The Complete Guide to the Machine Learning Lifecycle

Machine learning models don't simply come to life once they're deployed. Instead, they require constant care and feeding through updates and retraining to accommodate changing data and ensure optimal performance. Nevertheless, many organizations struggle to provide this level of ongoing maintenance for their ML models. A recent MIT Sloan Management Review study discovered that only 6% of organizations can scale AI successfully. If you’re facing similar challenges, this article can help. We'll provide an overview of the machine learning lifecycle and offer valuable insights to help you build, deploy, and maintain machine learning models efficiently and reliably, while accelerating iteration, reducing technical debt, and delivering real-world impact at scale. We will also touch upon the importance of monitoring ML Models in production.

One way to achieve these goals is by using Inference's AI inference APIs. With these tools, you can streamline deploying and maintaining your ML models to accelerate the delivery of actionable insights and improve your organization’s bottom line.

What is a Machine Learning Lifecycle?

The machine learning lifecycle describes the steps a team (or person) should use to create a predictive machine learning model. Hence, an ML lifecycle is a key part of most data science projects. For many people, it’s not clear what the difference is between a machine learning lifecycle and a data science lifecycle.

What is a Lifecycle?

A lifecycle is used to explain the steps (or phases) of a project. In short, a team that uses a lifecycle will have a consistent vocabulary to describe the work that needs to be done. While machine learning engineers and data scientists can tell the steps within a project, they may not use the exact words or even define the same number of phases.

By having a consistent vocabulary, the team can better ensure they do not “miss a step.” While you might think that experienced team members would know the steps and not skip steps, teams can easily skip steps.

The Rush to New Models

I have often seen that when the team has deadlines, it finishes one model and then goes directly to trying to create a different one without exploring how well the first model performs. This could be due to tight schedules or the team’s desire to explore many models and “play with the data.”

Benefits of an ML Lifecycle

Beyond ensuring the team does not miss a step and having a consistent vocabulary, using an ML lifecycle has another benefit. Non-technical people, such as product owners or senior managers, can better understand the work required and the project's progress.

In summary, a lifecycle framework will:

• Standardize the process and vocabulary
• Help guide the team’s work
• Allow others to understand how a problem is being approached
• Encourage the team to be more thorough, increasing the value of the work.

A Typical Machine Learning Lifecycle

There are many published machine learning lifecycles, and some are data science lifecycles. But one of the most popular frameworks is a simple machine learning lifecycle known as OSEMN. OSEMN was defined in 2010 by Hilary Mason and Chris Wiggins. OSEMN stands for:

• Obtain
• Scrub
• Explore
• Model
• iNterpret

While the original description was on a website that no longer exists, many others have noted the use of OSEMN. In short, OSEMN’s five phases are described below:

Obtain Data

This phase focuses on gathering data from relevant sources. It is also the phase when the team should consider challenges such as automating data collection (if needed).

Scrub Data

Scrubbing the data, sometimes known as “munging the data,” is required because the data obtained in step 1 is typically “messy.” For example, the data might have missing values. This is often the most time-consuming phase of a machine learning project.

Explore Data

Exploratory analysis helps gain a basic understanding of the data. For example, histograms and scatter plots can easily show the data distributions across various attributes.

Model Data

People typically envision a machine learning project involving building a predictive model. Nonetheless, the team sometimes needs to make a “good enough” model, which is not the best possible.

Interpret Results

No model is perfect, so people must understand its predictive power. In addition, this is the phase where the team needs to explore potential bias in the model.

Reframing the Machine Learning Lifecycle

In reviewing OSEMN, the first three steps can be considered part of data engineering (the tasks required to get, clean, and inspect the data), and the last two steps could be regarded as part of modeling engineering (the functions needed to build and evaluate the predictive model). This simple two-phase data science lifecycle is shown and explained below.

Data Engineering

The data engineering phase is focused on designing and building data pipelines. These pipelines get, clean, and transform data into a more easily used format to create a predictive model. Note that this data might come from multiple sources, so merging the data is also a key aspect of data engineering.

This is often where the most time is spent in an ML project, and in fact, many people are hired explicitly to do data engineering (it is a subfield of data science/machine learning).

Model Engineering

This is the phase that most people associate with building a machine learning model. During this phase, data is used to train and evaluate the model. This is often an iterative task, where the different models are tried, and the model is tuned.

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9 Stages of the Machine Learning Lifecycle

1. Problem Definition

We need to identify and frame the business problem in this initial phase. The team can comprehensively frame the situation by establishing a foundation for the machine learning lifecycle. Crucial elements such as project objectives, desired outcomes, and the scope of the task are carefully designed during this stage.

Here are some steps for problem definition:

• Collaboration: Work together with stakeholders to understand and define the business problem.
• Clarity: Write the objectives, desired outcomes, and task scope.
• Foundation: Establish a solid foundation for the machine learning process by framing the problem comprehensively.

2. Data Collection

After problem definition, the machine learning lifecycle progresses to data collection. This phase involves systematically collecting datasets that can be used as raw data to train the model. The quality and diversity of the data collected directly impact the model's robustness and generalization.

During data collection, we must consider the data's relevance to the defined problem, ensuring that the selected datasets have all necessary features and characteristics. A well-organized approach to data collection helps in effective:

• Model training
• Evaluation
• Deployment

This ensures that the resulting model is accurate and can be used for real-world scenarios. Here are some basic features of Data Collection:

• Relevance: Collect data that should be relevant to the defined problem and include necessary features.
• Quality: Ensure data quality by considering factors like accuracy and ethical use.
• Quantity: Gather sufficient data volume to train a robust model.
• Diversity: Include diverse datasets to capture various scenarios and patterns.

3. Data Cleaning and Preprocessing

With datasets in hand, we need to clean and preprocess the data. Raw data is often messy and unstructured, and using this data directly to train can lead to poor accuracy and capture unnecessary relations in the data. Data cleaning involves addressing issues that could compromise the accuracy and reliability of the machine learning model. This includes:

• Missing values
• Outliers
• Inconsistencies in data

Transforming Raw Data for Meaningful Analysis

Preprocessing involves standardizing formats, scaling values, and encoding categorical variables to create a consistent and well-organized dataset. The objective is to refine the raw data into a format meaningful for analysis and training. Data cleaning and preprocessing ensure the model is trained on high-quality and reliable data.

Basic Features

• Data Cleaning: Address issues such as missing values, outliers, and inconsistencies in the data.
• Data Preprocessing: Standardize formats, scale values, and encode categorical variables for consistency.
• Data Quality: Ensure the data is well-organized and prepared for meaningful analysis.

4. Exploratory Data Analysis (EDA)

Exploratory data analysis (EDA) uncovers insights and helps understand the dataset's structure by finding patterns and characteristics hidden in the data. During EDA, patterns, trends, and insights are provided that may not be visible to the naked eye. This valuable insight can be used to make informed decisions.

Visualizations help present statistical summaries easily and understandably. They also help make choices in feature engineering, model selection, and other critical aspects. Here are the basic features of Exploratory Data Analysis: 

• Exploration: Use statistical and visual tools to explore patterns in the data.
• Patterns and Trends: Identify underlying patterns, trends, and potential challenges within the dataset.
• Insights: Gain valuable insights for informed decision-making in later stages.
• Decision Making: Use EDA for feature engineering and model selection.

5. Feature Engineering and Selection

Feature engineering and selection is a transformative process that involves selecting only relevant features for model prediction. Feature selection refines the pool of variables, identifying the most relevant ones to enhance model efficiency and effectiveness.

The Art of Data Transformation

Feature engineering involves selecting relevant features or creating new features by transforming existing ones for prediction. This creative process requires domain expertise and a deep understanding of the problem, ensuring that the engineered features contribute meaningfully to model prediction. It helps accuracy while minimizing computational complexity.

Basic Features

• Feature Engineering: Create or transform new features to capture better patterns and relationships.
• Feature Selection: Identify the subset of features that most significantly impacts the model's performance.
• Domain Expertise: Use domain knowledge to engineer features that contribute meaningfully to prediction.
• Optimization: Balance the set of features for accuracy while minimizing computational complexity.

6. Model Selection

Model selection is an integral part of a good machine learning model. We must find a model that aligns with our defined problem and the dataset's characteristics. Model selection is an important decision that determines the algorithmic framework for prediction. The choice depends on

• Nature of the data
• Complexity of the problem
• Desired outcomes

Basic Features

• Alignment: Select a model that aligns with the defined problem and characteristics of the dataset.
• Complexity: Consider the problem's complexity and the data's nature when choosing a model.
• Decision Factors: Evaluate performance, interpretability, and scalability when selecting a model.
• Experimentation: Experiment with different models to find the best fit for the problem.

7. Model Training

The machine learning lifecycle moves to the model training process with the selected model. This process involves exposing the model to historical data, allowing it to learn:

• Patterns
• Relationships
• Dependencies within the dataset

Optimizing for Accuracy

Model training is an iterative process in which the algorithm adjusts its parameters to minimize errors and enhance predictive accuracy. During this phase, the model fine-tunes itself to better understand the data and optimize its ability to make predictions. A rigorous training process ensures that the trained model works well with new, unseen data for reliable predictions in real-world scenarios.

Basic Features

• Training Data: Expose the model to historical data to learn patterns, relationships, and dependencies.
• Iterative Process: Train the model iteratively, adjusting parameters to minimize errors and enhance accuracy.
• Optimization: Fine-tune the model to optimize its predictive capabilities.
• Validation: Rigorously train the model to ensure accuracy with new, unseen data.

8. Model Evaluation and Tuning

Model evaluation involves rigorous testing against validation or test datasets to determine the model's accuracy on new, unseen data. Techniques like accuracy, precision, recall, and F1 score can be used to check model effectiveness.

The Iterative Cycle of Evaluation and Tuning

Evaluation is critical for providing insights into the model's strengths and weaknesses. If the model fails to achieve the desired performance levels, we may need to tune it again and adjust its hyperparameters to enhance predictive accuracy. This iterative cycle of evaluation and tuning is crucial for achieving the desired model robustness and reliability level.

Basic Features

• Evaluation Metrics: Use metrics like accuracy, precision, recall, and F1 score to evaluate model performance.
• Strengths and Weaknesses: Identify the strengths and weaknesses of the model through rigorous testing.
• Iterative Improvement: Initiate model tuning to adjust hyperparameters and enhance predictive accuracy.
• Model Robustness: Iterative tuning achieves the desired model robustness and reliability levels.

9. Model Deployment

Upon successful evaluation, the machine learning model is ready for deployment for a real-world application. Model deployment involves integrating the predictive model with existing systems, allowing businesses to use it for informed decision-making.

Basic Features

• Integration: Integrate the trained model into existing systems or processes for real-world application.
• Decision Making: Use the model's predictions for informed decisions.
• Practical Solutions: Deploy the model to transform theoretical insights into practical use that addresses business needs.
• Continuous Improvement: Monitor model performance and make adjustments as necessary to maintain effectiveness over time.

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• Model Versioning
• Machine Learning Orchestration
• Machine Learning Observability

10 MLOps Best Practices Every Team Should Be Using

1. Automation: The Heart of MLOps

Automation is the core of every successful MLOps strategy. It transforms manual, error-prone tasks into consistent, repeatable processes that enable teams to deploy models quickly and reliably. Automation means building CI/CD pipelines that manage:

• Model training
• Validation
• Testing
• Deployment

Tools like Jenkins, GitLab CI, Step Functions, SageMaker Pipelines, and AWS CodePipeline allow you to retrain models when new data is ingested, validate performance automatically, and deploy updated models—all without human intervention.

The Power of Automated Retraining

This level of automation is compelling in environments where real-time data flows continuously. For example, e-commerce companies often automate their recommendation engines to retrain nightly, reflecting the most recent user behavior and inventory changes. The result is operational efficiency and consistently high-performing models aligned with user expectations.

The Backbone of ML Operations

Over time, automated pipelines become the backbone of model operations, reducing technical debt and freeing teams to focus on experimentation and strategic improvements. Automation enables seamless collaboration between data scientists, ML engineers, and infrastructure teams when integrated with broader DevOps workflows.

Automation also reduces the risk of deployment bottlenecks and human error, helping organizations maintain uptime and meet compliance targets even during frequent releases.

2. Versioning: Keeping Track of Your Models

Version control is well established in software engineering, but in machine learning, the complexity increases. ML projects manage not only code, but also:

• Datasets
• Hyperparameters
• Configurations
• Model weights
• Experiment results

The Pitfalls of Poor Versioning

Proper versioning allows teams to trace back how a particular result was produced. Without it, debugging is:

• Nearly impossible
• Collaboration becomes messy
• Compliance reporting breaks down

Modern Tools for ML Versioning

Modern tools like DVC, Git LFS, SageMaker Model Registry, and MLflow support comprehensive version tracking across different elements of the ML workflow. These systems enhance transparency and allow:

• Benchmarking model iterations
• Documenting experiments
• Streamlining collaboration across large asynchronous teams

Ensuring Reproducibility in ML

By aligning code and data versioning, teams can run meaningful comparisons, optimize performance, and maintain reproducibility in complex ML environments. This is especially critical when working with regulated data or auditing high-stakes models in sectors like finance and healthcare.

Versioning also helps teams preserve historical context. It allows researchers and engineers to revisit old models, analyze why specific versions worked better, and confidently roll back in case of production failures.

3. Testing: Validating ML Model Performance

Testing is essential to building trustworthy ML systems. However, there are unique challenges: model behavior can shift depending on the data, and there are no "hard rules" like in traditional programming. MLOps testing includes:

• Validating code logic
• Data integrity
• Model outputs

Robust Testing for ML Pipelines

It also spans regression testing, drift detection, and fairness audits. The more robust your test suite, the more resilient your ML pipeline. Teams that operationalize testing are better equipped to handle the uncertainties of real-world data. Structured test frameworks, continuous evaluation pipelines, and alerting systems help catch problems early, before they affect end users.

Strategic Testing for Model Reliability

A thoughtful testing strategy reinforces model reliability and helps maintain performance as systems scale or evolve. When models undergo repeated retraining cycles, testing assures that improvements don’t introduce new vulnerabilities. Organizations can also simulate production environments in staging to evaluate model behavior under real-world constraints, improving confidence before each deployment.

4. Reproducibility: Ensuring Consistent Results

Once a model is deployed, continuous monitoring becomes critical to maintaining its performance and reliability. Production environments are dynamic, and data can shift rapidly. Monitoring tools such as Prometheus, Grafana, SageMaker Model Monitor, and CloudWatch enable real-time tracking of:

• Prediction accuracy
• Latency
• Drift
• User impact

Monitoring for Continuous Improvement

Automated alerts can trigger retraining or rollback workflows when performance degrades or anomalies are detected. Beyond detection, monitoring creates a feedback loop that informs data collection, model tuning, and prioritization of development work. It ensures models are accurate at launch and remain valuable over time.

The Business Value of Comprehensive Monitoring

Comprehensive monitoring protects business outcomes and builds trust in AI-driven decision-making. It also lays the groundwork for continuous learning systems that evolve with user behavior and operational conditions. As ML becomes embedded in business-critical applications, robust monitoring is key to aligning model behavior with:

• Enterprise SLAs
• Customer expectations

6. Data Validation: Ensuring Quality Inputs

Quality data is the backbone of machine learning. Data validation ensures that models are only trained and tested on clean, reliable inputs. Typical forms of validation include:

Reproducibility is the ability to recreate the same results using the same data, code, and configuration. It’s essential for debugging, compliance, and scaling ML efforts across teams. Achieving reproducibility requires complete transparency of each pipeline step. That includes:

• Preprocessing code
• Feature engineering
• Model configurations
• Random seeds
• Runtime environments

Tools and Benefits of Reproducibility

Docker containers, MLflow tracking, and orchestration tools like Kubeflow Pipelines support this goal. Organizations prioritizing reproducibility often see improvements in onboarding, knowledge transfer, and regulatory readiness. It also empowers teams to confidently build on past work, fostering more innovation and fewer redundant experiments.

Reproducibility for Scaled Experimentation

Reproducibility also supports experimentation at scale, allowing teams to confidently branch, iterate, and compare model variants without losing visibility or control over the evolving development process. The result is a shared source of truth across your ML organization, essential for long-term collaboration and trust.

5. Monitoring: Keeping a Steady Eye on Model Performance

• Schema checks
• Null value scans
• Range validations

More advanced systems can detect statistical outliers or shifts in data distributions.

Streamlining Data Validation with Modern Tools

Tools like Great Expectations and built-in validators in Vertex AI and SageMaker streamline this process. By catching issues upstream, organizations reduce rework and improve model stability. Continuous data validation helps maintain trust across data pipelines, especially in high-velocity environments where minor errors propagate quickly.

As data volume and variety grow, scalable validation becomes necessary for ensuring:

• Model robustness
• Model accuracy

Preventing Quality Issues with Data Validation

Teams can also apply validation to unstructured data like images or text using custom rules and anomaly detectors. Integrating validation into data engineering workflows ensures that only trusted data reaches downstream ML applications, preventing quality issues before they impact production.

7. Tracking: Making Sense of the ML Lifecycle

Tracking every aspect of the ML lifecycle, from experiments to deployments, is critical for organizational memory and performance improvement. Experiment tracking platforms like Neptune.ai and MLflow allow teams to log:

• Hyperparameters
• Metrics
• Artifacts
• Results

Over time, this builds a searchable knowledge base of what worked and what didn’t, helping teams avoid redundant work.

The Value of Standardized Tracking

Tracking enables benchmarking across different model versions, simplifies review processes, and streamlines stakeholder reporting. It’s a cornerstone of operational maturity in ML. When tracking is standardized, it improves transparency, supports collaboration, and accelerates iteration across all ML stakeholders.

Scaling ML with Effective Tracking

Tracking strengthens the foundation for adequate documentation, handoffs, and team continuity, critical for scaling ML efforts within growing organizations. Strategic tracking practices help translate technical experimentation into business insight, keeping leadership aligned with ML progress and potential impact.

8. Security and Compliance: Protecting Your Models

MLOps workflows must account for security and governance from the start. With increasing scrutiny around AI systems, teams must ensure models are protected from data breaches and comply with industry regulations. Security includes:

• Data encryption
• Access control
• Audit logging

Traceability for ML Compliance and Trust

Compliance requires traceability and documentation around data handling, decision-making, and model evolution. Embedding these considerations early helps avoid costly rework and accelerates approval for production deployment. It also builds confidence among stakeholders, customers, and auditors.

Enabling ML Scale Through Security & Compliance

A robust security and compliance infrastructure gives ML initiatives the green light to scale responsibly in sensitive or highly regulated environments. Aligning with standards like ISO 27001, SOC 2, HIPAA, and GDPR is necessary for AI maturity. ML teams proactively adopting these practices are better positioned to collaborate with legal, risk, and IT counterparts, building trust across the enterprise.

9. Collaboration and Communication: Breaking Down Silos

MLOps is inherently cross-functional. Collaboration across engineering, data science, operations, and business teams is vital to building models that perform and deliver real value. Shared documentation, integrated dashboards, and clear ownership models foster better handoffs and faster feedback loops.

Visualizing ML Workflows for Enhanced Coordination

Visual tools, like project timelines and model flowcharts, make it easier to coordinate across roles. The more collaborative the workflow, the more resilient and aligned the ML output. Strong communication:

• Prevents duplication
• Reduces rework
• Keeps the focus on business outcomes

Aligning Strategy and Customer Needs

By embedding collaboration into tooling and process design, organizations can ensure that ML efforts align with strategic priorities and customer needs. Effective collaboration supports model explainability and stakeholder buy-in, increasing trust in AI outcomes. Cross-functional syncs, transparent goals, and shared performance metrics turn ML from an isolated practice into a strategic lever across departments.

10. Quality Assurance: Ensuring Reliable, Ethical Models

Quality assurance ensures that models are high-performing but also robust, ethical, and reliable. QA in ML goes beyond metrics, including:

• Manual reviews
• Adversarial testing
• Fairness assessments
• Domain expert input

QA as a Formal Deployment Step

Instituting QA as a formal step before deployment reduces the likelihood of unexpected behavior in production. It also signals organizational maturity and a commitment to responsible AI practices. QA is where technical excellence meets business alignment. It ensures that your models accurately reflect your:

• Brand
• Values
• Customer standards

QA as a Shared Organizational Responsibility

Treating QA as a shared responsibility across stakeholders builds organizational confidence in the integrity and impact of ML models. Over time, a strong QA program becomes a competitive differentiator in industries where accuracy, fairness, and transparency are mission-critical. QA doesn’t end at launch.

Post-deployment reviews, monitoring audits, and cross-team retrospectives help extend QA practices across the model lifecycle.

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