Machine Learning has emerged as one of the hottest professional fields in recent years. There are many job titles that have emerged in relation to it. In this article, we will explore the role of a machine learning engineer. We will learn about the work it entails, the skills and tools it requires, and differentiate them from other machine learning/data related roles.
a. … a Data Analyst?
b. … a Software Engineer?
c. … a Statistician?
d. … a Data Scientist?
So What does a machine learning engineer do exactly?
1. What Does a Machine Learning Engineer Do?
A machine learning engineer is a professional who is responsible for designing, building, and maintaining machine learning models. These models are created to analyze data, learn from it, and make intelligent decisions or predictions based on the data. Machine learning engineers work with large datasets, using statistical and mathematical techniques to build models that can accurately predict outcomes or classify data into specific categories.
The work of a machine learning engineer typically involves the following steps:
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Understanding the business problem: The first step in building a machine learning model is to understand the business problem that needs to be solved. This involves working with stakeholders to identify the problem, gather data, and determine the appropriate machine learning approach to solve the problem. While the set of machine learning algorithms is independent of the application domain, certain algorithms are more suited to specific settings, such as sequence models for Natural Language Processing or Genomics etc.
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Preprocessing and cleaning data: Machine learning models, especially Deep Learning ones with lots of parameters to train, require large amounts of data to be effective. However, this data is often messy and needs to be cleaned and pre-processed before it can be used to train a model. This involves tasks such as missing value imputation, outlier detection, normalization etc. Data cleaning and processing is probably the least exciting part of any project, but it is also one of the most important ones. A large chunk of the time spent on a machine learning project is dedicated to it, and the understanding of the business problem mentioned above is key to its success.
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Choosing an appropriate model: There are many different types of machine learning models, each with its own strengths and weaknesses. A machine learning engineer must choose the model that is most appropriate for the problem at hand, taking into account the nature of the data and the desired outcome. A good ML engineer should be familiar with a large set of algorithms to be able to choose from them.
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Training the model: Once the model has been selected, the next step is to train it using the cleaned and pre-processed data. This involves using algorithms to adjust the model's parameters so that it can accurately predict outcomes or classify data. One of the most important such training algorithms is gradient descent.
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Evaluating and optimizing the model: After the model has been trained, it is important to evaluate its performance to ensure that it is accurate and reliable. This may involve testing the model on a separate dataset, or using a variety of metrics to measure its performance. If the model's performance is not satisfactory, the machine learning engineer may need to go back and optimize the model by adjusting its parameters, or choosing a different model altogether.
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Deploying the model: Once the model has been trained and optimized, it is ready to be deployed in a production environment. This may involve integrating the model into an existing application, or building a new application specifically to utilize the model. Most companies choose to host their models in a dedicated cloud service, such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP).
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Monitoring and maintaining the model: Even after the model has been deployed, the work of a machine learning engineer is not finished. It is important to continuously monitor the model to ensure that it is performing as expected and to make any necessary updates or adjustments. This may involve retraining the model on new data or fine-tuning its parameters to improve its performance. A typical example of model performance degradation is caused by data drift, when the distribution of the data changes over time, and the model is not updated (Think, for example, about a model trained to detect signs of retinopathy in a lab under certain lighting conditions, but is then deployed in the wild where it is used in natural lighting conditions).
In addition to these tasks, a machine learning engineer may also be responsible for research and development, and collaborating with cross-functional teams. They must also stay up-to-date with the latest machine learning techniques and technologies, as they constantly evolve.
Now that we know what a machine learning engineer does in their work, let’s look at the skills this takes.
2. What are the Skills a Machine Learning Engineer Must Have?
To become a machine learning engineer, there are several skills that are essential:
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Strong programming skills: Machine learning engineers need to be proficient in one or more programming languages, such as Python. They should be comfortable working with large codebases and be able to write efficient, well-structured code.
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Data manipulation and analysis: Machine learning models are trained on large datasets, so it is important for machine learning engineers to have strong skills in data manipulation and analysis. This includes working with tools such as SQL, Pandas, and NumPy to clean, transform, and analyze data.
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Machine learning concepts and techniques: A machine learning engineer should have a strong understanding of machine learning concepts and techniques, including supervised and unsupervised learning, decision trees, neural networks, transformer architectures etc. They should also be familiar with a variety of algorithms, and be able to select the most appropriate one for a given problem.
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Statistics and probability: Machine learning models are based on statistical and probabilistic principles, so a strong foundation in these areas is important for machine learning engineers. This includes understanding concepts such as hypothesis testing, Bayesian inference, and probability distributions.
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Data visualization: Being able to effectively visualize and communicate data is an important skill for machine learning engineers. This includes using tools such as Matplotlib, Seaborn, and Tableau to create clear and informative graphs and charts.
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Problem-solving and critical thinking: Machine learning engineers are often faced with complex problems that require creative solutions. It is important for them to be able to think critically and approach problems in a logical and systematic way.
To acquire these skills, a person can start by taking online courses or earning a degree in a field such as computer science, data science, or statistics. It is also important for aspiring machine learning engineers to gain practical experience by working on projects and participating in hackathons or online challenges. Building a strong portfolio of projects and demonstrating the ability to apply machine learning concepts to real-world problems can be very helpful in getting hired as a machine learning engineer.
In order to learn how to become a good machine learning engineer, it is important to know which tools they use on a regular basis, so as to focus your learning on them.
3. What Tools Do Machine Learning Engineers Often Use?
What are some of the important tools a machine learning engineer needs to master to be efficient at their work?
There are many tools that are commonly used by machine learning engineers, and the specific tools that are most important to master will depend on the nature of the work and the preferences of the individual. However, here are some tools that are commonly used in the field of machine learning:
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Programming languages: Machine learning engineers typically need to be proficient in one or more programming languages, such as Python. These languages are used to write code that implements machine learning algorithms and builds models, most often using dedicated libraries and frameworks.
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Machine learning libraries and frameworks: There are many libraries and frameworks available that make it easier to build machine learning models, such as scikit-learn, TensorFlow, PyTorch, and JAX. These libraries provide pre-built algorithms and functions that can be easily incorporated into machine learning projects.
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Data manipulation and analysis tools: Tools such as SQL, Pandas, and NumPy are used to manipulate and analyze large datasets. These tools make it easier to clean, transform, and prepare data for use in machine learning models.
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Data visualization tools: Tools such as Matplotlib, Seaborn, and Tableau are used to create clear and informative graphs and charts that help to visualize and understand data.
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Cloud computing platforms: Machine learning models often require significant computing resources, and cloud computing platforms such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP) provide access to powerful computing resources on demand.
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Collaboration and project management tools: Machine learning engineers often work on teams and may use tools such as Jupyter notebook, Google colab, GitHub, and Asana to collaborate and manage projects.
In addition to these tools, it is also important for machine learning engineers to be familiar with a variety of machine learning algorithms and techniques, and to have a strong understanding of statistical and mathematical concepts.
As stated before, machine learning engineer is one example of many professions that have spun off the relatively new field of machine learning. Let’s try and understand the differences between the main job titles.
4. What is the Difference Between a Machine Learning Engineer and…
a. … a Data Analyst?
While there is some overlap between the roles of a machine learning engineer and a data analyst, they are distinct professions that involve different skills and responsibilities.
A data analyst is primarily responsible for analyzing data and reporting on findings to inform business decisions. This may involve tasks such as collecting and cleaning data, creating graphs and charts to visualize it, and running statistical analyses on it. A data analyst may also develop dashboards or reports to help stakeholders understand and make use of the data.
A machine learning engineer’s work involves using statistical and mathematical techniques to build models that can accurately predict outcomes or classify data based on patterns in the data. They may also be responsible for research and development, collaborating with cross-functional teams, and staying up-to-date with the latest machine learning techniques and technologies.
In general, data analysts focus more on analyzing and reporting on data, while machine learning engineers focus more on building and maintaining machine learning models. However, there is some overlap between the two roles, and some professionals may work in both areas.
b. … a Software Engineer?
A machine learning engineer and a software engineer are both responsible for designing, building, and maintaining computer systems, but they have different areas of focus and expertise.
A software engineer is responsible for developing software programs and systems that meet the needs of an organization or client. This may involve tasks such as designing and building applications, writing code, testing and debugging programs, and maintaining and updating existing systems. Software engineers may work on a variety of projects, including web applications, mobile apps, and desktop software.
A machine learning engineer, in contrast, is focused on building and maintaining machine learning models. Machine learning engineers work with large datasets and use statistical and mathematical techniques to build models that can accurately predict outcomes or classify data into specific categories.
While both software engineers and machine learning engineers are responsible for developing and maintaining computer systems, software engineers focus more on traditional software development, while machine learning engineers focus on building and maintaining machine learning models.
c. … a Statistician?
A machine learning engineer and a statistician are both professionals who work with data and use statistical and mathematical techniques to analyze and make predictions based on the data. However, they have different areas of focus and expertise.
A statistician is a professional who uses statistical methods to collect, analyze, and interpret data. Statisticians may work in a variety of fields, including business, finance, healthcare, and government. They may be responsible for tasks such as collecting and analyzing data, developing statistical models, and making data-driven recommendations.
A machine learning engineer, on the other hand, is focused on building and maintaining machine learning models. These models are designed to analyze data, learn from it, and make intelligent decisions or predictions based on it. Machine learning engineers work with large datasets and use statistical and mathematical techniques to build models that can accurately predict outcomes or classify data into specific categories.
Both statisticians and machine learning engineers work with data and use statistical techniques. Statisticians generally focus more on traditional statistical analysis and modeling, while machine learning engineers focus on building and maintaining machine learning models.
d. … a Data Scientist?
A data scientist applies statistical and machine learning techniques to analyze and interpret complex data. They are responsible for extracting insights from data, building predictive models, and communicating their findings to stakeholders.
Both machine learning engineers and data scientists work with data and use machine learning techniques, but they have different areas of focus and responsibility. Machine learning engineers are primarily concerned with building and deploying machine learning models, while data scientists are more focused on analyzing and interpreting data to extract insights and build predictive models.
