Machine Learning (ML) is the branch of Artificial Intelligence in which we use algorithms to learn from data provided to make predictions on unseen data. Recently, the demand for Machine Learning engineers has rapidly grown across healthcare, Finance, e-commerce, etc. According to Glassdoor, the median ML Engineer Salary is $131,290 per annum. In 2021, the global ML market was valued at $15.44 billion. It is expected to grow at a significant compound annual growth rate (CAGR) above 38% until 2029.
A Machine Learning Engineer is an expert programmer who designs, develops, and deploys systems to automate predictive modeling. ML Engineer is part of a larger team, including data scientists, engineers, business analysts, and stakeholders. He is an expert in manipulating large datasets, iterative modeling experimentation, and optimizing machine learning models. This blog will explore what an ML engineer does, their qualifications, and their career path to becoming a Machine Learning engineer.
Roles and Responsibilities of a Machine Learning Engineer
Roles and responsibilities of an ML Engineer vary across industries, depending upon the size of the organization and the availability of other roles, including data scientists, data engineers, and data analysts. Generally, an ML Engineer performs the following tasks:
- Understanding the business problem and the end goal
- Preprocessing data to make it model-ready.
- Performing Statistical tests and Exploratory Data Analysis for deeper insights
- Selecting the appropriate model and techniques for data
- Evaluating the model performance
- Optimizing the machine learning systems
- Communicating the results with data scientists, data engineers, and stakeholders
- Making the trained model available to make predictions or decisions for live applications or services
- Ensuring the deployed model’s scalability, versioning and logging
- Monitoring and resolving issues related to deployed model
- Staying in touch with latest research and algorithms
Skills Required to Become a Machine Learning Engineer
Mastering Programming concepts in Python or R is the most important technical skill for an ML Engineer. Expertise in libraries and frameworks is necessary, which are as follows:
- NumPy (Scientific computing)
- Pandas (Data Manipulation)
- Matplotlib/Seaborn (Exploratory Data Analysis)
- Scikit-learn (Machine Learning Modeling)
- PyTorch/TensorFlow/Keras (Deep Learning Modeling)
- Apache Spark (Data preprocessing)
Machine Learning Concepts
Understanding of the commonly used algorithms is required to become an ML Engineer. These algorithms and concepts are as follows:
- Linear Regression, Logistic Regression, Decision Trees and Neural Networks
- Supervised, Unsupervised, and Semi-supervised Learning
- Gradient Descent
- Overfitting and Underfitting
- Classification, Regression, Anomaly Detection, and Clustering
Moreover, an understanding of SQL, Git, and Cloud Computing is also necessary.
Mathematics (Statistics and Probability)
Since Statistics and Probability are the base of many ML algorithms, a firm grip on mathematics is a prerequisite. Probability is used in many algorithms and techniques, such as Hidden Markov Model, Naive Bayes Algorithms, and conditional probability. Key statistics concepts include Probability Distributions (Normal, Poisson, Uniform distribution, etc.), measures of central tendency and spread, Hypothesis Testing (ANOVA, chi-squared, etc.), Bayesian Inference, etc.
Soft skills are equally crucial for an ML Engineer. He should be able to communicate and cooperate with other team members, such as Data engineers and Data Analysts. He should have creativity and problem-solving skills to solve problems in ML Cycle and adaptability to learn new technology. Project Management skills are essential to handle and completing Projects efficiently.
Machine Learning Engineer Career Path
Machine Learning offers a promising career. The following path can be adopted to build a career in it:
Bachelor’s in Computer Science or a relevant Program
Many companies require candidates to have a Bachelor’s degree. ML Engineers come from diverse backgrounds; a Bachelor’s degree in computer science, mathematics, or statistics would set up a firm ground for someone pursuing a career in this field.
Various well-known Edtech platforms offer machine learning courses and programs. Completing these courses will help you learn programming and machine learning. It will also enhance your credibility as an expert professional.
Building a Portfolio
Building a Portfolio using the skills you have gained is the most crucial part of the ML Engineer Career Path. For this, take real-world datasets and pass them through the machine learning life-cycle. After deploying it, document your results on GitHub or via blog post. Make multiple portfolio projects to add prototypes to your resume.
To conclude, Machine Learning Engineer is a continuously evolving role. Every year, groundbreaking research and products are brought forward by research institutes. A person pursuing a career in this field should dedicate himself to life-long learning and adapt as technology improves.
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