What is a Machine Learning Expert? What do they do?

One of the options for data analysts who want to explore new opportunities and look for ways to climb the career ladder is to become a machine learning engineer. 

Machine learning engineers, whose demand is high but supply is insufficient, are vital not only for the data science sector but for all organizations that place data at the center of their strategy. 

Here's a surprising statistic: 🧐 On average, a person generates 1.7 MB of data every second. Therefore, it's not surprising that the popularity of machine learning—a field that gives computer systems the ability to learn using this data—is increasing.

In this article, we've written about what machine learning engineers do, their responsibilities, and the skills they need to have.

What Does a Machine Learning Engineer Do?

Machine learning engineers are highly skilled programmers who develop artificial intelligence (AI) systems that use large datasets to research, develop, and build algorithms that can learn and make predictions.

Machine learning engineers manage the entire data science pipeline, including finding and preparing data sources, building models, and deploying models. 

Typically, machine learning applications can include:

🔵 Natural language processing 

🔵 Image recognition 

🔵 Machine vision 

🔵 Speech recognition

🔵 Financial modeling 

🔵 Biomedical applications

🔵 Fraud detection 

🔵 Recommendation engines 

This list can go on and on! 

The main point to understand is that engineers and analysts use machine learning to automate tasks that are highly complex, time-consuming, and difficult for humans to complete accurately on their own.

ML engineers typically work within a data science team. They collaborate with data scientists, data analysts, IT specialists, DevOps specialists, software developers, and data engineers.

Many job descriptions require knowledge of programming languages such as Python, Java and C/C++.

Responsibilities of a Machine Learning Engineer

Machine learning engineers have two primary roles: Feeding data into machine learning models and deploying these models...

Data acquisition and preparation is a complex job. Data may come from various sources, often streamed in real-time. It needs to be automatically processed, cleaned, and prepared to fit the model's data format and other requirements.

Deployment involves taking a prototype model in a development environment and scaling it to serve real users. This requires running the model on more powerful hardware, enabling access to the model through APIs, and allowing the model to be updated using new data.

Although responsibilities and tasks may vary depending on the size of the company and the overall data science team, the machine learning engineer job description includes most of the following responsibilities:

1. Designing, developing, and researching machine learning systems, models, and schemas
2. Reviewing data science prototypes 
3. Searching for and selecting appropriate datasets before performing data collection and data modeling
4. Performing statistical analysis and using the results to improve models
5. Training ML systems and models as needed
6. Identifying differences in data distribution that can affect model performance in real-world situations
7. Visualizing data for deeper insights
8. Analyzing use cases of ML algorithms and ranking them according to their likelihood of success
9. Improving data quality

Skills a Machine Learning Engineer Should Have

There are certain specific qualities that machine learning engineers need. In addition to soft skills like teamwork, problem-solving, flexibility, and leadership, common technical skills include:

✅ Artificial intelligence, data mining, and data analysis fundamentals

✅ Knowledge about database systems, data warehouses, and other data tools

✅ Understanding of supervised, unsupervised, and deep learning

✅ Mathematics and statistics knowledge

✅ Extensive knowledge of programming languages like Python, R, and Java

✅ Debugging and optimizing machine learning models

✅ Ability to create software, APIs, and other interfaces that interact with machine learning models

✅ Understanding of cloud computing and distributed systems like Hadoop, Spark, or Flink

Tools and Programs Used by Machine Learning Engineers

A machine learning engineer is expected to be proficient with the following machine learning tools and resources:

• Apache Hadoop
• Apache Mahout
• Apache Spark
• AWS Machine Learning 
• C++
• Google Colaboratory 
• IBM Watson Studio
• Java
• JavaScript
• Python
• PyTorch

5 Reasons to Become a Machine Learning Engineer

If you are curious about a career in data science or artificial intelligence, here are some of the key advantages of becoming a machine learning engineer.

1. Attractive Salaries

Machine learning has become a rapidly growing field in Turkey in recent years. It offers attractive salaries for those who want to work in this area. The salaries of machine learning professionals in Turkey vary depending on the industry they work in, the size of the company, and their level of experience. Receiving relevant training and gaining experience provides great advantages for those who want to pursue a career in this field.

2. High Demand

Many companies are highly interested in data, and as a result, the demand for data professionals in the job market has never been higher. All indicators suggest that machine learning and artificial intelligence will become even more important in the future job market.

In Turkey, especially technology companies, the finance sector, the healthcare sector, and e-commerce firms need experts in this field. Large companies and startups compete to recruit talented professionals for data analysis and artificial intelligence projects.

3. Continuous Learning

Machine learning is a relatively new field. Like software developers, ML engineers are also in a constant learning cycle. This provides professionals in this field with continuous learning opportunities.

4. Cutting-Edge Technology

The role of a machine learning engineer offers the chance to work with cutting-edge technologies that drive innovations such as self-driving cars, conversational AI, automated cybersecurity, and smart city technologies.

5. Diversity

Machine learning engineering roles are highly diverse. You may find yourself working in various industries, transitioning between multiple approaches and tools, and exploring different algorithms. Additionally, you have the opportunity to make a real impact. You could join a team working on the next major breakthrough in healthcare, cybersecurity, marketing, or autonomous vehicles.

What is the Difference Between a Machine Learning Engineer and a Data Analyst?

You may have noticed that many of the technical skills and responsibilities required for a machine learning engineer are similar to those of a data analyst. So, what is the difference between these two roles?

The fundamental difference between a data analyst and a machine learning engineer is that a machine learning engineer requires a broader range of expertise. For example, while data analysts may have a detailed and refined understanding of specific datasets and analytical techniques, machine learning engineers are generally more focused on how these data are processed.

Machine learning engineers also need more technical skills. Designing, developing, and deploying algorithms that make autonomous decisions brings new responsibilities. Therefore, machine learning engineers must have in-depth knowledge of data science techniques and software development.