What is machine learning, and what does it mean for your business?

Data has become an essential economy-driving commodity, but companies are inundated with so much information, it can be hard to figure out how to make the most of it. Machine-learning solutions might be the tools you need to harness your data, unlock insights, improve decision-making, and drive your growth. 

Designed to sift through massive datasets and identify patterns, these advanced systems allow businesses to extract maximum value from their data. Read on to discover how this technology is shaping the future of business.  

What is machine learning?

In machine learning (ML), artificial intelligence (AI) systems are trained on vast amounts of data to develop statistical models and algorithms that improve their performance on certain tasks. The “learning” refers to the algorithm’s ability to adjust its models based on new data, becoming more accurate and efficient over time. It’s a departure from traditional applications and systems that can only operate based on pre-planned instructions and coding. Instead, ML algorithms analyze large datasets to identify patterns and use this training to improve their decision-making and predict outcomes more accurately. 

Machine learning algorithms are highly versatile and can handle many tasks, including:

• Traffic prediction

• Anomaly detection

• Image recognition

• Natural Language Processing (NLP)

• Product recommendations

• Fraud detection

• Customer segmentation

From marketing to healthcare and content delivery, machine learning is revolutionizing modern business by automating intricate processes, extracting valuable insights from data, and empowering companies to make faster, more informed decisions, leading to increased innovation and a sharper competitive edge. 

How does machine learning work?

Machine learning involves training AI models on massive collections of data to enhance their ability to identify patterns and adjust their skill in handling new inputs. 

Here are the most important steps in the process: 

• Data collection and preparation: ML algorithms acquire data from a range of sources such as web crawlers, databases, sensors, or APIs. This data can be both structured (numbers, tables) and unstructured (images, text). High-quality, diverse, and representative information is crucial because it directly influences the accuracy and generalizability of the model. The raw data is then sorted, labeled, and grouped into various classes to allow for effective evaluation of the model’s performance. 

• Data analysis: Once collected, the system must clean the data and preprocess it to remove noise, inconsistencies, and irrelevant information. This process includes dealing with empty values, rectifying inaccuracies, and normalizing the data. 

• Choosing a model: ML models come in many varieties, including decision trees, support vector machines, or neural networks. The selected model will determine the ML system's approach to identifying patterns and making predictions. Developers select the model best suited to the task at hand. 

• Training: The next step is to feed the data into the chosen model in a process known as training. The model uses this to learn by adjusting internal parameters (weights, biases, etc.) to reduce errors between its predictions and real-world outcomes. The goal is for the model to identify underlying patterns and relationships to generalize to new, unseen data.

• Evaluation: After training, developers test the model’s performance using a separate validation or test dataset that it has never seen before. This step measures how well the model generalizes to new data and identifies issues in its own decision-making processes. 

• Tuning: With the initial evaluation complete, it’s time for the developers to fine-tune the ML model to improve performance. The priority for this step is to find the best combination of settings that minimize the model’s error rate and enhance its predictive capabilities. 

• Prediction: In the deployment phase, the ML model is fed novel data and tasked to come up with predictions or classifications. The model can then send these insights to support various applications. Its performance will continue to be monitored in real-time, and it may be periodically retrained with new data to maintain accuracy as conditions change. 

What are the main types of machine learning?

Machine learning can be categorized into four types: unsupervised, semi-supervised, supervised, and reinforcement learning. Here’s a detailed breakdown of what sets these approaches apart:

• Unsupervised learning: In this approach, data is unlabeled, and the model attempts to find hidden patterns or intrinsic structures within the information. Common techniques for unsupervised learning algorithms include clustering and association. Clustering is a process in which data points are grouped based on their similarities, while association identifies relationships or patterns among variables in a dataset. 

• Semi-supervised learning: Combines supervised and unsupervised learning by using a modest quantity of labeled data in conjunction with a greater amount of unlabeled datasets to improve accuracy.

• Supervised learning: Uses labeled data for training and involves the model learning to map input data to the correct output based on provided examples. Applications for this approach include spam identification, regression analysis, and image analysis.

• Reinforcement learning: Involves training an “agent” (entity or software program) to make a sequence of decisions, rewarding desirable outcomes and penalizing poor ones. It's widely used in the fields of robotics and gaming.

The top 10 benefits of machine learning

The primary benefit of machine learning is its ability to react and learn as new data is fed into the system. This means that you can achieve increasingly accurate predictions over time. But that’s a fairly broad statement. How can machine learning help your business?

Here are some of the specific advantages of applying machine learning: 

1. Better data handling: ML algorithms enable you to get more value from your data with highly efficient processing and analysis that extracts valuable insights. 

2. Automation: Machine learning based on fine-tuned models has revolutionized automation by widely expanding the range and complexity of tasks that computers can complete, freeing up time for more strategic activities. 

3. Enhanced personalization: ML lets you provide customized experiences and tailored recommendations on an individual level, making interactions personal and relevant. This enables specific product suggestions and hyper-relevant customer service.Improved decision-making: Uncover hidden patterns and emerging trends, supporting more informed and data-driven decision-making across various fields.

4. Predictive maintenance: Organizations are increasingly relying on machine learning algorithms for predictive maintenance. For industries relying on complex machinery, machine learning can predict equipment failures before they occur, reducing delays and maintenance expenses.

5. Natural language processing: Machine learning enables computers to accurately comprehend and produce human language, improving how humans can interact with machines. This enables the development of sophisticated chatbots, translation services, and voice assistants. 

6. Optimization of resource allocation: From supply chain management to content distribution, machine learning helps optimize the allocation of resources, increasing efficiency and maximizing cost savings.

7. Threat Detection and Response: With machine learning security solutions, companies can quickly detect and block security threats, defending systems from attempted attacks. Fastly leverages machine learning for cybersecurity use cases, detecting threats and anomalous activity. ML models analyze network traffic patterns in real-time to identify and block potential attacks before human security teams even become aware there’s a problem.

8. Customized and Optimized Delivery: Machine learning powers intelligent Content Delivery Networks (CDNs). Fastly's ML models predict optimal caching and routing strategies based on factors like user location, device type, and content properties. With these strategies, the system automatically tailors the delivery to provide the fastest possible performance and a smooth user experience.

Explore Fastly’s ML-powered solutions by starting your free trial today, and check out our ML inference demo.