What Is Machine Learning Engineering?

Machine learning engineering is the use of scientific principles, tools, and techniques to design and build complex computational systems. From data collection to model training, machine learning engineering delivers working machine learning models that can serve end users.

Data analysts are generally interested in understanding and framing business problems.

They build models for solving these problems and evaluate them in a limited development environment. Machine learning engineers are responsible for taking these models and deploying them to a real production environment, and ensuring their effectiveness and resilience.

In addition, they are responsible for making production models stable, maintainable, and easily available for all relevant use cases. Machine learning engineering encompasses all the activities that allow ML algorithms to be implemented as part of an effective production system.

This is part of an extensive series of guides about machine learning.

Machine Learning Engineering Phases

Here are the typical phases of machine learning engineering. These phases may be iterative and may involve going back and forth between different steps as necessary to improve the performance of the machine learning system.

Project Prioritization 

This phase involves identifying which problems or opportunities are most important for the organization to address with machine learning, and determining which resources (e.g., data, computational power, personnel) are available to tackle these problems.

The first step is to define the problem that the machine learning model will be used to solve. This involves identifying the objective of the model (e.g., classify images, predict stock prices), the type of machine learning problem (e.g., supervised, unsupervised, reinforcement).

There are several factors that can be considered when prioritizing machine learning models:

• Business value: This may involve assessing the size of the market opportunity, the potential return on investment, and the impact that the model could have on the organization’s bottom line.
• Feasibility: This involves assessing whether the necessary resources (e.g., data, personnel, computational power) are available to build and deploy the model, as well as the complexity and risk associated with the project.
• Data availability and quality: Models that require large amounts of high-quality data may be more time-consuming and expensive to build, and may therefore be lower priority than models that can be trained on smaller or lower-quality datasets.
• Time to value: The time it will take to build and deploy the machine learning model is also an important factor to consider when prioritizing models. Models that can be deployed quickly may be more attractive because they can generate value more quickly.
• Alignment with organizational goals: Models that support strategic initiatives or that address pressing business needs may be given higher priority.

Data Collection and Preparation

This phase involves gathering and cleaning the data that will be used to train machine learning models. This may involve collecting data from a variety of sources, such as databases, sensors, or web scraping. It may also involve preprocessing the data to ensure that it is in a suitable format for model training, such as converting data into numerical format or handling missing values.

Collecting good data is essential for training effective machine learning models:

• Choose the right data sources: There are many different sources of data that can be used to train machine learning models, including databases, sensors, and web scraping. It is important to choose data sources that are reliable, accurate, and relevant to the problem at hand.
• Collect a diverse and representative sample: This is important to ensure that the machine learning model generalizes well to new, unseen examples. It may involve sampling from different geographic regions, time periods, or demographic groups to ensure that the data is representative of the real-world population.
• Ensure data quality: Check for and address issues such as missing values, duplicates, and outliers in the data.

Feature Engineering

This involves selecting and creating the input features (also known as “predictors” or “covariates”) that will be used to train the machine learning models. Feature engineering involves understanding the problem domain and selecting the most relevant and informative features to include in the model. It may also involve creating new features by combining or transforming existing features.

Model Training and Evaluation

This involves selecting and training machine learning algorithms on the prepared data, and evaluating their performance using metrics such as accuracy or F1 score. This stage requires supervised learning using the training data set. 

Once the model is trained, it is important to evaluate its performance on a separate dataset known as the test set. This helps to ensure that the model has not overfitted to the training data and is able to generalize to new, unseen examples.

Model Deployment

Once the model is performing satisfactorily on the test set, it can be deployed in a production environment. This may involve integrating the model into a larger system or product, and setting up monitoring and maintenance processes to ensure that the model continues to perform well and maintain its accuracy over time.

There are two main approaches to deploying an ML model to production:

• Static deployment involves deploying a machine learning model that is fixed and does not change over time. The model is trained offline, and the trained model is then deployed to a production environment where it is used to make predictions or decisions. This approach is suitable for problems where the underlying data distribution is relatively stable and the model’s performance does not degrade significantly over time.
• Dynamic deployment involves a machine learning model that is updated or retrained on a regular basis. This approach is suitable for problems where the data distribution is changing over time or where the model’s performance is expected to degrade over time. In dynamic deployment, the model is trained and deployed in an ongoing loop, with new data being used to update or retrain the model on a regular basis.

Machine Learning Operations (MLOps): ML Engineering for Production Models

MLOps stands for machine learning operations. It is a key capability in machine learning engineering, which focuses on simplifying the process of moving machine learning models into production and maintaining and monitoring them. MLOps is often a collaborative function of data scientists, DevOps engineers, and IT.

MLOps is a methodology that helps create and improve the quality of machine learning and AI solutions. By adopting an MLOps approach, data scientists and machine learning engineers work together by implementing continuous integration and deployment (CI/CD) practices. This provides monitoring, validation, and governance for ML models, and makes it possible to accelerate model development and deployment.

Building machine learning systems is hard. The machine learning lifecycle consists of many complex elements such as data collection, data preparation, model training, model tuning, model deployment, model monitoring, and explainability. It also requires collaboration and handoffs between teams from data engineering to data science to ML. 

It takes serious work to keep all these processes in sync and working together. But it is worthwhile – implementing MLOps enables experimentation, iteration, and continuous improvement of the machine learning lifecycle.

Learn more in our detailed guide to MLOps

What Is a Machine Learning Engineer?

A machine learning engineer (ML engineer) is an information technology (IT) professional who builds and maintains an organization’s machine learning algorithms and artificial intelligence systems.

An important goal of the ML engineer’s job is to make it easy for data scientists to access and derive value from very large data sets.

In large enterprises, ML engineers need background skills including those of a data analyst and a data scientist with an advanced degree.

What Does a Machine Learning Engineer Do?

Machine learning engineers are highly skilled programmers responsible for designing machine learning systems. This includes evaluating and cleaning data, running tests and experiments, monitoring and optimizing processes to help develop powerful machine learning systems.

While specific responsibilities will vary depending on the size of the organization and the overall data science team, a typical machine learning engineer job description includes:

• Researching, designing, and developing machine learning systems and solutions.
• Building data science prototypes and evolving them.
• Finding and selecting a suitable dataset before performing data collection and data modeling.
• Performing statistical analysis and using the results to improve a model.
• Training and retraining ML systems and models.
• Identifying differences in data distribution that can affect model performance under real-world conditions.
• Analyzing ML algorithm use cases and ranking them according to their probability of success.
• Identifying when research findings can be applied to business decisions.
• Tracking and leveraging updates to existing ML frameworks and libraries.
• Achieving assurance through data quality verification and data cleansing.

A Machine Learning Engineer’s Role in Model Monitoring

ML engineers manage the MLOps pipeline, which includes components for training, versioning, and model serving.

ML engineers also find ways to monitor production models to ensure that the predictions provided are of expected quality and that the service itself is always available. Monitoring is often associated with data engineering, because it can help identify whether real-world data has changed since the model was last trained, a phenomenon known as data drift.

Learn more in our detailed guides to:

• Machine learning engineer salary (coming soon)

• How to become a machine learning engineer (coming soon)

Machine Learning Engineer vs. Data Scientist 

The roles of machine learning engineers and data scientists are similar. Both jobs tend to process large amounts of data, require specific qualifications, and tend to use similar techniques. However, ML engineers focus on creating and managing AI systems and predictive models, while data scientists derive meaningful insights from large datasets.

Data scientists are responsible for collecting, analyzing, and interpreting large amounts of data. Use large amounts of data to make hypotheses, make inferences, and analyze customer and market trends. This role requires advanced analytical skills such as predictive modeling and machine learning skills, as well as skills in mathematics, statistics, and data visualization.

Other essential responsibilities of a data scientist include discovering patterns, trends, and relationships in data sets using various types of analysis and reporting tools. Machine learning engineers and data scientists work closely together, but both require good data management skills.

Machine Learning Engineering with Aporia

When a machine learning model starts interacting with the real world, making real predictions for real people and businesses, there are various production issues that can send your model spiraling out of control. 

Aporia’s ML observability is an ideal partner for ML engineers to ensure ML models are working as intended. Our platform fits naturally into your existing ML stack and seamlessly integrates with your existing ML infrastructure in minutes. Aporia offers data science and ML teams key features and tools to ensure production models perform at their best: 

Production Visibility

• Single pane of glass visibility into all production models. Custom dashboards that can be understood and accessed by all relevant stakeholders.
• Track model performance and health in one place. 
• A centralized hub for all your models in production.
• Custom metrics and widgets to ensure you see everything you need.

ML Monitoring

• Start monitoring in minutes.
• Instant alerts and advanced workflows trigger. 
• Custom monitors to detect data drift, model degradation, performance, etc.
• Track relevant custom metrics to ensure your model is drift-free and performance is driving value. 
• Choose from our automated monitors or get hands-on with our code-based monitor options. 

Explainable AI

• Get human readable insight into your model predictions. 
• Simulate ‘What if?’ situations. Play with different features and find how they impact predictions.
• Gain valuable insights to optimize model performance.
• Communicate predictions to relevant stakeholders and customers.

Root Cause Investigation

• Slice and dice model performance, data segments, data stats, or distribution.
• Identify and debug issues.
• Explore and understand connections in your data.

To get a hands-on feel for Aporia’s advanced model monitoring and deep visualization tools, we recommend:

Book a demo to get a guided tour of Aporia’s capabilities, see ML observability in action, and understand how we can help you achieve your ML goals.

See Additional Guides on Key Machine Learning Topics

Together with our content partners, we have authored in-depth guides on several other topics that can also be useful as you explore the world of machine learning.

Auto Image Crop

Authored by Cloudinary

• Auto Image Crop: Use Cases, Features, and Best Practices 
• 5 Ways to Crop Images in HTML/CSS
• Cropping Images in JavaScript

Advanced Threat Protection

Authored by Cynet

• What is Network Analytics? From Detection to Active Prevention
• Threat Detection and Threat Prevention: Tools and Tech
• Social Engineering Prevention

Multi GPU

Authored by Run.AI

• Multi GPU: An In-Depth Look
• TensorFlow Multiple GPU: 5 Strategies and 2 Quick Tutorials
• How to Build Your GPU Cluster: Process and Hardware Options

Gon Rappaport