This blog will help you understand the various technical and non-technical skills required to become a Machine Learning (ML) Engineer in a renowned organization in the year 2020.
To acquire these skills and become a Machine Learning Engineer, you must register for an Machine Learning online course wherein you will learn the key concepts of ML and attain all the necessary skills that are required to become a successful ML Engineer.
First, let’s discuss what Machine Learning is and who Machine Learning Engineers are.
What is Machine Learning?
Machine Learning (ML) is an advanced technology that offers systems the ability to automatically acquire knowledge and make necessary improvements, without being explicitly programmed, from past data and experiences. It is a subset of Artificial Intelligence (AI) that allows you to develop computer programs that can access the organization’s data and use it to learn on their own with the help of deep learning algorithms and technologies.
Now, let’s find out about Machine Learning Engineers and their job role.
Who is a Machine Learning Engineer?
Machine Learning Engineers are programmers that build systems and machines which have the capability of learning and applying gained knowledge without receiving any specific guidance. They use numerous ML algorithms and libraries in order to develop these systems.
Let’s read in detail about the various roles of a Machine Learning Engineer.
Role of a Machine Learning Engineer
The major roles of a Machine Learning Engineer in an organization include:
- Research and design ML systems
- Understand and further transform the Data Science prototypes
- Verify the quality of data and ensure it through data cleaning
- Perform experiments and tests on ML models
- Develop ML models as per the business requirements
- Perform various training models before tuning the hyperparameters
- Fine-tune the results of testing and perform statistical analysis
- Implement the correct ML algorithms
- Select the correct training data sets to develop the Machine Learning models
7 Popular Jobs in Machine Learning
There are numerous jobs available for Machine Learning professionals today. Following are some of the most popular job opportunities for these professionals:
- Machine Learning Engineer
- Data Scientist
- Software Engineer
- AI Engineer
- NLP Engineer
- Machine Learning Quality Analyst
- Deep Learning and AI Researcher
In recent years, the demand for ML Engineers has grown immensely. As per a report by Gartner, there would be over 2.3 million Machine Learning job opportunities in the year 2020.
Skills Required to Become a Machine Learning Engineer
There are numerous technical and non-technical skills required in order for you to become a successful Machine Learning Engineer.
5 Technical Skills that you need to have to Become an ML Engineer
Let’s read about the five technical skills that you need to have in order to become a Machine Learning Engineer.
Programming Skills and Fundamentals of Computer Science
In order to become a Machine Learning Engineer and have a promising career in this technology, you must have a background in the Computer Science field. Graduates or professionals, like you, who are looking for Machine Learning jobs must have a good understanding of the following:
- Programming languages such as Python, Java, R, and C++
- Data Structures concepts like queues, stacks, trees, graphs, etc.
- Searching, sorting, dynamic programming, optimization, and other such algorithms
- Complexity and computability of big-O notation, P vs NP, NP problems, approximate algorithms, and more
- The computer architecture including cache, memory, deadlocks, bandwidth, and distributed processing
You should have the ability to apply and implement these skills while programming.
Statistics and Probability
You need to be familiar with mathematical and statistical concepts such as algebra, calculus, mean, median, mode, vectors, standard deviation, matrices, matrix multiplication, etc. Further, you must have an understanding of conditional probability, likelihood independence, and Bayes rule along with techniques such as Markov Decision Processes, Gaussian Mixture Models, Bayes Nets, and Hidden Markov Models. In addition, knowledge of mean, median, mode, and analysis methods like hypothesis testing, and ANOVA is also required.
Data Modeling and Evaluation
Data Modeling is used to estimate and find useful patterns like clusters, correlations, etc. from the underlying structure of the dataset provided or to interpret properties of instances like their classification, anomaly detection, and regression. The main focus of this process is to continuously evaluate the model given. Based on the given task, you will be required to choose the suitable error measure—sum-of-squared-errors for regression, log-loss for classification, etc.—and evaluation strategies such as sequential vs. randomized cross-validation and training-testing split.
Redundant learning algorithms generally use resulting errors directly to tweak the data model. This is why it is necessary for you to learn these measures in-depth and also apply standard algorithms.
Applying ML Libraries and Algorithms
As an ML Engineer, you would not have to keep developing new algorithms. Rather, you must learn to use the different libraries and algorithms that have already been developed. There is a range of libraries and APIs like Microsoft’s Cognitive Toolkit (CNTK), Google TensorFlow, and Apache Spark MLlib. To apply these technologies, you need to have a clear understanding of all these concepts and also, learn how you can integrate them with various systems.
System Design and Software Engineering
To have a promising career in ML, you must have a strong base in system design and software engineering. You should have the ability to develop suitable interfaces for your system component. Besides, you should also have knowledge of modularity, requirements analysis, testing, version control, and documentation to improve the quality, productivity, and maintainability.
3 must-have Non-Technical Skills to be a Machine Learning Engineer
You will now learn about the must-have non-technical skills to become a Machine Learning professional.
Knowledge of Industry
Those projects that address real-life issues are the most successful ML projects. Further, for the benefit of your business organization, you must have good knowledge of your industry and how it works. If you do not have a good understanding of the elements that help your organization succeed then you would not be able to channel your technical skills effectively and productively. Moreover, you may not be able to detect the issues and challenges that need to be solved for the growth and sustainability of the business.
Effective Communication Skills
You will be responsible for explaining various ML concepts to employees that have negligent knowledge of this technology. With good communication skills, you will be able to deliver your knowledge more thoroughly which will allow your team to get a clear understanding of ML concepts.
Organizations, that hire ML Engineers, look for professionals who can translate their technical knowledge fluently and clearly to both technical and non-technical teams.
Keep Yourself Up-to-date
You need to stay updated with respect to any new and upcoming technologies and technological changes. New neural network models are released every month and they all outperform the previous version and architecture. Also, you must be aware of the various news based on the development of new algorithms and tools via blogs, research papers, and conferences.