For the past few decades, Machine Learning (ML) has transformed our lives. From clicking pictures with a blurry background & focused face to virtual assistants such as Alexa and Siri answering our queries, we are significantly dependent on applications that execute ML at their core.
ML engineering combines data science and software engineering. A data scientist will examine the obtained data extract actionable insights; an ML engineer will develop the self-running software that leverages the extracted data and automates predictive models.
These engineers are experienced in basic data science skills like quantitative analysis methods, stats, data structures & modelling, and developing data pipelines, while also having fundamental software engineering skills.
With so much happening around the breakthrough technology, it is no wonder that any enthusiast who is keen on advancing their career in software technology & programming would choose ML as a base to set their job.
If you are keen on shaping your career with ML but not familiar with critical skills, then this blog is for you. We will be taking you on tour specifying the essential skills required to be an ML engineer. So, keep reading till the end.
Technical Skills for ML Engineers
We have learned how ML application operates, followed by numerous job opportunities in the IT field for software engineers and data scientists. To be a part of ML technology, you need specific technical and soft skills.
Firstly, we will see technical skills required for an engineer, and they are:
Neural Network Architecture
Neural networks, also called Artificial Neural Network (ANN) or Simulated Neural Network (SNN), are the predefined algorithm sets used for ML task implementation.
They provide models and play a vital role in this futuristic technology. Now, ML seekers must be skilled in neural networks because it offers an understanding of how our brain works and assist in model & simulating an artificial one. It also provides in-depth knowledge about parallel and sequential computations.
Some of the neural network areas that are essential for ML are:
- Boltzmann machine network
- Convolutional neural networks
- Deep auto-encoders
- Long short-term memory network (LSTM)
- Perceptron
Natural Language Processing (NLP)
It is a branch of linguistics, AI & computer science that, when combined with ML, Deep Learning (DL), and statistical models, enables computers to process human language in the form of spoken words and text and understand its whole meaning with writer's intent.
Several techniques and libraries of NLP technology used in ML are:
- Word2vec
- Summarization
- Genism & NLTK
- Sentiment analysis
Applied Mathematics
ML is all about creating algorithms that can learn data to predict. Hence, mathematics is significant for solving data science projects DL use cases. If you wish to be an ML engineer, you must be an expert in the following math specializations.
But why math? There are several reasons why an ML engineer needs math or should depend on it. For instance, choosing appropriate algorithms to suit the final outcomes, understanding & working with parameters, deciding validation approaches, and estimating the confidence intervals.
If you are wondering about the math proficiency level one must hold to be an ML engineer, then it depends on the level at which the engineer works. The below-shown pie chart will give you an idea of how significant various math concepts are for an ML engineer.
Data Modeling & Evaluation
An ML has to work with a colossal amount of data and use them in predictive analytics. In such a scenario, data modelling & evaluation becomes beneficial in dealing with these bulks and estimating the final model's good.
Hence, the following concepts are must learn skills for an ML engineer:
- F1 Score
- Log loss
- Mean absolute error
- Confusion matrix
- Classification accuracy
- Area under curve
- Mean squared error
Video & Audio Processing
This processing concept is different from NLP because audio & video processing can only be applied to audio signals. For this, the following ideas are essential for an ML engineer:
- TensorFlow
- Fourier Transform (FT)
- Music theory
Advanced Signal Processing Techniques
Signal processing targets analyzing, modifying, and synthesizing signals to minimize noise and extract the provided signal's best features. For this, the techniques leverage certain concepts like spectral time-frequency analysis, convex optimization theory & algorithms, and algorithms (bandlets, shearlets, curvelets, wavelets, etc.)
Reinforcement Learning
Reinforcement learning is an ML area that takes suitable action by employing several machines and software to increase rewards in a particular scenario. Though it plays a vital role in understanding and learning DL & AI; however, it is beneficial for ML beginners to have an insight into the fundamental concept of reinforcement learning.
Soft Skills for ML Engineers
While ML engineering is a technical job, soft skills such as problem-solving, collaboration with others, communication, time management, etc., are what lead to successful completion and delivery of the project.
Here are some of the soft critical skills an ML engineer must possess:
Team Work
ML engineers are often at the core of AI initiatives within a company, so they naturally work with software engineers, product managers, data scientists, marketers and testers. The potential to work closely with others and contribute to a supportive working environment is a skill many recruiters seek in ML engineers.
Problem-solving
The potential to solve an issue is a significant skill required for both software & ML engineers and data scientists. ML focuses on solving challenges in real-time, so the potential to think creatively and critically about the problem and develop solutions accordingly is a fundamental skill.
Open to New Learning
The fields of ML, AI, DL and data science are drastically evolving, and those who have earned a degree and working as an ML engineer find ways to learn new things through workshops, boot camps and self-study.
Whether learning the latest programming languages or mastering new tools, the most effective ML engineers are open to new learning skills and constantly refreshing their learnt toolkits.
Communication
ML engineers must possess excellent communication skills when communicating with shareholders regarding the project objectives, timeline, and expected delivery. We know that ML engineers collaborate with data scientists, marketing & product teams, research scientists, and more; hence, communication skill is crucial.
Domain Knowledge
To develop self-running software and optimize solutions leveraged by end-users and businesses, ML engineers should have an insight into the requirements of business demands and the type of issues the software is solving.
Without domain knowledge, an ML engineer's recommendation may lack accuracy, their task may overlook compelling aspects, and it might be strenuous to evaluate a model.
Programming Skills for ML Engineers
Machine learning is all about coding and feeding the machines to carry out the tasks. ML engineers must have hands-on experience in software programming and related subjects to provide the code.
Let's see the programming skills an ML engineer is expected to have knowledge on:
ML Algorithms & Libraries
ML engineers are expected to work with myriads algorithms, packages, and libraries as part of a daily task. ML engineers must be skilled with the following ML algorithms and libraries:
- Knowledge in packages & APIs - TensorFlow, Spark MLlib, scikit-learn, etc.
- Decide and choosing of hyperparameters that impact the learning model & the result.
- Algorithm selection provides the best performance from support vector machines, Naive Bayes Classifiers and more.
- Expert in model handling like decision trees, neural net, SVMs and deciding which is suitable.
Unix
ML engineers require most servers and clusters to operate are Linux (Unix) variants. Though they can be performed on Mac & Windows, more than half of the time, they are required to run on Unix systems only. Therefore, having good knowledge of Linux & Unix is vital to being an ML engineer.
Computer Science Fundamentals & Programming
Engineers must apply the concepts of computer science and programming accurately as per the situation. The following ideas play a significant role in ML and are a must on the skillset list:
- Algorithms: search, sort, optimize, dynamic programming
- Computer architecture: memory, bandwidth, cache, distributed processing and more.
- Data structures: queues, trees, stacks, graphs and multi-dimensional arrays
- Complexity & computability: big-O notation, P vs NP, approximate algorithm, etc.
Distributed Computing
Being an ML engineer means working with massive data sets and focusing on one isolated infrastructure, and spreading among system clusters for data sharing. In such a situation, these engineers must know the concept of distributed computing.
Software Engineering & System Design
ML engineers must have sound knowledge of the following areas of software programming & system design, as all they do is code:
- Top-notch measures to circumvent bottlenecks & develop user-friendly outcomes.
- Algorithm scaling with data size.
- Interacting with different working components and modules using library calls, REST APIs and database queries.
- Fundamental software design methodologies and coding like testing, requirement analysis and version management.
Key Programs to Master for ML Engineers
In addition to an in-depth knowledge of programming languages such as SQL, C++, Python and Java, several ML engineers are also experts in the following tools:
- AWS ML
- IBM Watson
- TensorFlow
- R
- MATLAB
- Google Cloud ML Engine
- Weka
- Hadoop
- Apache Kafka
Final Thoughts
Knowing ML and DL concepts is necessary but not enough to get recruited. The technologies are evolving to new heights each day, and ML has been amplifying its growth. Global companies are heading towards applying AI, ML & DL in their sectors to scale up. This futuristic trend highlights how ML plays a vital role in online services, and mastering the necessary skills will keep you on the path where opportunities are boundless.
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