How to Become a Machine Learning Engineer

In today’s dynamic world of high-paying tech jobs, you would have stumbled on roles such as “ML(Machine Learning) Engineer” | “ML Researcher” | “NLP Scientist”; and more. These on-demand technology roles fall under the purview of being a Machine Learning Engineer/Expert, and in this article, we will discuss how you can land a machine learning engineer role with no prior work experience.

Before we jump into the “HOW,” let us first understand the “WHAT(s)” and the “WHY.”

What is machine learning?

As the name Artificial Intelligence(AI) suggests, AI is the simulation of human processes/intelligence by computer programs or machines. For example, AI is utilized virtually everywhere; the Siri voice assistant on Apple devices, the recommendation engine created by Netflix to recommend the “perfect” shows for you, or the self-driving cars we are trying to perfect. They run complex AI programs to ensure safety and better driving.

What does that have to do with machine learning? Yes, fair question.

Machine learning is one of the most important components of artificial intelligence. In large part, the “intelligence” in artificial intelligence is powered by machine learning. ML is the process of enabling a system to iteratively process large amounts of data and data points – text, images, audio, video, etc – and make increasingly intelligent decisions with each iteration cycle. In essence, ML is the process of helping a machine learn by itself(hence the name “machine learning”) as it continuously ingests and processes data. The interesting aspect of ML is that the computers aren’t explicitly programmed to compute complex mathematical problems, akin to traditional programming. Instead, the system is presented with the input parameters and the desired output. The machines are allowed to run their programs or algorithms to derive said output in the shortest time possible, considering all possible scenarios.

ML models are dichotomized into supervised learning and unsupervised learning. Supervised learning, as the name suggests, requires manual intervention to provide input data for the ML models to parse, understand, and identify patterns from said data. Whereas unsupervised learning, or “Deep Learning”, as it is called, leverages neural networks (a system structure that resembles the human brain by comprising intricately interconnected nodes akin to our brain’s neurons), which run multiple algorithms simultaneously to continually learn based on the nested layers available within the input data.

ML models are ideal for open-ended, repetitive challenges such as recommendation generators, language processing, and more. In such cases, understanding behavioral data, contextual clues, and emotional cues is essential for an algorithm to suggest intelligent solutions.

Why is machine learning in demand?

As the prevalence of artificial intelligence and our dependence on self-learning systems grows, so does our dependence on machine learning as a concept. For instance, have you ever wondered how Siri could understand an Indian’s English dialect as accurately as an American’s? By leveraging Natural Language Processing, systems are trained to process linguistic, contextual, and emotional inputs and respond in kind while presenting the information we seek.

The demand for machine learning is high and will continue to remain so because it is widely used for automating repetitive tasks, analyzing large swaths of unstructured data to augment business intelligence, generating images/text to improve accessibility for the differently abled, and creating accurate predictive models that can be programmed with specific parameters to identify optimal outcomes, enabling businesses to recalibrate their course of action.

In short, machine learning is at the intersection of data science, artificial intelligence, and software development, and it will always be in demand.

(If you have read this far, kudos, you seem interested in the role. (Keep reading till the end, and you will surely find it helpful.)

What is the role of a machine learning engineer?

A machine learning engineer will be responsible for developing the algorithms, or machine learning methods, as they are called, to assess and optimize data, execute tests and experiments, and monitor and optimize the algorithms to facilitate progressive intelligence with each iteration.

A typical ML engineer’s job description:

• Design, develop, and research machine learning methods and models in alignment with the data team’s objective
• Understand and transform data science prototypes into workable machine learning models
• Select the appropriate datasets for performing comprehensive data collection and modeling
• Perform statistical analysis to continually improve models
• Enable reinforcement learning methods to retrain ML systems as needed
• Identify data distribution differences that could affect model performance in real-world situations
• Data visualization for deep contexts
• Analyzing various ML algorithms to rank them on their success
• Understanding when you can apply your findings to business decisions
• Enrich existing ML frameworks and libraries
• Verify data quality and/or ensure it via data cleaning

If you do not understand any of these terms now, don’t worry. It is presented to give you an idea of the responsibility a machine learning engineer is expected to handle.

What coding and technology skills is an ML engineer expected to have?

As mentioned above, an ML engineer is at the intersection of software engineering, data science, and artificial intelligence.

1. Software Engineering Skills – An ML engineer’s fundamental function is to write algorithms to search, sort, store, and optimize data. Familiarity with approximate algorithms, data structures such as stacks, queues, arrays, strings, and overall knowledge of software architecture is essential.
2. Data Science Skills – Considering ML engineers will be working with large volumes of data, familiarity with programming languages such as Python, SQL, and Java is essential. Furthermore, proficiency in mathematics, specifically statistics and statistical models such as Naive Bayes classifiers, likelihood, Bayes rule, Bayes nets, and Hidden Markov Models, is important.
3. Machine Learning Techniques – Many ML engineers are also trained in deep learning, image processing, NLP, dynamic programming, neural network architecture, and reinforcement learning. Knowledge of ML techniques is surely preferred for an ML engineer.

Apart from these skills, here are a few non-negotiable skills for you to possess:

1. Communication – As an ML engineer, you will work with multiple teams across functions and levels. It is imperative that your communication skills are strong, both written and spoken.
2. Analytical Problem-Solving – If you want to see success as an ML engineer, you need to be able to analyze and identify solutions for bottlenecks and complex algorithmic and mathematical problems.
3. Presentation – You need to be able to present and speak confidently about numbers and solutions to persuade your teammates to make decisions based on your predictions.

How can I become an ML engineer?

The path to becoming an ML engineer is similar to any other role. It follows the same journey:

Skills —> Projects —> Networking —> Opportunities

1. 1. Get certified – Whether or not you have a bachelor’s degree in computer engineering, mathematics, or statistics, as most organizations prefer, undertake courses on platforms such as Data Quest, Kaggle, LinkedIn Learning, Coursera, etc. to get yourself certified. There are entire learning paths integrated with projects, which take you through the whole journey from start to finish, or there are specific courses to pick up individual skills. Depending on your priorities, you choose.
   2. Projects. Portfolio. Network. – When it comes to ML, it is important to demonstrate expertise owing to the tangibility of the skills and competencies required. Register yourself on platforms such as Kaggle to participate in community activities, ML marathons, and projects to gain hands-on experience and learn from your experienced peers. It will also help you connect with seniors in the same field and seek their references if you apply for roles within their organizations.
   3. Seek Internships – The best way to gain hands-on work experience is to seek internship opportunities and learn from experts across various organizations. It will also help you understand what skills you need to bolster.
   4. Apply – As a fresher, you would be best suited for entry-level ML roles such as:
      • a. ML Engineer – Entry Level
      • b. Data Engineering Associate
      • c. Jr. Machine Learning Engineer

   Leverage platforms like LinkedIn or register on our career portal for regular updates on in-demand tech roles across organizations.

   As we mentioned earlier, ML engineers are well-paid and sought-after for the unique blend of skill sets they bring to the table. In India, according to Glassdoor, the national average salary for ML engineers is Rs 9,55,000 per annum.

   What are you waiting for? Begin your journey to becoming an ML engineer now, for the world will always want you.