Machine learning and data science are the keys to modern business.
Nearly every industry is being disrupted by these two technologies, which are growing at a rapid pace and changing the way we live. They’re already so prevalent that many of us don’t even realize how much they’ve changed our world—or what it’s like to live without them.
What is a Machine Learning Engineer?
A machine learning engineer is a person who develops, optimizes, and maintains algorithms that they can train to solve problems based on data.
Machine learning engineers use large amounts of data to build models that can predict future events or outcomes. They typically work in teams with other data scientists and software developers to train and implement these models for different business scenarios.
What is a Data Scientist?
A data scientist is a person who uses statistical methods, machine learning, data mining, and predictive analytics to turn raw data into actionable insights.
Data scientists work in industries ranging from finance to healthcare to government. They use their skills to identify patterns, trends, and anomalies in the data they analyze and help companies make smarter decisions about how they operate.
Machine Learning vs. Data Science
Data science is a field of study that focuses on how to analyze data best. At the same time, machine learning is a subset of data science that uses various algorithms to predict future events based on past performance.
Data science and machine learning are used in the modern world for many different purposes, but there are some critical differences between the two fields. Data science focuses on finding insights from data sets, while machine learning focuses on making predictions about future events based on past performance.
Roles and Responsibilities of ML Engineer vs. Roles and Responsibilities of a Data Scientist.
ML Engineer Roles and Responsibilities |
Data Scientist Roles and Responsibilities |
| Design, develop, maintain and improve machine learning algorithms for the company’s products. | Data scientists use various software, including Python and R, to explore and visualize data. |
| Make sure the company uses machine learning principles in its business decisions. | They are also responsible for ensuring that their insights are adequately communicated to non-technical stakeholders. |
| The primary responsibility of an ML Engineer is to design, develop and implement machine learning models for different problems. | They are responsible for collecting and analyzing data and making recommendations for business processes. |
| Another responsibility of an ML Engineer is to test and evaluate their machine learning models and algorithms on real-world data sets. | Data scientists should be able to write code and use statistical software like R or Python. They must be able to work with a wide range of data sets, from social media analytics to healthcare information. |
Key Functions of ML Engineers and Data Scientists
The demand for machine learning engineers and data scientists has grown significantly in recent years due to the rise of artificial intelligence and big data analytics.
Functions of ML Engineers |
Functions of Data Scientists |
| Machine learning engineers use algorithms to create models based on data. Then they can use those models to decide how to process new information. | There are many functions of data scientists. First, they must be able to collect and analyze data. They also must know how to use different tools, such as programming languages and statistical software. |
| The Machine learning engineer supports Data Scientists by building data pipelines, implementing models, and training the models based on the results of experiments conducted by Data Scientists. | They must have a solid understanding of the business environment in which they work, so they can help their organization make better decisions. |
| Another critical function of ML engineers is evaluating new data sources for training models and developing new features for existing models. | Data scientists are responsible for assisting companies in using AI to optimize their business practices. |
| They are responsible for ensuring that AI systems can handle complex tasks like facial recognition and other types of pattern-matching, speech synthesis, and language translation. They also design algorithms that can be used in applications like driverless cars or voice-controlled devices. | They also work with machine learning algorithms that can help companies understand their internal processes better. |
Skills Required of Machine Learning Engineers and Data Scientists
Skills Required of ML Engineers |
Skills Required by Data Scientists |
| The Machine Learning Engineer uses tools like Git/Github for version control, Jenkins for continuous integration, and Docker for containerization. | They must also be familiar with machine learning algorithms and statistical modeling techniques for data analysis. |
| They’ll often be the ones who design and implement new algorithms and methods, as well as improve existing ones. | They must communicate technical concepts in a way non-technical people can easily understand. |
| Machine learning engineers need to be able work with a wide range of tools and skills, including Python, C++, R, and SQL. | Data scientists are problem solvers who enjoy working on complex problems and finding solutions using advanced techniques. |
| They also need to understand statistics, probability theory, and computer science fundamentals like algorithms and data structures. | They should also be familiar with machine learning techniques and have some exposure to software development. |
Average Salary for Data Scientists and ML Engineers
The average salary for Data Scientist and Machine Learning Engineer in India is ₹ 12.5 Lakhs per year.
Data scientist professionals with less than two years of experience earn an average salary of ₹ 4.4 Lakhs per year.
An average salary of 52.2 lakhs is made by data scientists with more than eight years of experience.
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FAQs
1. Who earns more, data scientists or machine learning engineers?
In recent years, data science has become one of the most sought-after professions in the world. The average salary of a Machine Learning Engineer is more than that of a Data Scientist. In the United States, it is around US$125,000; in India, it is ₹875,000.
2. Can a data scientist become a machine learning engineer?
Yes, absolutely. A data scientist is essentially a person who uses machine learning to build models and algorithms for solving problems, which then turn into insights that companies and other organizations can use. A machine learning engineer is someone who makes these algorithms, as well as works on improving them as they run in production.
3. Is machine learning harder than data science?
No, machine learning is not more complex than data science.
The two fields are closely related and have many overlapping skills. Data scientists can learn to implement machine learning models and vice versa. Many data scientists and machine learning engineers have a background in statistics or mathematics.
4. Should I learn ML first or data science?
Let’s get the difference between machine learning (ML) and data science out of the way.
Data science is about extracting knowledge from data, focusing on business problems. ML is the subset of data science that uses algorithms to find patterns in data.
So if you want to do data science, you’ll need to learn both, but if you want to do ML alone, it’s unnecessary to learn all data science.
5. Should I go for AI or data science?
AI and data science are complementary technologies that improve your business. AI is great for automating processes and making decisions on the fly; data science helps you understand how people use your product so that you can make informed decisions about how to improve it.
6. Will AI replace data science?
AI is great, but it’s not going to replace data science.
Data science is about understanding what the data means and how it can be used to tell you something about the world. AI is about understanding how to use algorithms and computers to find patterns in data that are meaningful or useful.