With a rising number of companies researching Data Science methods such as AI and ML, the demand for competent experts in these fields is growing. A Machine Learning Engineer is one of the most sought-after Data Science positions in the market.
What is Machine Learning?
Machine learning is an area of AI and computer science that uses data and algorithms to mimic how humans learn in order to increase accuracy over time.
Who is a Machine Learning Engineer?
An IT professional that specializes in researching, designing, and constructing self-running artificial intelligence (AI) systems with the goal of automating predictive models is known as a machine learning engineer (ML engineer).Machine learning engineers design and develop AI algorithms that can learn and make predictions, which is what machine learning is all about (ML).
Machine Learning engineers often collaborate with data scientists, administrators, data analysts, data engineers, and data architects as part of a wider data science team. They may also communicate with personnel outside of their teams, such as IT, software development, sales, or web development teams, depending on the size of the company.
Machine Learning engineers serve as a link between data scientists who specialize in statistical and model-building work and machine learning and AI system development. The machine learning engineer’s job includes assessing, organizing, and analyzing vast volumes of data, as well as running tests and optimizing machine learning models and algorithms.
Successful Machine Learning Engineer Characteristics:
Here are a few traits of machine learning engineers,
- 1. They’re excellent programmers: If you want to work in AI or machine learning, you’ll need to learn how to program. C++, Java, and Python are just a few of the languages that a programmer should be familiar with, but it doesn’t stop there. Machine learning languages such as R, Lisp, and Prolog have also gained popularity in the recent past.
- 2. A strong foundation in math and statistics: Machine learning is impossible to master without at least a basic understanding of mathematics. You’ll need at least a high-school level of math ability to stay up, whether you have a formal education in math and statistics or not. A formal characterization of probability and techniques derived from it are at the foundation of many machine learning algorithms. The area of statistics is closely connected to this since it provides many metrics, distributions, and analysis tools for developing and validating models from observable data. Many machine learning techniques are essentially statistical modeling procedures that have been extended.
- 3. Machine Learning Engineers think beyond the box: Curiosity drives the finest machine learning engineers. When a model or experiment fails, they’re not frustrated; instead, they’re intrigued to figure out why. They do, however, address issues quickly. Because addressing individual flaws is time-consuming and makes your models more difficult and complex to work with, the finest machine learning engineers build universal ways to repair faults and misclassifications in their machine learning models.
- 4. Machine Learning engineers are fascinated by the iterative process: By definition, machine learning is an iterative process. To be effective in this job, one must genuinely like the developing style. In order to develop a machine learning system, one must first build a very simple model rapidly, then iterate on improving it with each stage.
- 5. They have a strong data sensibility: Without data analysis, there is no machine learning. A skilled Machine Learning Engineer or Data Scientist should be able to filter through big data sets fast, recognize trends, and know how to use that data to draw meaningful and actionable conclusions.
What Jobs Are Similar to the Role of a Machine Learning Engineer?
- Data Scientist: Data Scientists work at the intersection of technology and business. A Data Scientist’s job is to understand the business difficulties that firms have and then use data analysis and data processing to uncover solutions and possibilities. A Data Scientist’s role is to sift through unstructured data for meaningful insights and then utilize that data to do predictive analysis. Data Scientists discover trends and patterns that help businesses make data-driven choices and, as a result, improve income. Data scientists should also be able to convey their findings using visually appealing visualizations.
- Data Analysts: Visualization, munging, and processing data are all things that data analysts are concerned with. Optimization is one of the most significant roles or talents of a Data Analyst, in which they build and adapt algorithms that may be used to filter data without damaging it.
- AI Engineer: Traditional machine learning techniques such as natural language processing and neural networks are used by AI engineers to create models that enable AI applications.
Why should you choose a career in Machine Learning?
- 1. Improved Career Prospects and Growth: If you want to take your career to the next level, Machine Learning can assist you. Machine Learning can also assist you in participating in something that is both global and current. Machine learning is used in a variety of fields, including image identification, health, cyber security, facial recognition, and others. As a growing number of companies realize that machine learning engineering has a significant impact on business intelligence, they are opting to invest in it.
- 2. Machine Learning Skills are in High Demand: Big data has piqued the interest of many businesses, and as a result, the demand for data specialists in the employment market has never been stronger. There have even been rumors of bidding wars for AI talent as tech behemoths compete for the best minds in the area. According to a recent analysis, 30% of questioned U.S. managers claimed their organization is now adopting AI and Machine Learning engineers, with 53% expecting to do so within the next three to five years. In other words, there’s no sign that this job market is about to dry up anytime soon.
- 3. Possibilities for Continuous Learning: The field of machine learning is still in its development. There are still a plethora of solutions, tools, algorithms, and applications that have yet to be developed and discovered. By nature, Machine Learning Engineers, like Software Developers, must value learning. And staying on top of a young and fast-changing profession requires the use of courses, blogs, tutorials, and podcasts. In fact, according to BrainStation’s 2020 Digital Skills Survey, 61% of data professionals take in-person courses, with another 60% opting for workshops. Continuing education is unquestionably a part of the field.
- 4. Careers in machine learning provide a wide range of opportunities: A Machine Learning engineering career would provide plenty of variety. Almost any industry might benefit from spending more money, time, and resources on data mining, so you could work in any field that interests you. You also have the opportunity to make a significant difference. You may be a part of a team that develops the next big thing in healthcare, cybersecurity, marketing, or self-driving cars. This is an exciting prospect for many people.
- 5. Machine Learning Jobs Pay Well: Last, but not least. Indeed named Machine Learning Engineer is the best job in 2019 for a reason: they earn an average of $148,485 in the United States, according to the site. According to Indeed’s figures, one can earn up to $200,000 in one of the country’s largest markets. Machine Learning Engineers in San Francisco made an average salary of just under $200,000, while those in New York made just under $170,000.
Basic Qualifications Required – Machine Learning Engineer in 2022:
Machine learning engineers with a master’s degree in computer science, math, statistics, or a related field are in high demand. Programming experience (e.g., Python, R, Java), understanding of machine learning frameworks (e.g., TensorFlow, Keras), and advanced mathematical skills are all gained during a master’s degree program (e.g., linear algebra, Bayesian statistics). Professional accreditation from Amazon or a recognized trade association might also help you stand out in the sector.
Skills Required – Machine Learning Engineer:
- 1. Fundamentals of Programming: Build data structures (e.g., stacks, queues, multi-dimensional arrays), use algorithms (e.g., searching, sorting, optimization), learn about computational complexity (e.g., P vs. NP, NP-complete problems, approximate algorithms), and design computer architecture (e.g., memory, cache, bandwidth).
- 2. Probability and statistics: Build and validate models from observed data using probability techniques (e.g., Bayes Nets, Markov Decision Processes, Hidden Markov Models), statistical measures, and distributions (e.g., uniform, normal, binomial), and analytical methods (e.g., ANOVA, hypothesis testing).
- 3. Machine learning algorithms and libraries: Find appropriate models to apply libraries, packages, and APIs (e.g., Spark MLlib, TensorFlow), design learning methods to fit the data (e.g., linear regression, gradient descent, genetic algorithms), and acquire an understanding of the benefits and drawbacks of various approaches (e.g., bias and variance, missing data, data leakage).
Salary Range of Machine Learning Engineers in India:
Machine Learning Engineer salaries in India range from 3.3 lakhs to 22.7 lakhs per year, with an average of 7.4 lakhs. Estimated salaries are based on 1.3k salaries paid to Machine Learning Engineers around India working in different sectors.
Summary:
Machine learning has a lot of potential job paths, and people who work in this industry earn a lot of money and have a bright future. It’s never been a better moment to start pursuing a career as a machine learning engineer
