The market today is overloaded with data and the requirement for professionals to leverage its power for business growth. Top brands are on a constant lookout for Data Scientists and Machine Learning engineers. The job market for machine learning is robust and continues to exhibit strong growth with no indications of deceleration.
As Data Scientists and Machine Learning engineers share some similarities in their roles, aspiring individuals often find themselves in a dilemma when deciding between the two career paths. However, making an informed choice becomes easier when you focus on understanding the key distinctions between these professions and consider your own experiences and future goals.
Here’s what we’ll cover:
- Why Switch From Data Scientist To Machine Learning Engineer?
- Transition From Data Scientist To Machine Learning Engineer: Roadmap
- Data Scientist And Machine Learning: Common And Specific Skills
- Data Scientist vs. Machine Learning Engineer: Roles and Responsibilities
- Data Scientist And Machine Learning Engineer: Salary
- Transition to Machine Learning Engineer: Walk the Path with Interview Kickstart
- FAQs on Data Scientists to Machine Learning Engineers
Why Switch From Data Scientist To Machine Learning Engineer?
Over the last four years, the field of AI and machine learning has witnessed a remarkable surge, with job opportunities soaring by nearly 75%. This growth trajectory is expected to persist. Moreover, several industries have prominently adopted machine learning, such as healthcare, education, marketing, retail and e-commerce, as well as financial services. Opting for a career in machine learning is a prudent decision for those seeking a lucrative profession with sustained demand.
Companies have been overloaded with data. The question of where or how to utilize the data remained a challenge until data scientists came into the picture. They analyze and manage the complete data to make it usable into actionable insights through model development, training and evaluation.
The generated results are further converted into action through a Machine Learning Engineer. Their work is concerned with the deployment of models curated by data scientists into production systems. They continue to monitor and maintain the model performance and hence are responsible for continuous efficient functionality.
But why upskill or make a transition to a Machine Learning Engineering career? As we began with, the generated data had been enormous. It is already processed and modeled by a vast number of companies using sophisticated technologies. The next step ahead is a requirement of professionals capable of deploying it for practical applications that align with the company’s goals.
Transition From Data Scientist To Machine Learning Engineer: Roadmap
Post working as a data scientist, the urge to use your knowledge more efficiently is obvious. The journey to becoming a Machine Learning Engineer can be quick based on the existing skills. Here is what’s needed:
- Machine Learning fundamentals: With practical knowledge of ML algorithms, data preprocessing and model evaluation, the requirement is to dive into theoretical aspects of ML, which encompasses neural networks, optimization algorithms and reinforcement learning techniques.
- Programming languages and tools: Proficiency in Python and relevant libraries such as Pandas, NumPy and scikit-learn is expected. The need is to get familiar with deep learning libraries like PyTorch and TensorFlow.
- Software engineering skills: The already known here are data analysis and scripting. More familiarity is necessary with respect to coding standards, version control, modular code design, code testing and software development lifecycles.
- Model deployment: The prior experience here would be with Jupyter notebooks and cloud services for data storage and analysis. For deployment, learning about containerization using Docker, container orchestration, and cloud services for scalability and cost-effective infrastructure is of significance.Â
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Data Scientist And Machine Learning: Common And Specific Skills
The foundation of a data scientist’s work revolves around data, whereas machine learning engineers focus on crafting models derived from data scientist-developed datasets. Thus, both possess some common set of skills and have some distinct requirements. Here are the common and specific skill sets for a career in machine learning engineer vs. data scientist:
Common Skills for Data Scientists and Machine Learning Engineers
- Programming
- Data manipulation, including data cleaning, preprocessing, wrangling and feature engineering
- Statistics for data analysis, hypothesis testing and model evaluation
- Data visualization with knowledge of tools like Seaborn or Matplotlib
- SQL for extraction and querying data from relational databases
- Communication with both technical and non-technical audiences
Specific Skills for Data Scientists
- Domain knowledge
- A/B testing
- Data mining
- Predictive modeling
Specific Skills for Machine Learning Engineers
- Software engineering
- Model Optimization
- Production monitoring
- Automated testing and deployment
- Scalability
- Neural networks
- Natural Language Processing
- Advanced signal processing
Data Scientist vs. Machine Learning Engineer: Roles and Responsibilities
There lies a difference between data scientist and machine learning engineer roles and responsibilities.
Responsibilities of Data Scientist
- Recognize the data sources and develop the automated data collection procedure
- Preprocess both types of data, structured and unstructured
- Look for trends and patterns from massive datasets available
- Develop machine learning algorithms and build predictive models
- Perform ensemble modeling to combine the models
- Use data visualization techniques to effectively communicate the findings and results
- Come up with solutions and strategies for business challenges
- Work with different departments and teams
Responsibilities of Machine Learning Engineer
- Design machine learning systems
- Understand and work on data science prototypes
- Work on statistical analysis and fine-tuning through test results
- Learn about different ML algorithms and tools and implement appropriate ones according to requirements
- Train and modify the system training as per the requirements
- Choose the right dataset based on domain knowledge and, subsequently, the data representation methods
- Work on optimization of existing ML libraries and frameworks
- Perform machine learning tests and experiments
- Be updated with developments in the field
Data Scientist And Machine Learning Engineer: Salary
The machine learning engineers vs. data scientists in some common regions of the world are tabulated below.
Transition to Machine Learning Engineer: Walk the Path with Interview Kickstart
The transition from data scientist to machine learning engineer can be an interesting and dream opportunity. With familiarity with demand, responsibilities and challenges faced at the next level, opting to step up in career is a wise decision supported by top recruiters and professionals.
Helping you embark on the journey, the Interview Kickstart helps the candidates understand their strengths and weaknesses by brushing up on the skills. We also make you interview-ready with top recruiters from FAANG+ companies. Don’t believe us? Register for our FREE Webinar! Have a walkthrough of our curriculum and then decide for yourself.
FAQs on Data Scientists to Machine Learning Engineers
Q1. What is the difference between a MLOps engineer and a data scientist?
Data scientists deal with data analysis, extracting insights and building machine learning models. MLOps engineers focus on the deployment, automation and management of Machine Learning models.
Q2. Should I learn data science or machine learning first?
A beginner is recommended to start building fundamentals through data science. However, starting with ML is a wise choice if your interest lies in building predictive models and working with AI algorithms.
Q3. Do you need a Master’s to be a machine learning engineer?
A Master’s degree is beneficial but not the only source of becoming a machine learning engineer. A dedicated and strong profile in the field post-bachelor’s can also help.
Q4. Should data engineers learn ML?
Ans. Data engineers can leverage the concepts of Machine Learning if their work is in association with data scientists and machine learning engineers. It will be helpful in designing data pipelines.
Q5. Can I become an AI engineer after data science?
Yes, a transition to AI engineering or Machine Learning engineering is possible after data science. The aspirants need to get well-versed in the required concepts.
Q6. Is Machine learning more computer science or data science?
Machine learning is an interdisciplinary field drawing information and concepts of both computer and data science. Data science algorithms and techniques for data prediction are of equal importance with concepts for the implementation and deployment of models.
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