximise their results and avoid potential blind spots that they may create.

1. LIMITS
BUSINESS
Ahmed Fattah
AI Architect, full stack data scientist and data science trainer
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2. § Current machine learning (ML) landscape
§ Applications of deep learning (DL) and reinforcement learning (RL) in business
§ Limits of machine learning
§ Overcoming ML limits in business
§ Summary, conclusions and call for action
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DL can be applied on almost all use
cases for supervised and
unsupervised learning.
Deep Learning can be used on most
regression and classification use cases
instead of algorithms such as Random
Forest and XGBoost.
Reinforcement Learning can be used
for some supervised learning use
cases that can learn for experiences
such as personalisation and as a
replacement of A/B testing.

5. § Fundamental problems
§ Problems with induction, the Black Swan and ‘No free lunch’ theorem
§ Problems the the data
§ Availability of required volume of labelled, reliable data
§ Noise and changeability in data
§ Bias in data
§ Problems with machine learning models
§ Lack of ability to generalise model or transfer knowledge
§ Brittleness of models
§ Lack of interpretability
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Problem Deep learning considerations
Fundamental problems with learning
Problems with induction, the Black
Swan and ‘No free lunch’ theorem
These problem are fundamental and affect equally all machine learning algorithms
including deep learning
Problems the the data
Availability of required volume of
labelled, reliable data
DL requires much more data then other ML algorithms and therefore DL exaggerate this
problem.
However, emerging techniques such as RL are showing promise to alleviate this problem.
Noise in data DL is more tolerant to noise (not withstanding its need for large volume of data)
Bias in data This problem affects all ML models regardless of the algorithms used
Problems with the models
Lack of ability to generalise model
or transfer knowledge
Recent advances in Transfer Learning showing great promise to increase our ability to transfer
learning from one dataset to another. However, we still have the restriction Lack of ability to
generalise model or transfer knowledge of transfer learning outside a domain
Brittleness of models DL models can be as brittle as other ML models if used on data outside training data profile
Lack of interpretability DL are much harder to interpret than simpler models

7. § Architecture design
§ Are there principled ways to design networks?
§ How many layers?
§ Size of layers?
§ Choice of layer types?
§ What classes of functions can be approximated by a feedforward neural network?
§ How does the architecture impact expressiveness?
§ Optimisation
§ What does the error surface look like?
§ How to guarantee optimality?
§ When does local descent succeed?
§ Generalisation
§ How well do deep networks generalise?
§ How should networks be regularised?
§ How to prevent under or overfitting?
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8. § Focus on data and domain knowledge as the primary enabler of business value
§ Understanding the business context and the end-to-end solution
§ Watch the ML landscape but deploy only n – 1 technologies
§ Use multiple algorithms to model the domain at least initially
§ Actively monitor models after deployment to identify performance degradation
and training blind spots.
§ Qualifying problems for ML in general and DL in particular
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10. § We reviewed the current ML landscape
§ We discussed the limits of ML and how DL & RL affect these limits
§ The conclusion is that it is best for business to:
§ Monitor new innovation in the ML landscape but deploy only n – 1 technologies
§ Focus on data and domain knowledge with proven ML algorithms to maximise business value
§ I encourage any ML practitioner to incorporate DL and RL into their toolkit while
maintaining focus on understanding data with domain knowledge.
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11. Ahmed Fattah
@afattah
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12. Ahmed Fattah
@afattah
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More focus on dataLess focus on data
LessfocusonalgorithmsMorefocusonalgorithms
Potential for
higher
business value
Suitable for ML
research
Stagnation
Space for
entrepreneurs

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