You might be new to the workforce, have recently graduated, or trying to keep your current job. There is always a chance to learn and do better in your field. There is no better time to pick up some trending skills than right now.
Be it an Online data science course or a machine learning certification, options are many. Artificial intelligence and machine learning jobs have grown over the past few years. You may find many job titles in this category. For instance, data scientists and machine learning engineers.
Many are unaware of the differences between a data scientist and a machine learning engineer. Continue reading to know the differences between the two and various job opportunities.
Who Is A Machine Learning Engineer?
Machine learning engineers start learning with the software development side. They add machine learning skills through training or additional study. Although some of them are now graduating from specific degree programs.
It is not easy for a software developer to become a machine learning engineer.
There are now more open-source libraries from market leaders like TensorFlow. Now it has become simpler for machine learning engineers to experiment with a variety of models. An experienced machine learning engineer uses these models to build APIs and web interfaces.
Machine learning engineers have some common skills with data scientists. Both of them use R or Python. They need advanced maths skills such as linear algebra and statistics.
However, machine learning engineers are highly skilled when it comes to programming. They also need to know production platforms. For example AWS, and GCP.
Who Are Data Scientists?
The term “data scientist” is often used as the overall term, with machine learning engineer being a subset of it. But, a data science career
is becoming a more specialised job category now. Analysing business data using machine learning or artificial intelligence is becoming common.
Data scientists may start as business analysts and enhance their maths and analytics skills. They can do it with additional courses or on-the-job training. Some may directly start as data scientists. They need to have academic backgrounds in statistics or AI.
With the knowledge of maths and business domains, data scientists need programming skills too. It enables them to develop prototypes of models. The most common programming languages for the job are R and Python. Additionally, Julia, JavaScript, Matlab, and others are also useful. Data scientists must be familiar with data visualisation tools. For example tools like Tableau and Qlik.
Can The Two Work Together?
Big companies consider data scientists and machine learning engineers as separate jobs. However, the smaller and medium companies need one person for both jobs. The company may be hiring either one or the other. It can be a mistake and affect efficiency. If a company can only afford to hire one person, hire for what they need the most. They may cross-train other resources.
What Are The Responsibilities Of An ML Engineer?
- Creating and deploying useful algorithms to a function.
- Applying Docker technologies for creating deployable model versions.
- Development of APIs that are scalable, flexible, and reliable. They integrate data products and sources into applications.
- Creating Infrastructure as Code. They create environments during model development and training that are easily replicated for the final solution.
- Developing and maintaining machine learning systems.
- Implement machine learning algorithms
- They integrate and do versioning control to track model iterations.
- Testing and deploying models.
- Creation of any user interfaces for displaying an in-depth view of the models.
- Implementation of other software engineering concepts. For instance l continuous delivery, auto-scaling, and monitoring.
- They focus on delivering ready-to-use ML products.
- Looking over the needs of the project.
What Are The Responsibilities Of A Data Scientist?
A Data scientist can do everything throughout the Data Scientist career.
From setting up a server to presenting the insights, they are multitasking. Below are some jobs a data scientist performs:
- Identifying business problems and collecting large datasets to solve them.
- Preparing, cleaning, and transforming data before analysing.
- Applying smart methods for data mining for generating information.
- Implementing appropriate algorithms for a business problem.
- Using statistical modelling and ML techniques to measure and improve the results.
- Optimising model hyperparameters.
- Using analytical methods and models to identify trends, patterns, and correlations in given datasets.
- Consult with AI engineers, data analysts, and other stakeholders to support better business decision-making.
- Communicating the findings to business stakeholders.
Necessary Skills For A Data Scientist
The top skills to become a data scientist are as follows:
1. Programming
It is a very important skill necessary for any data scientist. Knowledge of languages like Python helps to excel in this career. It helps in organising unstructured data sets.
2. Analytical Tools
These tools help in extracting valuable information from an organised set of data. For example R, Hadoop, SAS, Pig, and Hive.
3. Mathematics
Mathematical concepts like Probability, Linear Algebra, and Statistics are useful in Data Science.
4. Handling Data
Extracting data from various sources and transforming it to store it in a specific format and structure is crucial. It helps in querying and analysis. Using Data Wrangling, one can clean complex data sets. Examples of data visualisation tools are Tableau and Power BI.
5. Model Deployment
It involves the methods for deploying an ML model in a live environment. It helps in gaining operational value.
Necessary Skills For A Machine Learning Engineer
Below are the necessary skills for a machine learning engineer:
1. Programming
Programming is the most important skill ML engineers should have. They should have a good understanding of computer concepts.
2. Advanced Signal Processing
It helps in minimising noise and extract useful features of a signal. Understanding concepts like wavelets, curvelets, contourlets, and bandlets is useful.
3. Mathematics
It is another important skill for a machine learning engineer in machine learning career. Concepts like calculus, probability, linear algebra, multivariate statistics, and distributions are essential.
4. Data Modeling and Evaluation
It involves knowledge of the data structure and finding suitable data patterns. With the help of algorithms, they can evaluate suitable data.
5. Natural language Processing
NLP Learns, and creates devices and systems for understanding, interpreting, and manipulating human language. Examples of NLP Libraries and techniques are Word2vec and Sentiment analysis.
6. Neural Networks
They are a set of algorithms designed for recognizing patterns. Neural networks interpret data using a type of clustering. They may use labelling raw input and machine perception.
Conclusion
To be a machine learning engineer or a data scientist, you must have skills like mathematics and programming. Also, good communication is required by both professions. So choosing one domain over another is not too challenging. Both data science and machine learning are good career options for 2023. There are great opportunities in both.
So, instead of wondering which one is a better profession, it will be wise to know that both professions are best in their way. Both of them get decent salaries. It depends on your interest in which you want to work. In both career options, you need to have relevant knowledge. It will lead to your best career decisions. Although both differ from each other but play a major role in the development of any company.
To master your technical skills, you can join courses offered by The IoT Academy.
Press Release Distributed by The Express Wire
To view the original version on The Express Wire visit Data Scientist vs Machine Learning Engineer: a career comparison in 2023
