These days, machine learning is used everywhere – in our Netflix queue, our Amazon product suggestions, the self-driving cars of today and tomorrow, and everything in between. As innovation, cloud computing, and capacity continues to grow, so will the demand for skilled machine learning engineers to meet the exponential potential of artificial intelligence (AI).
Starting a career in machine learning requires discipline, commitment, and a love of mathematics, algorithms, and data analytics. Yet, the road to become a machine learning engineer is not easy. Today we will explore how to start a career in machine learning, and how a data science machine learning course through Lambda School can help get you closer to your goals.
Machine learning is a branch of AI focused on building applications that learn from data and make improvements over time without being explicitly programmed. Within machine learning, algorithms make predictions about future data based on features and patterns within current data. The better the algorithm, the more accurate the decisions and predictions will be as the algorithm processes more data.
Machine learning is usually implemented in one of four ways:
1. Supervised machine learning algorithms. Here a machine learning engineer labels data and sets strict boundaries upon how the algorithm operates through a process called classification. The computer is then presented with example inputs and their desired outputs, with a goal to identify patterns and calculate predictions of inputs to outputs through a process called regression. Common examples of supervised learning include stock trading, sales forecasting, and retail commerce.
2. Unsupervised machine learning algorithms. Here machine learning engineers do not assign labels to the learning algorithm, leaving the algorithm itself to find structure, insights, and patterns in the unlabeled data, or to improve efficiency. Common examples of unsupervised machine learning include use in the field of fraud detection, genetics, and the digital marketing field to find patterns in consumer browsing and shopping habits.
3. Semi-supervised machine learning algorithms. Here machine learning engineers create a middle ground between supervised and unsupervised algorithms to increase the size of their training data. Machine learning engineers train with a limited set of labeled sample data, resulting in a partially trained model which can be used to label the unlabeled data. Although the accuracy of this model has limitations, it can be especially helpful when machine learning engineers don’t have enough labeled data to produce an accurate model to begin with. A common example of semi-supervised machine learning is automated transcription, in which an algorithm uses labeling to give the program an analytic speech model to predict words and speech patterns, turning unstructured data into useful information.
4. Reinforcement machine learning algorithms. Here machine learning engineers work with AI systems creating self-sustaining, continuous sequences based on “exploration and exploitation” toward a certain goal. Taking core principles from behaviorist psychology, data is observed, and successes and failures are learned upon through positive and negative reward signals. The most newsworthy example of reinforcement learning to date are the potential applications in self driving cars, including in trajectory optimization, motion planning, controller optimization, dynamic pathing, and scenario-based learning policies for highways. While the cars of today may only be able to achieve auto-park, the cars of tomorrow will require deep machine learning and talented developers to drive innovation.
Machine learning engineers sit at the intersection of software engineering and data science. Not only do they build algorithms, enabling machines to identify patterns in programming data that spur self-learning, they also collaborate with teams of data scientists, computer engineers, and developers to create cutting edge AI technology. Other members on a machine learning engineer’s team focus on presentation and analysis, but the key output for a machine learning engineer is the working software.
Machine learning engineers use big data and programming frameworks to ensure the raw data gathered from data scientists is redefined and ready for production. Additionally, machine learning engineers improve and feed data into models defined by data scientists, for instance by using theoretical data science models and scaling them out to production-level models in order to handle large amounts of data.
Some typical machine learning engineer responsibilities include:
Although machine learning engineers oversee a very small part of the machine learning system, their role is crucial in impacting how autonomously the software components run on their own. They also act as a leader on the team, making sure production tasks are executed and running on time, encouraging constant performance improvement to the production environment, and bringing best software development practices and operational architecture to their teams to improve efficiency.
Machine learning traverses almost every field, from tech to healthcare to construction to education. Perhaps most compelling about machine learning is its almost limitless growth potential for machine learning engineers now and well into the future. Machine learning engineers are one of the most lucrative careers in the tech sector as well. According to Indeed, the average base salary for a machine learning engineer in 2021 is $150,422. So, what are the best ways to start a career in machine learning?
A machine learning engineer is unique in the tech field due to the specialization needed to fulfill the role. Machine learning engineers must have an advanced knowledge of mathematics to define data patterns and recognize different types of data sets, and must be proficient in data science, statistics, and software engineering. The primary requirements to enter the field include a background in either mathematics, data science, computer science, or computer programming, with possible additional knowledge or training in statistics or physics, depending on the industry.
Typically, those entering a career in machine learning will start their path as a software engineer, software programmer, software developer, computer engineer, or data scientist. Some may choose a more traditional and costly route through a 4-year university to gain a degree as a first step, while others opt for less time intensive and often less costly online coding programs or bootcamps. Some online coding bootcamps offer data science machine learning courses, while other coding programs may offer machine learning data science certificates.Some aspiring machine learning engineers who opt to tech themselves through a la carte online programs may pick and choose individual classes that fit a curated machine learning curriculum, such as Python or R, but may struggle to find support, mentorship, or all of what they need on their own.
However, Lambda School’s innovative model is different from the rest. Their data science program includes data science and machine learning curriculum, along with databases, SQL, statistics and modeling, data visualization, linear algebra, natural language processing, and frameworks and languages such as Python that will establish a solid foundation in machine learning. In addition, a Lambda data science online course provides mentorship and instruction from top industry experts along with real-world labs, giving students opportunities for hands-on learning and portfolio building. Finally, accessible tuition options through a Lambda data science online course gives aspiring machine learning engineers the flexibility to defer tuition until they are hired at a qualifying position.
To prepare for the transition from data scientist to machine learning engineer, aspiring machine learning engineers must be prepared to demonstrate a foundation in:
After training or working in the field as a software engineer, software programmer, software developer, computer engineer, or data scientist, aspiring machine learning engineers may opt to pursue an additional degree or certificate in machine learning. This can be through a Masters’ or Ph.D. program, or through additional online certification. Still, with the right resources, experience, and foundation of learning, others receive training on the job and work their way into machine learning roles. What is clear, however, is that machine learning engineer roles require extensive training, a clear skillset, and a next level love for mathematics, data analytics, and algorithms.