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Machine Learning Lifecycle: What Every Data Scientist Should Know

There’s no one formula for developing machine learning models, but most ML projects follow a set of standard—and cyclical—steps. 

In this article, we’ll explain what the machine learning lifecycle is, describe how it works, and explain how best to develop ML models from ideation to production.

Table of Contents





What Is the Machine Learning Lifecycle?

The machine learning lifecycle is the cyclical process that most data science and machine learning projects move through. ML projects generally start with planning and proceed to production. Once a model is in production, ML practitioners can evaluate its performance and tweak it when necessary, beginning the cycle over again.

Why Is the Machine Learning Lifecycle Important?

The machine learning lifecycle is important because it helps guide practitioners and reminds them to think about machine learning as an iterative loop rather than a linear process. Models are rarely finished—there is always room for improvement.

Using a cyclical framework for machine learning:

  • Gives practitioners clarity around the process and enables better planning
  • Helps guide and coordinate an ML team’s tasks and activities
  • Prompts ML teams to continue to improve models even after they are in production

Stages in the ML Lifecycle

We think about the machine learning lifecycle as four distinct stages: planning, data preparation, modeling, and production.

1. Planning

Planning is perhaps the most important stage. This is when an ML practitioner carefully thinks about the problem they’re trying to solve and chooses an approach for solving it. Tasks in this stage include:

  • Clearly stating the problem or business objective
  • Designing an approach to solving the problem—including ML if appropriate
  • Determining relevant target variables and feature variables
  • Considering limitations to the project, risks, and contingencies
  • Identifying metrics for success

2. Data

Once there is a plan, the next step is to collect and prepare data for modeling. This is often one of the most time-consuming stages. Tasks in this stage include:

  • Collecting data and merging it into a single database
  • Wrangling the data and cleaning it so it’s ready for modeling
  • Defining an annotation or labeling schema for data and annotating it
  • Augmenting the data if necessary
  • Conducting preliminary and exploratory data analysis to understand the data set

3. Modeling

Once there is a complete and clean set of data, the next step is to train a model. Tasks in this stage include:

  • Selecting the appropriate model type for the problem and data
  • Training the model with a training data set
  • Tracking multiple model iterations or experiments and versioning them
  • Evaluating the performance of the model based on the success metrics identified
  • Choosing the best model to go into production

4. Production

Production is the final step in the process. It’s where the model is integrated into a company’s process and helps to solve the business problem. Tasks in this stage include:

  • Deploying the model into the existing production environment
  • Monitoring model performance to ensure it continues to perform well
  • Adding any additional functionality that is required

What Happens After Production

Once a model is in production, it is monitored to ensure that it continues to perform well. If a model begins to perform poorly, the team can return to the first step in the lifecycle: plan the next iteration of the model, collect and prepare the data, build a revised model, and then put it into production.

Machine Learning Lifecycle vs Software Development Lifecycle

The machine learning lifecycle is similar to the software development lifecycle, but it’s not the same. In many ways, it’s more complicated to build and deploy machine learning models than it is to build and deploy software.

Planning. Software engineers do a requirement analysis, which is similar to machine learning practitioners planning their ML models.

Solution design vs. data collection. The second stage in software development is to design the solutions architecture of the software. In the ML lifecycle, the second step is collecting and wrangling data. Unlike software developers, ML practitioners have to consider their data because the model will ultimately depend on the features of the available data.

Coding vs. modeling. The third stage in software development is coding and testing the software. In the ML lifecycle, the third stage is modeling. These stages are similar—they both involve coding a solution and evaluating the performance of that solution.

Deployment. The fourth stage in both software development and ML is deployment. For software, this stage also includes maintenance. For ML models, this stage includes monitoring the performance of the model over time and tweaking the models.

Data Privacy Concerns During Data Collection

Machine learning requires massive amounts of data that often contain personal, private, or sensitive information. Several laws regulate the collection, storage, and use of such data.

To minimize legal risk, companies should have clear data management policies and should monitor and review their data collection practices. Companies may also benefit from creating a data governance council, made up of a mix of individuals from across the organization, including ML practitioners.

Another way to overcome privacy concerns during data collection is by generating synthetic data. This type of data is derived from a real dataset. It takes the essential characteristics of actual data without the risk of leaking personal information. Different algorithms can be applied to different data types to generate synthetic samples, protecting data privacy and mitigating issues with data scarcity and model robustness. 

Challenges Teams Face in an ML Lifecycle

Building an ML model gets more complex as your data science team expands. And deploying ML models typically requires coordination with other teams, as well—business analysts, designers, software engineers, and others.

With multiple people working on the same project, you begin to face challenges like:

  • Poor communication
  • Lack of coordination between teams
  • Disorganized file systems and experiments everywhere
  • Confusion about which model versions are the most current or the best

Clearly defining the ML lifecycle helps standardize the process within your ML team and other business teams. Collaboration tools that track experiments and enable model versioning can help overcome these challenges.

Best Practices for ML Lifecycle Management: MLOps

What’s the best way to develop and deploy ML models? Using a standardized process of machine learning operations (MLOps). Best practices for machine learning lifecycle management include:

  • Continuous training. Models often suffer from drift over time. Consistently monitoring and retraining deployed models helps ensure they reliably perform well.
  • Automating the lifecycle. Automating aspects of model training, monitoring, and retraining can make it faster to train and deploy new models.
  • Using lifecycle development tools. Tools can track ML experiments and model versions, making it easier to collaborate between teams.

Top Programming Languages for Machine Learning

Machine learning practitioners use several programming languages, but some are much more common than others. The top programming languages for machine learning are:

  • Python
  • R
  • C/C++
  • Java
  • JavaScript
  • Shell
  • Go

Frequently Asked Questions (FAQs)

What’s the difference between the machine learning lifecycle and the traditional software programming lifecycle?

These different lifecycles are similar, but they aren’t the same.

One difference is in the second stage. In traditional software programming, the second step is to design a solution architecture based on the programming requirements. In Machine learning, the second step is more hands-on—data collection, wrangling, and exploratory analysis. In other words, ML practitioners have to prepare their data to ensure their solution fits with the available data.

Is it better to use in-house data or external data for machine learning?

It depends on your problem and what data you have in-house.

One benefit of in-house data is that you know how they were collected and their quality. You also have full control over them. But one drawback is that you may not have all the data that you need in-house.

One benefit of using data from customers, vendors, regulators, or competitors is that they can be added to your in-house data and allow you to build better models. But the drawbacks are that external data can be expensive, may be low quality, and you may be restricted in how you use them.

Why is planning important in the machine learning lifecycle?

Adequate planning is critical because it helps ensure that you understand the problem and build a useful model. Without adequate planning, you are more likely to waste your time and resources.

What are the three main types of ML models?

Three main types of machine learning modes are:

  • Descriptive models: help you understand a data set or what happened in the past
  • Prescriptive: help automate business decisions and processes using data
  • Predictive: help you predict what will happen in the future

ML algorithms can also be separated into three categories with respect to their aims:

  • Supervised learning algorithms: aim to predict an outcome, target, or variable
  • Unsupervised learning algorithms: aim to group data without trying to predict an outcome
  • Reinforcement learning algorithms: aim to train an algorithm to make certain decisions

What is deep learning?

Deep learning is a subset of machine learning that uses a neural network more than three layers deep. It aims to obtain knowledge in a way that is similar to how humans learn.

What are things to consider when creating your own dataset?

The most important things to consider when creating a dataset are:

  • A clear articulation of the problem
  • Collecting the right data for the problem
  • Choosing an appropriate collection method
  • Ensuring data quality
  • Consistent formatting of data

Who is involved in each stage of the machine learning lifecycle?

It depends on the company. Many people may be involved, depending on how the teams are set up.

  • Planning can often include data scientists, data engineers, business analysts, or activation teams (like marketing teams).
  • Data collection and wrangling can include data engineers, database administrators, machine learning engineers, or data architects.
  • Modeling can include machine learning engineers, data scientists, data analysts, or statisticians.
  • Production can include machine learning engineers, MLOps teams, DevOps teams, developers, IT teams, or activation teams.

How can you automate the entire machine learning lifecycle?

Much of the machine learning lifecycle can be automated, although some stages can’t be. For example, the planning stage requires planning and can’t be easily automated. For the stages that can be automated, the best way is to use tools that build-in automation—for example, tools that automatically track experiments or visualize model performance.

What are machine learning platforms and what’s the best one?

Machine learning platforms help you build, train, deploy, and monitor ML models. Comet is one of the top machine learning platforms. It integrates with your existing infrastructure and tools so you can build ML models more efficiently and with less friction.

Why is data preprocessing important?

Data preprocessing helps make data wrangling more efficient. It helps ensure that there aren’t missing or incorrect values and eliminates duplicates and inconsistencies.

Is it better to build or buy an MLOps tool?

It depends on the level of maturity and size of the organization. Smaller enterprises that do not have dedicated resources to build may need to buy an external platform, while larger companies may have the capacity to develop an original tool. But it is our recommendation to do both. Learn more about it in our blog, Managing MLOps: When To Build vs. Buy.

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