Functions, Users, and Comparative Analysis
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Fundamentals of ML observability
Metrics, feature importance and more
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Introduction and agenda >
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.
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.
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:
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:
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.
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.
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:
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
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.
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:
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:
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.
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:
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.
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
