Machine learning engineers and data scientists are not the same role, although there is often the misconception that they are synonymous. While there are areas of overlap or reliance on one another, there are very distinct differences between these two roles in computer science.
What is a machine learning engineer?
Machine learning engineers are responsible for using production-level coding to build the machines (models) that data scientists use to quickly analyze raw data. These models can be easily scaled and are capable of learning from themselves (unsupervised learning), increasing efficiency over time.
What is a data scientist?
Data scientists are responsible for extracting information and insights from an ocean of structured and unstructured data. This often includes data mining and big data. Understanding and translating the meaning behind the data is what makes these individuals valuable to an organization.
Data scientists define which models ML engineers will use to funnel the data. From there, the data is prepared, analyzed, and used for making critical business decisions.
A Master’s degree or PhD in computer science is sometimes preferred for jobs in machine learning and data science. Degrees in mathematics, statistics, or engineering are usually also acceptable. However, the young nature of this industry allows for companies—like Algorithmia—to make exceptions to this requirement if a candidate has the skills needed to succeed. Let’s take a closer look at the skills that go along with ML and data science roles.
Machine learning engineer
- Proficiency in Python, Java, C/C++, R, and/or other programming languages
- Highly analytical mindset
- Ability to quickly learn and modify existing code bases
- Excellent algorithm and data structure skills (including time and space complexity analysis, optimization, etc.)
- Understanding of the goals of machine learning algorithms, how they work, and how to implement them on data at scale
- Skilled in data architecture design
- Strong collaboration and interpersonal skills (used in conjunction with data scientists)
- Proficiency in Python, Java, R, and SQL
- Understanding of data mining and cleansing
- Ability to visualize data, present data, and analyze third-party data
- Experience in manipulating datasets and building statistical models
- Use of big data tools like Hadoop, Hive, and Pig
- Unstructured data management efficiency
- Strong collaboration and interpersonal skills (used in conjunction with machine learning engineers)
Limitations of machine learning
While machine learning helps many companies better organize and analyze mass amounts of data, it’s not a perfect system.
Although algorithms can help with efficiently gathering and grouping data, they need to be routinely modified to retrain and optimize the algorithm for new or updated data.
A poorly built machine learning model can also limit the amount of good data (or data in general) that it can capture, leading to less effective analysis, and thereafter, poor recommendations from the data scientists who rely on the information.
Limitations of data science
Limitations also reside in the realm of data science. First, data must be accurate and relevant for businesses to find it useful. With good data, data scientists can transform business operations. However, if data is incomplete, messy, or erroneous, even the best data scientists would be unable to make informed decisions to benefit their companies.
While many data scientists specialize in one aspect (ie. computer science, mathematics, statistics, etc.), it is nearly impossible for an individual to be proficient in all of them. The ever-changing landscape of data science can prove to be a limitation, as these individuals may have finite abilities in a field with infinite scope.
Algorithmia understands ML engineers and data scientists
Algorithmia understands the distinct goals and methods of ML engineers and data scientists, as well as their day-to-day challenges and needs. That’s why Algorithmia created a serverless microservices architecture that allows organizations to deploy their machine learning models at scale with ease.
Hosting models on this type of architecture allows for models to be pipelined together, no matter what language each one is written in. Pieces of code can be edited or reused without making edits to the entire model, as you would in a monolithic architecture. In a microservices architecture, each piece is deployed as a microservice, which allows for easier and more accurate editing.
See how Algorithmia can help you build better software for your organization in our video demo.