ML Engineer in 2024: Roles, Challenges & the Future

It is safe to say beyond the shadow of a doubt that we are amidst an AI revolution. Today, there is AI behind everything from semi-autonomous vehicles to chatbots that answer almost all of your online queries.

One of the critical disciplines that makes AI tick is Machine Learning (ML) - a science that revolves around creating machines that learn from data and evolve to a point where they can make predictions from it.

The architects behind these sophisticated machines are called ML Engineers.

In this blog, we will explore the roles and responsibilities of machine learning engineers in detail, list out the skills and qualifications needed to be one, and discuss the future of machine learning engineer role and more.

What Does a Machine Learning Engineer Do?

Artificial intelligence and machine learning engineers are responsible for the entire lifecycle of creating self-running software or applications -from the data collection phase to its deployment.

They generally work as part of a larger team that can include, based on the project at hand, data scientists, administrators, and software engineers. Succinctly put, they are a unique blend of software engineers and data scientists who design, develop, and deploy ML systems and applications.

Their primary goal is bridging the gap between raw data and extracting meaningful insights from them.

Skills and Qualifications of an ML Engineer

AI/ML engineers need to possess a myriad of skills in order to land jobs in the machine learning industry.

The skills can be divided into two major categories:

Technical Skills

Strong technical skills are the foundation to be a machine learning developer. The list of technical skills you need to possess are:

• Knowledge of at least one of these programming languages, i.e., Python, Java, R, C, or C++, in order to be able to create applications.
• Machine Learning Frameworks such as TensorFlow or Keras.
• A comprehensive understanding of Data Analysis and Visualization Tools. For instance, Pandas and Seaborn.
• Familiarity with cloud computing platforms to be able to handle vast amounts of data and to be able to create cloud-based scalable applications.
• A good grasp of DevOps to monitor and test ML applications.

Mathematics and Statistics

A career in Machine Learning will also require you to have a firm grasp of theoretical concepts and principles of statistics as well as various other disciplines of mathematics, such as:

• Calculus
• Linear Algebra
• Probability
• Numerical Methods

Apart from these basic skills, you can also consider pursuing the following certifications in order to improve and set you apart as a machine learning engineer.

• IBM Machine Learning professional
• AWS Certified Machine Learning
• Google's Professional Machine Learning Engineer

Responsibilities of an ML Engineer

ML engineers are responsible for the entire lifecycle of ML systems from start to finish. Their list of responsibilities is as follows:

A. Data Collection and Cleaning

An ML system is nothing without data it learns from. This is why an ML engineer's first and foremost responsibility is to collect and clean the data to ensure consistency and improve the quality of data.  

In large organizations, data collection and cleaning are typically handled by data engineers. However, in smaller teams, an ML engineer might need to manage these tasks, requiring proficiency in data management and preprocessing alongside machine learning expertise.

Often, ML engineers also look for readily available training data sets online rather than having to curate them from scratch each time.

B. Algorithm Selection

After the data is cleaned, ML engineers then start developing a model. To do so, they are first required to evaluate the various algorithms available and analyze which would be best suited for the task at hand.

Once a model is selected, they determine the hyperparameters and begin training the model on the input data.

C. Model Development and Evaluation

Once the model is trained, they are then required to evaluate its performance on a held-out test set. If the results are inconclusive or unsatisfactory, the process repeats itself from the beginning by rechecking the data, experimenting with various models, and altering the hyperparameters.

Tools and Technologies Used by ML Engineers

ML engineers use a multitude of tools and technologies when developing machine learning applications. The essential among them are:

1. Programming Languages

The knowledge of programming languages is essential to build machine learning algorithms. The most common ones used in the ML industry are:

• Python - a general-purpose programming language with vast libraries and frameworks.
• R - a domain-specific programming language, an extensive collection of packages and tools for ML engineering.
• Java - another general-purpose programming language that boasts of a cross-platform and scalable architecture along with an extensive library for ML engineering-specific applications.  

2. Machine Learning Frameworks

ML frameworks are ready-to-use pieces of code designed to reduce the need for writing code to develop ML applications. Tensorflow by Google, PyTorch by Facebook, and Scikit-learn, an open-source framework, are some of the most commonly used ML frameworks.

Challenges Faced by ML Engineers

Machine learning engineering is a field that is still in its infant stage, and as a result, it comes with its own unique set of challenges.

1. Lack of or Low-Quality Data‍

The first and foremost hurdle ML engineers have to face is sometimes the lack of data. At times, even when there is sufficient data available, the quality of the data can also be a challenge. Underfitting and overfitting of data are common occurrences, too.

2. Choosing the Right ML Model‍

Once you have the data preprocessed and cleaned, the next challenge as a deep learning engineer lies in choosing the best model for the specific task at hand. Each model has its strengths and trade-offs, and choosing the right one must be carefully thought out.

3. Continual Monitoring of ML Applications‍

ML engineers' final challenge is ensuring their applications are running as designed with the two main components in place. This requires constant monitoring and, if any issues arise, addressing them promptly.

Future Outlook of ML Engineering

ML Engineering has a bright future ahead of it, considering that the human race is becoming more technologically independent by the minute. In fact, a machine learning engineer's job description is one of the highest-paying IT jobs and professions.

The ML market was valued at $24 Billion in 2022 and, as per current projections, will grow to over a $750 Billion industry by 2035. An ML Engineer's salary in India is between 8 and 16 lakh rupees. In America, a machine learning role will earn you between $85,000 and $ 100,000 annually.

Conclusion

As AI continues to transform the world around us, automated machine learning and ML engineers behind them are and will remain in demand for years to come. This field can be a tough nut to crack, and developing the necessary skills and qualifications will be the key to getting in.

That said, if you want to be at the tip of the spear in the world of technology, the time and effort to do so are well worth the effort. Markov is a shining example in this field, providing deep data insights, automated workflows, and seamless collaboration for data teams to achieve more together, all without the need for code.

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