While data scientists and machine learning engineers often collaborate closely and their work may overlap, there are distinct differences in their roles and responsibilities. Machine learning engineers focus on deploying and maintaining models, while data scientists are more involved in the analysis and initial development of those models.
Here’s a closer look at the two roles:
Data Scientist
- Data science is a broad field with varying responsibilities across companies. At some companies, data scientists may focus solely on analytical work and setting metrics, while at others, they might be building and deploying machine learning models.
- Generally, data scientists work on a mix of analytical and modeling tasks, bridging the gap between business needs and technical implementation. They are often involved in:
- Identifying opportunities by engaging with stakeholders and senior management
- Ensuring data availability for a given problem
- Exploratory data analysis to uncover patterns and insights
- Building initial proof-of-concept models to assess potential value
- Data scientists also communicate their findings to stakeholders through presentations, keeping them informed about project progress and results.
Machine Learning Engineer
- The primary responsibility of a machine learning engineer is to deploy and monitor machine learning models, ensuring they effectively generate business value.
- While data scientists might develop a high-performing model in a notebook environment, it may not be usable for real-time business decisions unless it’s deployed into production. This is where machine learning engineers come in. They:
- Turn proof-of-concept models into production-ready code and deploy them.
- Monitor models in production to ensure they function as expected and address any issues.
- Machine learning engineers possess strong software engineering skills and expertise in machine learning and modeling. Their work often involves:
- Optimizing models for algorithmic and runtime performance.
- Determining the best deployment strategy, including architecture and cloud provider selection.
- Implementing model testing through unit tests, CI/CD pipelines, and live testing with A/B or shadow systems.
- Utilizing containerization tools like Docker or Kubernetes to ensure model consistency across different machines.
- While machine learning engineers primarily focus on deployment and maintenance, they may also contribute to model research and improvement initiatives.
Key Differences and Challenges
- Skills and Technologies: Data scientists typically possess strong analytical, statistical, and communication skills, while machine learning engineers excel in software engineering, machine learning, and cloud computing.
- Career Path: Machine learning engineering roles are often more challenging to secure, requiring prior experience in data science or software engineering.
- Company Size: Machine learning engineer positions are more common in larger, established tech companies.
The skill difference is given below :
| Data Scientist | Machine Learning Engineer |
|---|---|
| Problem solving, programming | Programming |
| Identify business problems and build PoC models | Deploys models |
| Data analysis and visualisation | Optimises models for better performance, latency, memory, and throughput |
| Develop custom algorithms and models | Deploy model in CPU/GPU on edge or Cloud systems (AWS/Azure/GCP) |
| Develop data annotation strategies | Version control of models, experiments, and metadata |
| Written and Verbal Communication skills | Software engineering skills, Unit Tests |
| ML Concepts | ML Concepts, Ethics, ML Frameworks knowledge |
| Data Analysis, Statistics | Statistics |
| Python/R | Python/C++/Rust |
| Jupyter/Sagemaker | Linux, Bash, Zsh |
| Git | Git, Docker |
It’s important to note that these roles are not rigidly defined, and responsibilities can vary significantly between companies. The provided tables in source offer a general guideline for the skills and technologies associated with each role, but exceptions may exist. In some companies, data scientists might utilize technologies typically associated with machine learning engineers, and vice versa.
Ultimately, both data scientists and machine learning engineers play vital roles in bringing machine learning solutions to life. They work collaboratively to transform data into actionable insights and create impactful applications that benefit businesses and users alike.