A machine learning engineer (ML engineer) is a programmer who designs and builds software that can automate artificial intelligence and machine learning (AI/ML) models.
ML engineers build large-scale systems that take in massive data sets and use them to train algorithms that can learn cognitive tasks and generate useful insights and predictions. These systems are then deployed to production where they can serve real users - this is known as the inference stage.
Machine learning engineers manage the entire data science pipeline, including sourcing and preparing data, building and training models, and deploying models to production.
ML engineers typically work within a data science team, collaborating with data scientists, data analysts, IT experts, DevOps experts, software developers, and data engineers.
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Machine learning engineers have two key roles: feeding data into machine learning models, and deploying these models in production.
Data ingestion and preparation is a complex task. The data might come from a variety of sources, often streaming in real time. It needs to be automatically processed, cleaned and prepared to suit the data format and other requirements of the model.
Deployment involves taking a prototype model in a development environment and scaling it out to serve real users. This may require running the model on more powerful hardware, enabling access to it via APIs, and allowing for updates and re-training of the model using new data.
In order to achieve these and related tasks, machine learning engineers perform the following activities in an organization:
Related content: read our guide to machine learning infrastructure
Here are some of the essential skills required from machine learning engineers:
The machine learning engineer role is new, and there is limited data about the salary range. However, because it is closely related to several well known roles, it is possible to estimate the salary based on these related roles. The following is the US national median salary for 2022 based on data from Robert Half:
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As you start on your machine learning engineering job path, here are a few things that will make you successful at this role.
Machine learning engineers and data scientists, while they work in the same team towards a shared goal, have different roles and responsibilities.
Machine learning engineers build software systems and develop algorithms that can be used to generate business insights. Their main responsibility is to create AI tools and infrastructure enabling machine learning in production and at scale.
Data scientists are responsible for collecting data, analyzing it, and using machine learning algorithms to transform it into a usable form. They identify patterns in data that can help a business make better decisions, or can directly provide value to users.
So while machine learning engineers are mainly responsible for the “how” of machine learning, facilitating machine learning at scale, data scientists are responsible for the “what”, using the infrastructure to create an impact for the business.
While their responsibilities are different, machine learning engineers and data scientists have many of the same skills. Both positions require a good understanding of programming languages such as Python and R, a solid understanding of big data analytics, statistical data, and predictive models, and the ability to operate deep learning frameworks, clustered big data systems, and GPU hardware.
Both roles need to collaborate intensively with others. Dealing with large data sets is a problem that can span the entire organization, including IT, development teams, and business units. Both roles are also required to deliver their findings and make their work usable to others. Machine learning engineers create infrastructure and models that must be usable for day-to-day business problems, while data scientists create visualizations and dashboards for wide use.
Up until now we covered what is involved in becoming a machine learning engineer. Now let’s dive into the field itself: what is machine learning engineering and how it works.
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.
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.
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:
Here are several model deployment options:
Related content: read our guide to machine learning workflow
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:
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
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 you the ML model you release into production is designed to meet the following requirements:
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:
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.
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:
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.
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