Machine learning (ML) is currently one of the fastest-growing technologies, shaping how businesses, healthcare, finance, and entertainment function. Reports suggest that 70% of companies are already experimenting with ML, from chatbots in customer support to fraud detection in banking.
But building an ML model is not just about coding an algorithm. It is a structured cycle of stages, and skipping even one can cause the whole project to fail. AWS (Amazon Web Services) describes this as a six-stage life cycle.
Why ML Projects Need a Structured Lifecycle?
Machine learning is exciting, but it’s not magic. Many projects fail because teams jump straight into coding without following a process. Think of building a house — you would never start laying bricks before designing the blueprint, buying the right materials, and checking the foundation. The same is true for ML.
A structured lifecycle makes sure every stage is done in the right order – setting business goals, framing the problem, preparing quality data, training and testing models, deploying them, and then monitoring results. Without this, projects may look promising in early stages but collapse later when faced with real-world data.
Industry research shows that up to 80% of AI projects fail to deliver business value, often because organizations skip critical steps like problem framing or monitoring. A lifecycle acts as a roadmap that keeps the project focused, measurable, and practical.
Benefits of Following the Lifecycle Properly
- Better alignment with business outcomes: The project stays connected to real goals, like reducing waste or improving customer service.
- Reliable and accurate models: Clean data, proper training, and careful evaluation lead to trustworthy predictions.
- Long-term adaptability: Continuous monitoring ensures models stay relevant as trends and customer behaviour change.
- Reduced project failure rates: A clear roadmap lowers the chance of wasted time and money.
In short, the lifecycle helps transform ML from an experiment into a reliable tool for growth.
6 Stages of Machine Learning Lifecycle
Let’s explore each stage in detail and see why each stage matters.
1. Business Goal Identification
This is the very first step. Before looking at data or algorithms, the team must ask:
- What business challenges are we trying to solve?
- Why should we use machine learning instead of traditional methods?
- How will we measure success?
For example, a retailer might aim to reduce unsold stock, a bank may want to detect fraudulent transactions, or a hospital may want faster disease detection. Success is measured using key performance indicators (KPIs), such as “reduce waste by 30%” or “detect fraud with at least 95% accuracy”.
Why this stage matters?
If goals are indefinite or unclear, the model may end up solving the wrong problem. A clear business objective ensures the ML project is not just a science experiment but a drive of real value.
2. ML Problem Framing
Once the goal is clear, you must reframe it as a machine learning question. Consider this as translating a real-world challenge into a question a machine can answer.
During this stage, teams must decide:
- Type of learning: is it classification (yes/no), regression (numbers), or clustering (groups)?
- Labels: What are we predicting? For returns, the label is “returned” or “not returned”.
- Boundaries: What is in scope and what is not?
This step often needs collaboration between domain experts (people who know the business well) and data scientists (people who know ML).
Why this stage matters?
If the problem is framed incorrectly, even the best models will not help. For example, predicting monthly sales when the goal was to predict weekly store-level sales creates a gap between output and business need. Correct framing keeps the technical solution aligned with business reality.
3. Data Processing
Data is the fuel of machine learning. However, real-world data is usually messy: incomplete, inconsistent, or noisy. That’s why this stage is often the longest and most effort-intensive.
This stage is further classified into:
- Data collection – Gathering information from databases, sensors, logs, APIs, or surveys.
- Data cleaning – Fixing missing values, removing duplicates, and correcting errors.
- Feature engineering – Creating meaningful inputs for the model. For instance, instead of just using “date,” we may create “day of the week” or “is a holiday”.
- Splitting data – Dividing into training, validation, and testing sets. Typically, 80% of the data is used for training and 20% for evaluation.
Experts report that up to 80% of the total ML project time is spent on preparing and cleaning data.
Why this stage matters?
The saying, “garbage in, garbage out”, fits perfectly here. If the data is poor, the model will learn the wrong things. Strong data processing ensures the model gets reliable, relevant, and representative information, making predictions more accurate.
4. Model Development
At this stage, the real “machine learning” begins. This stage includes:
- Training the model – Feeding the training dataset to the algorithm so it can learn patterns. For example, a model might learn that “rainy weather + weekend = higher demand for the umbrellas”.
- Hyperparameter tuning – Adjusting settings (like learning rate or decision tree depth) to improve accuracy.
- Evaluation – Testing the model on unseen data (the test set). Metrics such as accuracy, precision, recall, or F1-score tell us how good the model is.
It’s also common to compare multiple models (e.g., logistic regression, decision trees, neural networks) and pick the best one.
Why this stage matters?
This is the heart of machine learning, but its success depends heavily on earlier stages. A poorly framed problem or dirty data will still create a bad model. Careful training, tuning, and evaluation ensure the model is robust and reliable before going live.
5. Model Deployment
A model sitting on a data scientist’s laptop has no value. Deployment makes it available in the real world, where it can generate predictions and influence decisions.
Deployment can happen in several ways:
- Batch predictions: Running the model daily or weekly to update reports.
- Real-time predictions: Integrating with apps so users get instant answers (like Netflix recommending movies).
- Edge deployment: Putting models on mobile devices or IoT hardware for offline use.
Deployment also requires infrastructure considerations – scalability, latency, security, and integration with existing business systems.
Why this stage matters?
Deployment is the bridge between research and business impact. Without this step, the model is just an experiment. Proper deployment ensures the model’s predictions actually reach decision-makers, customers, or systems where they can create value.
6. Model Monitoring
Machine learning is not “build once and done.” Data changes, customer behaviour evolves, and external conditions shift. Over time, a model trained on old data may become less accurate. This is known as model drift.
In this stage, teams:
- Continuously measure performance using live data.
- Detect anomalies or drifts in predictions.
- Gather user feedback.
- Retrain the model with new data if accuracy drops.
For example, during the COVID-19 pandemic, models trained on old consumer patterns (like travel booking) quickly became outdated. Monitoring helped organizations realize when retraining was necessary.
Why this stage matters?
Without monitoring, a business may rely on a “broken” model, leading to poor decisions and financial losses. Monitoring ensures that the ML system stays reliable, up-to-date, and trustworthy in the long run.
Real Life Example to Understand the ML Lifecycle
Let’s take a simple real-world case. Imagine a supermarket that struggles with wasted fruit. Fresh items like bananas and strawberries spoil quickly if not sold in time, costing both money and customer trust. The store wants to use machine learning to forecast how much fruit will sell each day so it can stock smarter and waste less.
Here’s how the six stages of the ML lifecycle apply:
- Business Goal Identification: Reduce perishable waste by 25%
- ML Problem Framing: Predict daily fruit demand.
- Data Processing: Gather sales, weather, and holiday data; split into training, validation, and test sets.
- Model Development: Train a regression model, test on validation data, and evaluate accuracy on the test set.
- Model Deployment: Connect predictions to the ordering system
- Model Monitoring: Track error daily and retrain when patterns change.
Common Challenges in the ML Lifecycle
While the ML lifecycle provides structure, each stage has its own hurdles. In the beginning, teams may set unclear goals, leading to solutions that don’t match real needs. During data processing, poor-quality or incomplete data is a frequent roadblock – bad data leads to bad predictions.
In model development, overfitting is common, where a model performs well on training data but fails on new cases. Deployment also brings challenges like integration with existing speed and security requirements. Finally, in monitoring, many organizations neglect regular checks, causing models to drift and lose accuracy.
Identifying these challenges early helps teams plan better and avoid the common mistakes that cause ML projects to fail.
The Takeaway
By following the 6 stages of ML lifecycle properly, organizations can build ML systems that are reliable, accurate, and valuable in the long term. Training, testing, and evaluation are not separate add-ons but are built directly into Data Processing and Model Development, ensuring that models are always trained correctly, evaluated fairly, and monitored continuously.
- The Role of Personality Development in MBA Career Success - October 5, 2025
- Best Online MBA Programs in USA - September 27, 2025
- How Important is AI for MBA Students? - September 18, 2025