Today, machine learning plays a critical role in modern business strategy. Organizations increasingly rely on machine learning to accelerate the decision-making process and make smarter choices across numerous activities, from enhancing the customer experience to detecting fraud and forecasting customer demand. However, many organizations are still having difficulty taking the next step after pilot projects or proof of concepts.

The underlying cause for this predicament is not a technological issue; it stems from not having a framework in place. Organizations find it difficult to scale their machine learning projects without an established process for developing those projects.

Enter the machine learning lifecycle. The machine learning lifecycle provides organizations with a methodology for building, deploying, and managing machine learning systems in a consistent, repeatable manner.

What Is the Machine Learning Lifecycle?

The machine learning lifecycle is the complete development process of machine learning solutions from the inception of the project to the final deployment of the solution. It includes all components of a machine learning project from problem identification through maintaining the resulting model once it is put into service.

This is not a linear process; rather, it is an ongoing loop of improving the machine learning model based on new data and changing business environments.

This process keeps machine learning projects continuing to create value for organizations after the resulting model is deployed.

Why the Machine Learning Lifecycle Matters in Enterprises

Enterprises usually have to deal with large amounts of complex systems, data, and a variety of business teams, making it easy for their machine learning projects to stop contributing to their business objectives if not done correctly or with structure.

Having a well-defined life cycle allows them to:

• turn business issues into measurable machine learning goals

• Increase model accuracy and reliability

• Decrease the percentage of model failures post-production

• Ensure a high level of data quality and a high degree of data consistency

• Maintain a high level of alignment between technical team/Business stakeholder

Creating business value at scale is where a lot of the organization’s machine learning strengths are achieved through a transition from experimentation to production.

Key Stages of the Machine Learning Lifecycle

There are four main stages in the Machine Learning Lifecycle:

1. Problem Understanding and Business Planning

All machine learning projects should start with an understanding of what they want to accomplish by clearly identifying the problems. This is critical to the overall project since it sets the path and focus for the project.

The team will need to address questions such as what they are trying to solve and what the problem means to them as a team; once they have answered those questions, they will define how they will measure success.

At this point in time, businesses define:

• Business objectives/expected outcomes

• KPIs for assessing success

• Data that will be available and any limits on that data

• Cost, time, and resources available for the project

• The expectations of stakeholders

Inadequate definition of a problem will result in even the greatest models not producing meaningful output.

2. Data Collection and Data Preparation

Data serves as the primary support for any machine learning solution; therefore, no model will do well if the data is of low quality.

At this stage, businesses gather data from a variety of different sources available to them, including internal databases, customer systems, sensors, applications, or third-party platforms.

After the data has been gathered, it must be cleaned and prepared. This is a lengthy and tedious task, yet critical to the success of developing a model.

Data preparation includes but is not limited to:

• Removing missing or duplicate records

• Correcting inconsistent/inaccurate values

• Transforming the data to be usable in a model

• Selecting features that will be useful for training

• Structuring data for analysis

Properly prepared data will increase the accuracy of a model and reduce the likelihood of errors later in the lifecycle of a model.

3. Exploratory Data Analysis

Before building a model, teams will typically examine data to learn about it.

This analysis will assist teams in discovering:

• Hidden correlations among independent variables

• How the data is distributed

• Outliers that could affect results

• Issues with the quality of the data itself

By performing exploratory analysis, teams can ensure that they take the appropriate path forward prior to investing time into model development, thus reducing their chances of developing an erroneous model.

4. Model Building and Training

After acquiring and understanding the data, you can begin constructing the machine learning model. This is done by selecting and training algorithms on historical data to help them learn from patterns so that they can forecast on new data.

There are diverse methods and algorithms depending on what you are trying to predict. Classification models are typically used for classification tasks, whereas regression models are typically used to predict numerical values.

During this process, the team will typically:

• Experiment with multiple algorithms

• Adjust model parameters

• Compare the performance between various models

• Figure out which model works best

As you can tell, this is an iterative procedure that requires lots of trial and error.

5. Model Evaluation and Validation

After creating the model, you should evaluate how well the model performs on samples of unseen data. This is a critical step because models may perform well with the samples of historical data, yet may not work well in the actual operations.

The evaluations occur based on performance metrics through measures such as:

• Accuracy of predictions

• Precision and recall

• Error rate

• F1 score in classification problems

If the new model performed below your expected threshold, then you will go back and revisit the previous stages, such as Model Selection or Data Preparation. This continuous feedback loop is an important aspect of the Machine Learning Lifecycle.

6. Model Deployment into Production

After a model has passed all tests, it is then put into a production environment where it can interact with real-time or live data.

The deployment phase is the most critical because this is when machine learning and business applications are connected.

Cloud platforms, such as Amazon Web Services or Microsoft Azure, and containerized environments, like Docker, are typically used by companies to deploy scalable and secure models.

Integration with existing systems is also an important task during this phase since the model needs to work seamlessly with business applications without disrupting their operation in any way.

7. Monitoring and Performance Tracking

Once models are in production, they need to have their performance continuously monitored. This is necessary because the data that models work on in the real world changes over time, which results in degradation of model performance.

The degradation of model performance due to changes in the data is often referred to as data drift.

The monitoring of models is used to keep track of the following:

• Accuracy of Predictions over Time

• Performance and Latency (Response Time) of the System

• Changes and Anomalies in Data

• Business Impacts from Model Outputs

Without monitoring, even a high-quality model can become outdated and unusable.

8. Model Retraining and Improvement

There is no doubt that machine learning is a dynamic environment. Therefore, models need to be periodically retrained so they remain current.

Whenever there is a decline in the performance of a model or when new data becomes available, the team will retrain the model using the new dataset to ensure that predictions are still accurate and in line with current circumstances.

This process is where the feedback loop is established back to the earlier stages of the modelling process.

Common Challenges in the Machine Learning Lifecycle

Most organizations need to employ a structured approach, but issues they may run into include:

• Poor quality or unstructured data

• Lack of coordination between technical and business teams

• Difficulty scaling models across systems

• Integration issues with legacy infrastructure

• Limited machine learning expertise in teams

These issues can create delays in adoption and decrease ROI if left unaddressed.

Best Practices for a Successful Machine Learning Lifecycle

To increase machine learning implementation success rates, organizations should consider the following:

• Clearly defined business goals should be established. The goal of machine learning should be to solve a business issue, not just a technical one.

• An organization should give the highest priority to data quality. Clean and structured data can produce better results than complex algorithms applied to dirty or unstructured data.

• The organization must invest in scalable infrastructure. In most instances, cloud-based systems are ideal for handling larger volume workloads than traditional systems.

• Collaboration must occur between personnel. Data scientists, engineers, and business personnel must collaborate during the entire lifecycle.

• From the start, a strategy for continuous monitoring and improvement should be included.

Final Thoughts

A framework to help establish ongoing relationships within an organization and support the successful adoption of your organization’s initiatives is to establish a machine learning lifecycle. The machine learning lifecycle provides organizations with the tools that will allow them to create successful long-term relationships with their projects.

By having a structured framework for machine learning, organizations are able to minimize their risks, increase the accuracy of their predictions, and provide valuable business results. Additionally, by using a coordinated approach to machine learning, organizations will be able to transition to scalable and production-ready solutions instead of operating in silos.

Ultimately, organizations that have mastered this framework will have a competitive edge over others who are still developing their machine learning capabilities.

Author Bio: Sarah Lewis is an IT Project Manager at Binmile Technologies, a Machine Learning Development Services in the USA. She has more than 10 years of experience in the IT sector. She likes to write technical articles in her free time.