Integrating Machine Learning and AI in your business
01
Machine learning is one of the main branches of artificial intelligence. It presents multiple opportunities for companies and revolutionizing use cases : process automation, content generation, decision support, etc.
80% of the organizations that have adopted it have found that this technology has increased their revenue. (source: Mckinsey)
The purpose of machine learning is to enable machines to develop an autonomous learning capability. To do this, they need to be provided with datasets. Rather than programming a task, the machine is allowed to explore the data so that it learns by detecting patterns (recurring patterns) and improves its performance.
02
| Supervised | Unsupervised | Semi-supervised | Reinforcement Learning | |
|---|---|---|---|---|
| Mechanism | The model trains from a set of labeled or annotated data. | Data exploration is done autonomously by the machine. | Some of the data is labeled, but the machine still explores it on its own. | By assigning rewards or penalties to the model based on its actions, it improves by trying to get the highest reward. |
| Examples of algorithms |
|
|
|
|
Supervised
The model trains from a set of labeled or annotated data.
Examples of algorithms :
-
Linear regression :
- Asset valuation
- Performance forecasting
-
Support vector machines :
- Fraud detection
- Image classification
-
Decision trees :
- Obtain a diagnosis
- Identify growth opportunities
Unsupervised
Data exploration is done autonomously by the machine.
Examples of algorithms :
-
Apriori :
- Basket analysis
- Product or content recommendations
-
K-Means :
- Customer segmentation
- Data structuring
-
Neural networks :
- Image recognition
- Anomaly detection
Semi-supervised
Some of the data is labeled, but the machine still explores it on its own.
Examples of algorithms :
-
GAN (generative adversarial network) :
- Image generation
- Photo editing
-
Naïve bayes :
- Text classification
- Spam filter
Reinforcement Learning
By assigning rewards or penalties to the model based on its actions, it improves by trying to get the highest reward.
Examples of algorithms :
-
Q-learning :
- Financial portfolio management
- Robotics
-
Deep Q Network (DQN) :
- Industrial process control
- Autonomous navigation
03
How are companies currently using machine learning ? Here is a list of the main use cases explored by organizations :
Personalize the customer experience
- Machine learning-based algorithms analyze customer behavior to personalize their experience. For example, by offering them recommendations for products or content related to their interests, by anticipating their needs, or by identifying the best time and channel to make contact
Anticipate trends
- Machine learning is often used to predict trends, such as forecasting the trajectory of the sales curve or anticipating fluctuations in consumer demand, etc. This allows companies to make informed decisions and plan their actions to stay in line with the market
Automate customer service
- Chatbots rely on machine learning to learn from every problem customers encounter. This allows for better responses, automates the handling of simple or recurring issues, and directs customers to the resources they need
Detect fraud
- A system can be trained to identify suspicious behavior and analyze it in a given context to detect fraudulent activity and minimize the financial loss associated with it
Automate operational tasks
- Machine Learning makes it possible to automate simple tasks that have low added value, thus reducing associated costs and increasing productivity and efficiency
04
Identification and framing of the need
- In-depth interviews to understand your business goals
- Definition of the scope of intervention
- Identification of relevant use cases for machine learning within your organization
- Goal setting
Integration with existing systems and deployment
- Integrate machine learning models into your existing infrastructure
- Secure deployment of models via APIs or other mechanisms, ensuring easy and efficient use
Data Preparation
- Data cleaning and pre-processing
- Using methods such as encoding categorical variables
Monitoring & Maintenance
- Set up monitoring systems to track the performance of your models in real-time
- Maintain and update learning models to adapt them to changes in your business
Development and training of AI and Machine Learning models
- Selecting and designing machine learning models for your project
- Optimization of these models examples : searching for hyperparameters, assembling models, using deep neural networks...
