What is Machine Learning Engineering?
Machine learning engineering (MLE) involves the use of various skills and technologies—including machine learning techniques, tools, and principles, and software engineering—for the purpose of designing and building complex computing systems.
MLE covers the entire data science pipeline, including data collection, training models, and releasing the model in production. A machine learning engineer is responsible for the entire process and may perform several tasks.
A machine learning engineer is responsible for sourcing data from multiple systems and locations, preprocessing data, programming features. Additionally, the ML engineer needs to train the model effectively to coexist with relevant production processes in a stable, easily accessible, and stable manner.
This article will explain five phases of the machine learning engineering process, and help you understand how MLE will fit into your organization – roles and responsibilities, prioritization of projects, and how machine learning operations and automation is transforming the field.
In this article, you will learn:
- 5 Phases of Machine Learning Engineering
- The Machine Learning Engineer Role
- Prioritization of Machine Learning Projects
- What is Machine Learning Operations?
- Machine Learning Automation
- Optimizing Your Machine Learning Infrastructure with Run.ai
Here are the main stages in a machine learning pipeline, and the machine learning engineering activities involved in each one.
A machine learning model requires massive amounts of data, which helps the model learn how to perform its purpose. Before it can be used, big data needs to be collected and usually also prepared.
Data collection is the process of aggregating data from multiple sources. The data you collect needs to be sizable, accessible, understandable, reliable, and usable.
Data preparation, or data preprocessing, is the process of transforming raw data into usable information.
There are several challenges you might encounter when handling data. For example, high costs, bias, and low predictive power.
In general, good data has consistent labels and can reflect the real inputs the model is expected to work with in production. If you are using interaction data, you also need to make sure it comes with context, including the action and outcome of the interaction.
Feature engineering is the process of conceptually and programmatically transforming your raw example into a feature vector.
You first need to conceptualize the feature and then write a code that can transform your raw example into a feature. After creating several features, you need to scale and store them and document all features in feature stores or schema files. Additionally, you should make sure that all code, models, and training data are in sync.
The next step in the process is training your ML model. There are several techniques you can use, including supervised and unsupervised learning.
Supervised learning involves the use of labeled datasets to train your model to classify data and predict outcomes, whereas unsupervised learning involves the use of unlabeled data.
The modeling process requires the use of algorithms. You can use your own algorithm or choose the relevant algorithms from an open source library like scikit-learn. Once you choose an algorithm, you can start testing different combinations of hyperparameters.
It is critical to evaluate a machine learning model before and after running in production. You can evaluate a model offline, after the training phase is complete. Offline evaluation is based on historical data. Alternatively, you can leverage online model evaluation to test and compare models running in production.
Ideally, model evaluation should be performed on a continuous basis. This process should help you gain several insights, including:
- Evaluating model performance before deploying in production
- Estimating the possible legal risks of deploying in production
- Monitoring the performance of the model after it is running in a production environment
Here are several model deployment options:
- Static deployment—enables you to maintain user privacy, achieve quick execution, and call the model while users are offline. However, upgrading the model usually requires upgrading the entire application.
- Dynamic deployment on user devices—enables the model to quickly answer device-based user calls. However, delivering updates to users can be complex. It is also difficult to monitor the model when it is deployed on user devices.
- Dynamic deployment on a server—enables you to deploy on a virtual machine (VM), in a container, or leverage serverless services.
- Model streaming—lets you register all models in a stream-processing engine. Alternatively, you can package the model as an application, which is based on a stream-processing library.
Related content: read our guide to machine learning workflow
Machine learning engineers manage the entire data science pipeline, including sourcing and preparing data, building and training models, and deploying models.
Here are the main tasks performed by machine learning engineers:
- Data ingestion and preparation—machine learning engineers need to source data and then prepare it for ingestion. This is often a complex and time consuming task, because data is often sourced (or streamed in real-time) from multiple sources. However, it is critical to ensure data is properly—but automatically—processed, clearn, and prepared.
- Deployment—once the model is ready, machine learning engineers deploy it in production. Initially, you deploy a prototype and test it, and then gradually scale it out so it could serve real users. This process involves running the model on powerful hardware and providing access to the model through APIs, as well as releasing updates.
Machine learning engineers usually perform the following tasks:
- Analyze big datasets and then determine the best method to prepare the data for analysis.
- Collaborate with other data scientists and build effective data pipelines.
- Build the infrastructure required to deploy a machine learning model in production.
- Manage, maintain, scale, and improve machine learning models already running in production environments.
- Work with common ML algorithms and relevant software libraries.
- Optimize and tweak ML models according to how they behave in production.
- Communicate with relevant stakeholders and key users to understand business requirements, and also clearly explain the capabilities of the ML model.
- Provide technical support to data and product teams, helping relevant parties use and understand machine learning systems and datasets.
There are many aspects to consider when prioritizing machine learning projects. Perhaps the most critical aspects are the time and cost involved, and whether you can use these resources to build a model that meets the basic requirements.
Make sure the ML model you release into production is designed to meet the following requirements:
- The ML model respects the specifications of input and output, as well as the performance requirements.
- The ML model is designed to benefit both the organization and the end user.
- The ML model is scientifically rigorous.
In addition to the above requirements, you also need to make sure that the machine learning project you prioritize has the greatest impact on your business at the lowest possible cost. Here are several considerations that can help you assess this aspect:
- If a ML project can replace a complex and time consuming component and increase efficiency, performance, or sales, it can be defined as having “great impact”.
- ML projects are expensive. Sometimes lower costs translate into imperfect predictions. Carefully assess and define the budget before starting work on the project. When estimating costs, account for the difficulty of the problem you need to solve, the amount of data needed and its cost, and the required model performance quality.
Note that machine learning projects are nonlinear. At first, errors decrease quickly and then the progress starts slowing down. If you need to quickly deploy the solution in production, machine learning may not be the right technology for your current needs.
You can track the progress of your model by logging all activities and monitoring the time each activity takes. You can use this data to continuously improve the model while also estimating the complexity of similar future projects.
Machine learning engineering does not operate in a vacuum. ML engineers need to be aware of new organizational patterns that are changing how machine learning projects are managed and operated, and represent compelling benefits for the organization.
Machine Learning Operations (MLOps) is a new discipline that aims to organize the entire ML lifecycle. It promotes workflows and processes that can improve communication between data scientists and DevOps teams, and accelerate time to market for machine learning applications. It is being supported by frameworks like MLFlow and Kubeflow.
MLOps starts with the modeling and data collection activities of the data science team, taking into account the business goals of the ML application, and the governance and compliance issues to consider.
The end goal is to enable seamless deployment and continuous monitoring of ML systems, through collaboration between data science, machine learning engineers, and operations teams. When performance issues occur, or new production data is added, data science teams can tune models, they can be easily pushed to production, and results can be evaluated.
When working in an MLOps approach, machine learning engineers should focus on:
- Reducing the time and complexity of moving models to production
- Improving the use of version control, tracking, and monitoring, to ease ongoing development
- Monitoring ML infrastructure and compute costs at every stage from development to production
- Standardizing ML processes in line with the organization’s governance policies and compliance obligations
Learn more in our detailed guide to machine learning operations
Automation of machine learning processes is the next step forward for many data science organizations. Machine learning engineers play a key role in these automation efforts.
Machine learning automation makes machine learning engineering processes faster, more efficient, and easier to operate. Without machine learning automation, a new model can take months from data preparation and training to actual deployment.
Automated Machine Learning (AutoML) is an approach that automates many of the time-consuming and repetitive tasks associated with model development. It is designed to improve productivity for data scientists, analysts, and developers, and to make machine learning easier for those who are not data and machine learning experts.
AutoML has other important benefits:
- It improves model accuracy and insight, by ensuring machine learning processes are developed according to data science best practices. Ad-hoc machine learning processes may not always incorporate best practices.
- It improves security, by enforcing secure practices in treatment of data, training and inference processes.
Machine learning automation simplifies the input requirements for model development and makes it available to industries where machine learning was not previously available. This creates opportunities for innovation, strengthens market competitiveness and promotes development.
Related content: learn more in our detailed guide to machine learning automation
Run:AI automates resource management and orchestration for machine learning infrastructure. With Run:AI, you can automatically run as many compute intensive experiments as needed.
Here are some of the capabilities you gain when using Run:AI:
- Advanced visibility—create an efficient pipeline of resource sharing by pooling GPU compute resources.
- No more bottlenecks—you can set up guaranteed quotas of GPU resources, to avoid bottlenecks and optimize billing.
- A higher level of control—Run:AI enables you to dynamically change resource allocation, ensuring each job gets the resources it needs at any given time.
Run:AI simplifies machine learning infrastructure pipelines, helping data scientists accelerate their productivity and the quality of their models.
Learn more about the Run.ai GPU virtualization platform.
Learn More About Machine Learning Engineering
There’s a lot more to learn about machine learning operations. To continue your research, take a look at the rest of our guides on this topic:
Machine Learning Infrastructure: Components of Effective Pipelines
Machine learning infrastructure includes the processes, resources, and tooling needed to develop, train, and operate machine learning models. It is sometimes referred to as AI infrastructure or a component of MLOps.
ML infrastructure supports every stage of machine learning workflows. It enables engineers, data scientists, and DevOps teams to manage and operate the required resources and processes.
Machine Learning Automation: Speeding Up the Data Science Pipeline
Machine learning automation enables data scientists to automate the machine learning workflow. Without automation, the ML workflow can take a very long time, even months. This includes data preparation, training, until actual deployment.
Machine Learning Workflow: Streamlining Your ML Pipeline
Machine learning workflows define which stages are implemented during a machine learning project. The common stages include data collection, data pre-processing, building datasets, model training, and deployment to production. You can automate some aspects of the workflow, such as model and feature selection phases, but not all.
Machine Learning Engineers: Shaping the AI Revolution
Machine learning engineering involves using programming, analytics, and data science knowledge to work with a machine learning (ML) model and deliver it as part of a product or directly to end users. Learn about the exciting role of machine learning engineer, responsible for building the infrastructure behind the biggest technical revolution of our times.
Machine Learning Operations
MLOps (Machine Learning Operations) is a relatively new discipline that seeks to systematize the entire ML lifecycle, from science to production. Today, MLOps capabilities are considered a key requirement for Data Science and Machine Learning (DSML) platforms. Learn how Machine Learning Operations came to be a discipline inside many companies and things to consider when deciding if your organization is ready to form an MLOps team.
Read more: Machine Learning Operations