Data Scientist vs Machine Learning Engineer
The role of Data Scientist has become popularized over the last decade or so. Data Scientists usually have a background in statistics, math, and computer science; however, these roles were not truly versed in the infrastructure side of things, and therefore, could not go from inception to production with their machine learning models without time and resource constraints. Ergo, the role of Machine Learning Engineer spawned. ML Engineers are veteran software engineers that possess skills in building ML workflows and infrastructure that are required to move projects to production.
Data Scientist. When a business has a problem that needs resolving, they turn to Data Scientists to gather, analyze, and obtain valuable insights from data. This role does not usually provide production-ready code as it is not their background. Being a Data Scientist is a bit more ad hoc and seeks to translate the business problem into a more technical model to help drive business decisions.
Responsibilities:
- Understand how to translate business needs into data-oriented solutions
- Produce reports and presentations of research, findings, and insights to key business leaders
- Develop custom data models and algorithms
- Identify what data sources/measurements might be appropriate to solve specific analytical problems, and/or recommend what other processes or data sources the organization should be measuring in order to meet specific goals
- Help the business achieve organizational goals, for instance increase revenue, exploit new growth areas, or acquire new customers
- Develop A/B testing framework for continuous testing of model quality
ML Engineer. This role is a crossroad between data science and software engineering. These engineers are responsible for integrating tools and frameworks together to ensure the data, data pipelines, and key infrastructure are working cohesively to allow for ML models to be productionized and scale as needed. These engineers also have the capability to automate repetitive tasks as well as build algorithms that allow systems to identify patterns within its own data to teach itself how to think.
Responsibilities:
- Develop data and model pipelines
- Design distributed systems
- Write production-level code
- Perform code reviews
- Enable ML projects to run in production and scale
- Execute on ML algorithms, frameworks, and libraries
Collaboration. Having a team of Data Scientists and ML Engineers can be advantageous to larger enterprises. Reason being, most of the entire life cycle of any analytics and ML project consists of tasks that require both roles to complete the project successfully. No one role can answer these key questions when building an ML track:
- How do we convert a business problem into a data science problem?
- For our project, do we have the data, infrastructure, and pipelines required?
- How do we measure if the data quality is good enough?
- What is our deployment strategy?
- What metrics are we using to measure the success of the model?
The full cycle from start to finish begins with the Data Scientist extracting data and beginning preparing that data, building the models, and training, testing, and validating those models. Once the Data Scientists complete their stage of the process, the ML Engineer deploys the models into production and sets up the system for continuous iteration, auditing, and monitoring. Together, these roles provide the business with high-quality work and best practices.
You will find much confusion over these two roles due to the fluidity of how companies decide to define each of these roles by what they need rather than focusing on what each role is meant to be responsible for, but at a high level, Data Scientists should be focused on analyzing data, providing insights, and building models, while ML Engineers should be focused on productionizing and deploying for large-scale complex machine learning products.
About Wallaroo.
Wallaroo enables data scientists and ML engineers to deploy enterprise-level AI into production simpler, faster, and with incredible efficiency. Our platform provides powerful self-service tools, a purpose-built ultrafast engine for ML workflows, observability, and experimentation framework. Wallaroo runs in cloud, on-prem, and edge environments while reducing infrastructure costs by 80 percent.
Wallaroo’s unique approach to production AI gives any organization the desired fast time-to market, audited visibility, scalability — and ultimately measurable business value — from their AI-driven initiatives, and allows data scientists to focus on value creation, not low-level “plumbing.”
