TECHNOLOGY
Machine learning
Get the most out of your customers’ data to enhance their experience, streamline operations, and drive growth
What is machine learning?
Machine learning algorithms are a subset of artificial intelligence (AI) that enable systems to learn and improve from experience without being explicitly programmed.
It’s teaching a computer to recognize patterns and make decisions, similar to how humans learn from past experiences. As if when you recognize a friend’s face in a crowd, machine learning algorithms can identify objects in images, predict stock market trends, or recommend tailored products to customers
Proof of the pudding: McKinsey found that businesses implementing AI and machine learning could see at least a 15-20% increase in profitability.
How does it work?
01
Data collection
Gathering relevant data from various sources.
02
Data preparation
Cleaning and organizing data to make it suitable for analysis
03
Model training
Using algorithms to train a model on the prepared data
04
Model evaluation
Gathering relevant data from various sources.
05
Model deployment
Gathering relevant data from various sources.
06
Monitoring and maintenance
Gathering relevant data from various sources.
How businesses are using
Machine learning
From the “new wave of productivity” to extremely personalized
experience, Machine learning is revolutionizing various industries.
Healthcare
Statistics show that ML technology in healthcare could potentially save the industry USD 150 billion annually.
For example, IBM Watson Health uses Machine learning to analyze medical images and patient data, helping doctors diagnose and treat patients more accurately.
Use cases:
- Disease detection
- Personalized treatment plans
- Predictive analytics for patient outcomes.
Healthcare
Statistics show that ML technology in healthcare could potentially save the industry USD 150 billion annually.
For example, IBM Watson Health uses Machine learning to analyze medical images and patient data, helping doctors diagnose and treat patients more accurately.
Use cases:
- Disease detection
- Personalized treatment plans
- Predictive analytics for patient outcomes.
Finance
Finance companies are going to spend USD 11 billion on AI technology.
JPMorgan Chase employs Machine learning in its Contract Intelligence (COiN) platform to analyze legal documents and extract vital data, reducing the review time from 360,000 hours to just seconds.
Use cases:
- Fraud detection
- Risk management
- Personalized banking
Retail
Machine learning development allows retailers to increase their sales by 20% and reduce inventory costs by 30%.
Amazon’s recommendation engine, powered by machine learning, accounts for at least 35% of its total sales by suggesting products based on customer behavior.
Use cases:
- Demand forecasting
- Inventory management
- Personalized marketing
Manufacturing
As of 2022, manufacturing was the most advanced field in terms of using ML with an 18.9% share.
Siemens uses machine learning in its factories to predict equipment failures and optimize production processes.
Use cases:
- Predictive maintenance
- Quality control
- Supply chain optimization
The core paradigms
Criteria
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Definition
Learning from labeled data (data with predefined categories) to predict outcomes for new data.
Finding patterns in data without labels (without predefined categories).
Trains a model through trial and error interactions with an environment, where the model receives rewards for desired actions.
Goal
Predict outcomes based on input data.
Discover hidden patterns or groupings.
Maximize cumulative reward through actions.
Algorithms in focus
Linear Regression, Decision Trees, SVM
K-means, Hierarchical Clustering, PCA
Q-learning, Deep Q-Networks, Policy Gradients
Data requirement
Requires large amounts of labeled data.
Requires large amounts of unlabeled data.
Requires a defined environment and feedback.
Advantages
High accuracy, easy-to-interpret results.
Can handle large, complex datasets.
Learns optimal actions autonomously.
Applications
Spam detection, image recognition, stock prediction
Customer segmentation, anomaly detection
Robotics, gaming, real-time decision systems
Complexity
Moderate
High
Very High
Reduce customer-processing costs
by 45% with ML and demand to speak to
an agent by 25%
Let’s discuss how we can help you achieve your goals effortlessly
and affordably.
Advanced machine learning
The table below dives deeper into advanced ML techniques. However, we should warn readers they require significant computational resources and expertise for implementation.
Criteria
Deep Learning
Transfer Learning
Neural Networks
Definition
A subset of ML with neural networks having multiple layers.
Reusing a pre-trained model on a new but similar task.
Computational models inspired by human brain structure.
Goal
Automatically discover representations from data, particularly for complex tasks.
Improve learning efficiency by leveraging existing knowledge.
Simulate brain functions to recognize patterns and make decisions.
Algorithms in focus
Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs).
Fine-tuning, domain adaptation.
Feedforward Neural Networks, Multilayer Perceptrons (MLPs), Deep Belief Networks (DBNs).
Data requirement
Requires vast amounts of labeled data.
Requires pre-trained models and less data than training from scratch
Requires structured data, scalability with large datasets.
Advantages
High accuracy for tasks like image and speech recognition.
Reduced training time and improved performance on related tasks.
Flexibility to model complex relationships and interactions.
Applications
Image classification, natural language processing (NLP), autonomous vehicles.
Medical imaging, natural language translation, personalized recommendations.
Pattern recognition, predictive analytics, game AI.
Techniques
Backpropagation, dropout regularization, data augmentation.
Model fine-tuning, transfer learning architectures like BERT and GPT
Activation functions (ReLU, sigmoid), network architectures (feedforward, recurrent).
Used for predictive analysis. It models the relationship between a dependent variable (target) and one or more independent variables (features) by fitting a linear equation to the observed data.
For instance, predicting housing prices based on factors like square footage, number of bedrooms, and location involves finding the best-fit line that minimizes the difference between the actual and predicted values.
The simplicity of linear regression makes it a powerful tool for understanding relationships within data, though it assumes a linear relationship that might not always be present.
Used for predictive analysis. It models the relationship between a dependent variable (target) and one or more independent variables (features) by fitting a linear equation to the observed data.
For instance, predicting housing prices based on factors like square footage, number of bedrooms, and location involves finding the best-fit line that minimizes the difference between the actual and predicted values.
The simplicity of linear regression makes it a powerful tool for understanding relationships within data, though it assumes a linear relationship that might not always be present.
Used for binary classification problems, where the outcome is a categorical variable with two possible values, such as spam vs. not spam or disease vs. no disease. Uses a logistic function to model the probability of the default class and outputs values between 0 and 1.
For example, logistic regression can predict whether a customer will buy a product based on their browsing behavior and purchase history. The sigmoid function applied in logistic regression helps in mapping predicted values to probabilities, making it suitable for binary outcome predictions.
Unsupervised learning technique that groups data points into clusters based on their similarities. Useful for identifying hidden patterns and segmenting data for further analysis. Widely used in market segmentation, image compression, and anomaly detection.
Another popular method, hierarchical clustering, builds nested clusters by progressively merging or splitting them based on a distance metric.
A versatile Machine learning algorithm capable of performing both classification and regression tasks. They split the data into subsets based on the value of input features, creating a tree-like structure where each node represents a feature, each branch a decision rule, and each leaf a target outcome.
Can be used to classify emails as spam or not spam by evaluating features like the presence of certain keywords. Decision trees are intuitive and easy to interpret, but they can become complex and prone to overfitting, especially with noisy data.
Enhance the predictive power and robustness of decision trees: construct multiple decision trees during training and merge their results. Each tree in the forest is trained on a random subset of the data and features, which helps in reducing overfitting and improving generalization.
In predicting loan defaults, a random forest would aggregate predictions from numerous decision trees, resulting in a more accurate and stable prediction model.
How to choose the right
AI platform for
Machine learning
Consider the following aspects before committing.
MLOps Capabilities
Machine learning operations (MLOps) capabilities should match specific criteria:
Automated deployment and CI/CD pipelines
- Automated deployment and CI/CD pipelines
- Examples: AWS SageMaker, Google AI Platform.
Model monitoring and management
- Tracks model performance, accuracy, and drift in real time.
- Examples: Azure machine learning.
Scalability and resource management
- Dynamically scales resources to handle large datasets and complex models.
- Examples: Google Kubernetes Engine (GKE), Amazon EKS.
Generative AI Capabilities
Check the platform’s generative AI features:
Pre-trained models and customization
- Access to advanced models like GPT-3 for text generation or GANs for image synthesis.
- Examples: OpenAI’s GPT-3 API, NVIDIA’s StyleGAN.
Integration with existing workflows
- Seamless integration with your current business processes via APIs and SDKs.
- Examples: IBM Watson.
Ethical AI and bias mitigation
- Tools for auditing and reducing biases in generative outputs.
- Examples: Microsoft’s Fairlearn, IBM’s AI Fairness 360.
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