There are some key qualifications you’ll need to become a Machine Learning Engineer. Overall, this role is responsible for designing machine learning applications and systems, which involves assessing and organizing data, executing tests and experiments, and generally monitoring and optimizing the learning process to help develop strong performing machine learning systems.
As a Machine Learning Engineer, you’ll work to apply algorithms to different codebases so experience in software development is perfect for a resume for this position. Basically, the perfect blend of math, statistics, and web development will give you the background you need – once you have a grasp of these concepts, you’ll be equipped to apply to Machine learning Engineering jobs.
If you don’t have that experience, you can still work toward a career in machine learning. First, you’ll need to first understand basic machine learning methods and the tools required to implement, use, and optimize machine learning algorithms. Many people opt to complete a data science bootcamp or machine learning course to fast-track learning these fundamentals and work toward a job as a Machine Learning Engineer.
How to become a Machine Learning Engineer in six steps.
1. Learn to Code With Python
If you’re wondering how to become a Machine Learning Engineer, you’ll need to demonstrate proficiency in Python and/or C++ and their associated libraries. Python and C++ are a couple of the most widely used programming languages for Data Scientists and Machine Learning Engineers. Get comfortable with SQL and Github to help you access company data and work collaboratively with your team.
It’s also a good idea to get familiar with Google’s TensorFlow software library, which allows users to write in Python, Java, C++, and Swift, and that can be used for a wide range of deep-learning tasks, such as image and speech recognition. It executes on CPUs, GPUs, and other types of processors. It is well-documented, and has many tutorials and implemented models that are available.
For beginners, we recommend PyTorch, a framework that can be used with the imperative programming model familiar to Developers. It allows Developers and Machine Learning Engineers to use standard Python statements and can be used to implement deep neural networks.
Here are some other programming languages you might consider learning for a machine learning career:
A free, open-source programming language that was released in 1995 as a descendant of the S programming language, R offers a top-notch range of quality domain-specific packages to meet nearly every statistical and data visualization application a Data Scientist might need, including neural networks, non-linear regression, advanced plotting, and much more.
Standing for “Structured Query Language,” SQL has been at the core of storing and retrieving data for decades now. SQL is a domain-specific language for managing data in relational databases and it’s a must-have skill for Data Scientists, who rely on SQL for updating, querying, editing, and manipulating databases and extracting data.
One of the oldest general-purpose languages used by Data Scientists, Java’s strength lies in part in its popularity and ubiquity: many companies, especially big, international companies, used Java to create backend systems and applications for desktop, mobile, or web.
User-friendly and flexible, Scala is the ideal programming language when dealing with great volumes of data. Combining object-oriented and functional programming, Scala avoids bugs in complex applications with its static types, facilitates large-scale parallel processing, and, when paired with Apache Spark, provides high-performance cluster computing.
A much newer programming language than others on this list, Julia has nevertheless made a fast impression thanks to its lightning-fast performance, simplicity, and readability. Designed for numerical analysis and computational science, Julia is especially useful for solving complex mathematical operations, which explains why it’s becoming a fixture in the financial industry. It’s also becoming widely known as a language for artificial intelligence, and many large banks are now using Julia for risk analytics.
Used widely in statistical analysis, this proprietary numerical computing language will be helpful for Data Scientists dealing with high-level mathematical needs, including Fourier transforms, signal processing, image processing, and matrix algebra. MATLAB has become widely used in industry and academia for its intensive mathematical functionality.
2. Enroll in a Machine Learning Course
Although Machine Learning Engineer is one of the most high-paying jobs you can get without extensive formal schooling, it would be very hard to break in without completing an online course, bootcamp, course, or machine learning certification program.
There is no shortage of highly regarded programs that allow students to gain a comprehensive understanding of machine learning in a short period of time. BrainStation’s Machine Learning course teaches students to apply machine learning algorithms to real-world business problems. Eventually, students use real data and select the relevant machine learning model to create a project and learn how to leverage these frameworks and tools to make decisions.
3. Try a Personal Machine Learning Project
When you’re first starting out, try reviewing and recreating basic projects provided by Scikit-learn, PredictionIO, Awesome Machine Learning, and similar resources. Once you have a solid grasp on how machine learning works in practice, try coming up with your own projects that you can share online or list on a resume.
Take on a project that interests you and requires a simple AI algorithm, and build that algorithm from scratch. There might be a learning curve, but you will learn a lot along the way and the long-term benefit is significant.
You won’t want to waste a lot of time collecting data, so try using publicly available data sets from places like the UCI Machine Learning Repository and Quandl. If you can’t come up with a project idea, look for inspiration on websites like GitHub.
4. Learn How to Gather the Right Data
AI is excellent at processing large amounts of data at once. When you’re thinking about creating AI software, think about tasks that require data points like customer service and marketing, and create a software that makes data-heavy tasks fast and easy.
You might ultimately find that building your own machine learning rig may be sensible for long-term cost savings, initially, it will be easier to spin up machine learning tailored infrastructure on a public cloud platform.
Virtual machines with underlying ML accelerators are available via each of the major cloud platforms, including AWS, Google Cloud, and Microsoft Azure. Each also offers automated systems that streamline the process of training a machine learning model, with offerings including Microsoft’s Machine Learning Studio, Google’s Cloud AutoML, and AWS SageMaker.
5. Join Online Machine Learning Communities
Kaggle is an online community for Data Scientists and machine learners. The platform allows users to find and publish data sets, build models in a web-based data science environment, communicate with other Machine Learning Engineers, and more. It’s a great way to learn from others in the field.
Kaggle also hosts a variety of machine learning challenges. Some of these are official competitions – with monetary prizes, to boot – and some are free competitions that simply provide experience.
6. Apply to Machine Learning Internships and Jobs
While personal projects and competitions are fun and will appeal to employers, you might not learn the business-specific machine learning skills required by many companies. To get that experience, search for internships or entry-level jobs related to product-focused machine learning.
An entry-level title to watch out for is Junior Machine Learning Engineer, a title that has more than 1,000 open positions on Indeed.
Is Machine Learning a Growing Field?
Yes, machine learning is a growing field – in fact, it’s one of the fastest-growing fields in technology.
According to a report from job site Indeed, Machine Learning Engineer was the best job of 2019 due to sky-high and growing demand and high salaries.
Roles such as Software Developer continue to rank highly due to a high number of job openings, but Machine Learning Engineer roles claim the top spot due to higher salaries and speedier growth.
Another AI-related job didn’t quite make the Top 10. At No. 13, Computer Vision Engineer was ranked behind Machine Learning Engineer due to slower growth (116%).
Due to the increasing use of AI in the operations of most companies, the report expects this growth to continue accelerating in the coming years.
What Is the Salary of a Machine Learning Engineer?
According to Indeed, Machine Learning Engineers earn an average salary of $146,085 with a growth rate of 344 percent from 2018 to 2019.
Even entry-level Machine Learning Engineers are handsomely rewarded. According to PayScale, the average entry-level Machine Learning Engineer makes $93,575 annually. Meanwhile, Almost Senior Machine Learning Engineers take home nearly $155,000 per year.
Why Do We Need Machine Learning?
We need machine learning because we want to automate certain processes and work. Machine learning was born from pattern recognition and the idea that computers can learn without being programmed to perform specific tasks. Researchers interested in artificial intelligence wanted to see if computers could learn from data.
The iterative aspect of machine learning is important because as machine learning models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.
While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development.
Why Is Machine Learning Important?
Machine learning is important because by using algorithms to build models that uncover connections, organizations are making better decisions without human intervention.
Machine learning is basically a mathematical approach, where the system analyzes data (images, sound files, texts, for instance) for certain patterns. The trick is that the system figures out on its own which patterns to look for (usually by analyzing thousands of examples). The system implicitly learns the rules (e.g. for identifying an elephant) which we are struggling to write down explicitly.
Most industries working with large amounts of data now recognize the value of machine learning technology. By gleaning insights from this data – usually in real-time – companies are able to work more efficiently or gain an advantage over their competitors.
Also, government agencies such as public safety and utilities have a particular need for machine learning solutions since they have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine learning can also help detect fraud and minimize identity theft.
What Is Machine Learning Used For?
Demand for machine learning is growing fast and is already used for many things by many industries, including the financial sector, retail, the transportation industry, the oil and gas industry, and even the automotive industry (for self-driving cars). Some applications of machine learning include detecting fraud and minimizing identity theft, finding new energy sources, and making truck routes more efficient.
Banks and other businesses in the financial industry also use machine learning technology to prevent fraud. Machine learning insights also help banks identify investment opportunities, or help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cybersurveillance to detect warning signs of fraud.
In the retail industry, websites use machine learning to recommend items you might like based on your buying history. Retailers rely on machine learning to capture data, analyze it, and use it to personalize a shopping experience, implement a marketing campaign, price optimization, merchandise supply planning, and for customer insights.
Further, machine learning is a fast-growing trend in the healthcare industry, thanks to the advent of wearable devices and sensors that can use data to assess a patient’s health in real-time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnosis and treatment.
What Jobs Can I Get in Machine Learning?
People who specialize in machine learning can have a number of different titles and jobs, including:
- Machine Learning Engineer
- Data Engineer
- Data Scientist
- Software Engineer
- Machine Learning Researcher
- NLP Scientist
- Business Intelligence Developer
Let’s take a look at each position:
Machine Learning Engineers run various machine learning experiments using programming languages such as Python, Scala, and Java with the appropriate machine learning libraries. Some of the major skills required for this are programming, probability and statistics, data modeling, data structures, machine learning algorithms, and system design.
Data Scientists analyze data in order to produce actionable insights, which are then used to make business decisions by the company executives. They use advanced analytics technologies, including machine learning and predictive modeling to collect, analyze, and interpret large amounts of data.
A lot of people confuse Data Scientists and Machine Learning Engineers. To put it simply: a Data Scientist creates the required outputs for humans while a Machine Learning Engineer creates them for machines.
NLP Scientists (or Natural Language Processing Scientists) give machines the ability to understand human language. This means that machines can eventually talk with humans in our own language.
An NLP Scientist essentially helps in the creation of a machine that can learn patterns of speech and also translate spoken words into other languages. Thus a good NLP Scientist will be fluent in the syntax, spelling, and grammar of at least one language in addition to machine learning so that a machine can acquire the same skills.
Business Intelligence Developers use data analytics and machine learning to collect, analyze and interpret large amounts of data and produce actionable insights that can be used to make business decisions by the company executives. (In simpler words, using data to make better business decisions).
To do this efficiently, a Business Intelligence Developer requires knowledge of both relational and multidimensional databases along with programming languages such as Scala, SQL, Python, and Perl. Some experience with business analytics services such as Power BI would also be an asset.
How Do I Get a Job in Machine Learning?
To get a machine learning engineer job, you’ll need to learn how to collect data, how different algorithms process data, how to diagnose results, and how to demonstrate business value to the organizations. These elements come with time, taking courses, and work experience.
A background in computer science fundamentals, computer programming, software engineering, robotics, or deep learning will also help you land a coveted Machine Learning Engineer role.
Aside from education in one of these fields, there are multiple training programs you can take to help you build a niche expertise in machine learning specifically. These certificate courses will help take those proficient with math, development, or science and push them in the direction of a career in machine learning.
A high-quality machine learning course will teach you the foundational skills so that you have a comprehensive understanding of how machine learning and artificial intelligence works and how you can bring that technical perspective to the workplace. You’ll also be taught how to apply machine learning to real business problems and use real data to help leverage the decisions to these problems.
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