A Machine Learning Processes Intro: 7 Steps to an Efficient Workflow

We demystify the machine learning process for business leaders and IT professionals by providing actionable insights into the seven core steps of ML implementation while addressing modern challenges like AI governance, ethical considerations, and ROI measurement.

In brief:

• Machine learning processes offer a structured and repeatable workflow that enables organizations to transition from experimentation to effective implementation.
• Machine learning empowers computers to learn from data without explicit programming, a concept that has gained new power through cloud technologies and AI tools.
• Success in machine learning requires advanced tools and strong governance with clear alignment to business objectives.
• There are two primary types of machine learning: supervised and unsupervised, each defined by the kind of data and algorithms it uses.

Are you excited about artificial intelligence and machine learning? Machine learning (ML) and artificial intelligence (AI) continue to provide exciting learning opportunities for technologists like us.

So what is ML exactly? Let’s cover the basics of machine learning processes and what steps make up a workflow.

Why You Need a Clear Machine Learning Process

Machine learning has become a cornerstone of digital transformation, yet many organizations still struggle to move from experimentation to execution. Teams see potential everywhere but lack a repeatable, measurable framework for turning ideas into outcomes.

Machine learning, at its core, gives computers the ability to learn without being explicitly programmed, as defined by computer science pioneer Arthur Samuel in the 1950s. That idea may be decades old, but it’s powering today’s most innovative applications.

ML started in the 1950s and has risen and fallen in and out of fashion over the years. However, ML is in its prime now thanks to the popularity of cloud technologies and AI tools. The cloud enables ML and AI to ingest and compute enormous amounts of data, allowing ML to be more powerful. Additionally, new cloud services allow ML to be much more accessible than previously known.

Machine learning is more of an experimental development process than a traditional software project. You’re forming a hypothesis, testing it, refining it — and sometimes learning that the hypothesis doesn’t hold. That’s still a valuable outcome.

Success in ML doesn’t come from tools alone. It requires structure, governance and alignment with clear business goals. Before diving into workflows, it helps to understand the fundamental types of learning that drive every ML model.

The 2 Types of Machine Learning: Supervised and Unsupervised Learning

There are two basic types of ML: unsupervised and supervised learning.

The difference between supervised and unsupervised machine learning are the types of data they ingest and the algorithms they use.

• Unsupervised learning uses unlabeled data and “self-guided” learning algorithms.
• Supervised learning uses labeled data and defined training machine learning algorithms.

The primary goals are also different.

• In supervised learning, the primary goal is predictive analytics.
• Unsupervised learning focuses on finding data patterns.

When we think about predicting outcomes with machine learning, we typically refer to supervised learning — for example, classifying fraudulent transactions or forecasting customer churn. Unsupervised learning, on the other hand, helps uncover hidden relationships or clusters in data, such as grouping customers by behavior or detecting anomalies.

Both forms of learning form the foundation of AI systems. Knowing which approach fits your business problem determines everything that follows — from algorithm selection to data requirements.

With that foundation in mind, let’s move from theory to execution: the practical workflow for putting machine learning to work.

The 7 Steps to Machine Learning Workflows

Every effective ML initiative follows a structured, iterative process. Each step includes stage gates — clear decision points where teams validate data, confirm objectives, and determine whether to continue or pivot.

Below, we walk through the seven essential steps of a modern ML workflow, including what each one involves, the challenges it presents, and how to approach them strategically to deliver lasting business value.

Step 1: Define the Use Case and Business Objectives

Before touching data or selecting a platform, start with a clear question: What are we trying to predict, classify or understand?

Defining the right use case means aligning machine learning with measurable business outcomes, such as reducing claims cycle times, improving customer retention, or forecasting demand more accurately.

A proof-of-value framework begins here. Teams conduct a short outcome workshop to identify key performance indicators (KPIs), success criteria, and data availability. If those elements don’t align, the project pauses.

If you can’t define what you’re trying to accomplish — and how that connects to your organization’s goals — there’s no reason to continue. Training ML models is expensive, so value alignment comes first.

Step 2: Select the Right Tools and Platforms

Choosing the right platform is one of the most critical, and often overlooked, steps in building a sustainable ML workflow. The platform you select determines how your team collects, cleans, and monitors data; how models are trained; and how quickly you can move from proof of concept to production.

Modern cloud-based platforms like Azure AI Studio, Amazon SageMaker, and Google Vertex AI streamline this life cycle by automating repetitive work like data labeling, feature engineering, and pipeline orchestration. Each offers unique advantages depending on your business ecosystem, but there are other tools that may fit your governance model and existing data maturity.

When evaluating platforms, start with your business and technical requirements:

• Consider Integration: How well will the platform fit with your existing data stack and security framework?
• Assess Scalability and MLOps Support: Can it handle future data growth, automation, and retraining needs?
• Evaluate Governance and Compliance Features: These features are especially important if your models will process sensitive or regulated data.
• Ensure Cross-Functional Usability: Data scientists, engineers, and business stakeholders should all be able to collaborate effectively within the same environment.

The “best” platform is the one that balances flexibility with control — allowing teams to experiment freely while maintaining security, transparency and operational oversight.

Step 3: Get and Prepare Your Data

Every successful machine learning model or initiative is built on trustworthy data. The process starts with collecting the right data sources — whether the data is streaming from Internet of Things (IoT) devices, stored in existing databases, or publicly available through platforms like Kaggle.

From there, the real work begins: cleaning, preparing, and manipulating data so it’s usable. Therefore, after we chose our data, we need to clean, prepare, and manipulate the data for machine learning success.

This step often consumes a significant share of project effort. According to the JetBrains Data Science Ecosystem 2023 report, nearly half of data professionals say they spend 30 percent or more of their time preparing, cleaning, and labeling data — underscoring just how foundational this stage is. Clean, well-structured data improves model performance and reduces rework later in the process.

Feature engineering — deciding which attributes actually drive outcomes — can also make or break accuracy. For example, Centric Consulting worked on a project with a restaurant supplier where, by letting the data “speak,” the team discovered that patio seating demand peaked not in summer, as assumed, but in spring and fall. Insights like these only emerge when organizations invest in profiling and exploration instead of relying on assumptions.

Finally, teams split their refined dataset into training and test sets — typically a 70/30 split — to balance learning and validation before modeling begins.

Step 4: Train the ML Model With the Data

This is where machine learning moves from theory to execution. Data connects to algorithms that learn from patterns and generate predictions. These algorithms typically fall into three categories:

• Binary: Classify into two categories.
• Classification: Classify into multiple categories.
• Regression: Predict a numeric outcome.

At Centric, we test a hypothesis, adjust the model, and repeat. Sometimes we learn that the data isn’t strong enough — and that’s progress, too.

Your data scientists may run thousands of training iterations, tuning hyperparameters, swapping learning algorithms, and refining features until the model meets its defined success criteria. Documenting each run enables reproducibility and governance alignment.

Step 5: Test the Model

Once you have trained the model, the next step is to validate its performance using test data that the ML algorithm hasn’t seen before. Testing reveals whether the model generalizes well to new information — and whether it performs consistently across demographic or categorical groups.

When results fall short, you can revisit earlier steps: refining data, adjusting features, or even redefining the hypothesis. This test-retrain cycle ensures that models not only perform accurately but also remain fair and explainable.

Step 6: Deploy, Pilot and Monitor in Production

Once a model performs well in training, it’s time to pilot it in the real world. This phase is where many projects reveal hidden weaknesses or issues.

For example, when Centric partnered with Maas Energy Works, the company wanted to improve how it used operational data to plan for future growth. Our team built a modern analytics platform on Microsoft Azure that allowed Maas Energy Works to monitor, measure, and optimize KPIs in real time.

The project demonstrated how critical it is to pilot models in production — not just in test environments — to ensure data reflects live business conditions and scales accurately.

Running models in production helps identify these issues early. Our approach includes a pilot gate, ensuring models are tested under real-world conditions before full deployment.

After deployment, continuous monitoring becomes essential. Over time, model drift — the gradual loss of accuracy as customer behavior or external conditions change — can degrade results. Recent research found that over 90 percent of ML models experience drift during production life cycles. Regular retraining and governance checkpoints keep predictions reliable and trustworthy.

Step 7: Govern and Measure ROI

Governance isn’t an afterthought — it’s built into every stage. Centric follows the NIST AI Risk Management Framework, which helps organizations define policies for training data selection, validation, monitoring, and retraining. I call this “just-in-time governance.” Apply the right guardrails at the right moments without overcomplicating the process.

Yet governance remains a challenge across industries. The Responsible AI Benchmark Report 2024 found that while 81 percent of companies have AI use cases in production, only 15 percent consider their governance efforts very effective.

Building a strong AI governance foundation is becoming a competitive differentiator.

Measuring return on investment (ROI) also requires focusing on business impact, not just technical metrics. Executives care less about F-scores and more about outcomes. For example, in the insurance industry:

• Did underwriting accuracy improve?
• Did claims resolution time decrease?
• Did fraud detection rates increase?

Leaders want to see how prediction accuracy translates into better decisions. A 10 percent improvement in risk prediction might drive a 0.5 percent lift in profitability. That’s what matters.

How Machine Learning Processes Enable AI Success

Machine learning doesn’t operate in isolation. It’s the foundation that powers modern AI solutions. Machine learning creates the predictive foundation. AI connects the dots and acts on those insights.

For enterprises, AI success depends on this same synergy: well-trained ML models integrated into real-world systems that make autonomous or assistive decisions. By nailing the ML workflow, organizations can confidently scale to AI initiatives that improve customer engagement, streamline operations, and unlock new business models.

From Experimentation to Value With Machine Learning Processes

Machine learning’s true value is in how consistently those models create insight, efficiency, and new opportunities. When organizations embed ML into decision-making and treat it as an enterprise capability, innovation stops being a side project and starts driving measurable results.

As the foundation of AI and the bridge to the next generation of intelligent business systems, the right machine learning framework can help you gain not only an opportunity to learn, but also a chance to lead.

Get specialized guidance to build and optimize ML models for predictive analytics and data-driven decision-making from our machine learning consulting team. Contact us today to learn more. Let’s talk