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How to Become a Successful Machine Learning Engineer in 2021?

How to Become a Machine Learning Engineer

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How to Become a Successful Machine Learning Engineer in 2021?

The machine learning industry is rapidly progressing by leaps and bounds with innovation becoming a key factor in the process. It won’t be an overestimation to say that the daily developments and progress have pushed the boundaries of Machine Learning and Artificial Intelligence a bit further.

If you are someone who is part of machine learning phenomenon in an engineer capacity, it can be become rather challenging for you to keep up with the progress of advancement that is rapidly moving ahead within the industry. Even if you lag behind for a short period, you can end up setting back months or years behind the boundary of ML.

“I’ve learned over the years that when it comes to success, consistency is key. Consistent hard work that we may not like doing today, but for a payoff, we’ll love tomorrow. Earn it. Enjoy it.”

— Dwayne Johnson

As per specifics revealed by Glassdoor, the average base earning for a machine learning engineer is more than $114,000 annually in the US as we enter the New Year 2021. Besides, you can also enjoy more benefits, such as bonuses and equity, which can help you to earn more than your base pay as your career as a machine learning engineer moves on successfully. 

Let’s shed some light on the essentials facts associated with becoming a successful machine learning engineer. 

What is Machine Learning and Why is it such a Big Deal?

Machine learning is one-of-a-kind technique of creating innovative systems that can successfully analyse pre-existing data, and successfully learn patterns as well as make decisions or predictions and classifications or any other relevant tasks based on similar data, with least human involvement. 

What is Machine Learning

To put it simply, ML is the technique where we are training a computer to learn from previous data in order to carry out tasks for us in a comparatively better way in the future.

As far as machine learning is concerned, it differs from the conventional programming in so many ways. In the case of traditional programming, a human workforce enters data into a computer while developing a program that is specifically designed to alter that particular data into the preferred output. 

On the other hand, we provide input for the machine in case of machine learning and depending on what the previous data provided. The machine successfully develops its own logic based on the preferred output. 

The key point is that the machine learns without any human interference in MI. It may look like something that’s restricted to research work, but it’s a technology that we can easily witness in many applications that we come across in our daily life. Moreover, machine learning is not programmed but rather taught with data. 

What is a Machine Learning Engineer?

Whenever a job involves the word “engineer”– this can easily tell you that the nature of the job is hands-on, and the person hiring you is expecting that you can easily write code on a daily basis. It’s not just limited to building models and relating only to theory. 

What is a Machine Learning Engineer

The basic job of a machine learning engineer is to actually build machine learning systems and to put the build models into production. One of the biggest companies in the world, such as Amazon also hires MI Scientists that focus on theory mainly. However, its software engineering roles will need some kind of machine learning. 

It is also worth mentioning here that a machine learning engineer also is not a data engineer. As far as a data engineer is concerned, he/she is has a more precise role that mostly focuses on accumulating and transforming data prior to analysis. On the other hand, a machine learning engineer needs a thorough understanding of data engineering too. The systems being built by a Machine Learning engineer will span the entire data pipeline. 

Machine Learning Engineer and the New World of Artificial Intelligence

With more and more innovations in the IT field, careers as a machine learning engineer are rapidly becoming one of the most preferred positions. Every now and then, more companies are leaning towards AI technologies, including machine learning, and even more, companies are planning on adopting this new writing on the wall within the next five years. 

Machine Learning Engineer and the New World of Artificial Intelligence

Machine learning engineers can find exciting positions in a variety of industries, many of which will enable them to have a significant contribution to how society interacts with technology and how it enhances our lives.

While keeping in view the fact as to how rapidly the world of machine learning is moving, the life of a machine learning engineer can be something you dreamt of. Being a machine learning engineer, you can be part of some of the most exciting happenings in the world of Artificial Intelligence.

If you are someone who seeks a position as a machine learning engineer, you are having an exciting career path ahead of you. Along with developing applications that can easily enable machines to self-learn and carry out tasks without any particular human programming, machine learning engineers can move on to attain a position as an architect that works to develop out-of-the-ordinary application prototypes. 

The best part of being a machine learning engineer is that you can easily work in a broad range of professional capacities, filling several positions including machine learning engineer, principal machine learning engineer, machine learning software engineer, lead machine learning engineer, senior machine learning engineer, machine learning research scientist, data scientist positions, machine Learning Engineer experience and skills.

Machine Learning Engineer Experience and Skills

As far as qualification is concerned, machine learning engineers are required to have a master’s degree in most of the cases, and sometimes a PhD in computer science or any relevant field is also mentioned in requirement by big companies. 

In addition, a machine learning engineer should also have advanced knowledge of mathematics and also data analytical skills as both are crucial components of a machine learning engineer’s background. 

Owing to the fact that processes and results need to be communicated to management and other outside stakeholders, it is also necessary that machine learning engineers should have strong written and oral communication skills. 

Some of the employers also expect specific experience and knowledge from a machine learning engineer as they required being an essential part. Some of these skills include, 

1. Core Programming Skills and Computer Science Fundamentals

Computer science fundamentals are core part of machine learning, and ML engineers need to have basic knowledge of core skills such as data structures such as stacks, queues, trees, multi-dimensional arrays and graphs to name a few. He/she also need to have a better knowledge of algorithms such as searching, optimization, sorting, and dynamic programming, among others. 

In addition, computability and complexity (P vs NP, NP-complete problems, big-O notation, approximate algorithms, etc.), and computer architecture including memory, cache, bandwidth, deadlocks, distributed processing, etc. are also necessary for a successful machine learning engineer. 

2. Data Modeling and Evaluation

Data modeling is the process by which we estimate the underlying structure of a particular dataset, with the main aim of finding useful patterns (correlations, clusters, eigenvectors, etc.) and forecasting properties of instances that were not witnesses before and include classification, regression, anomaly detection, etc. 

An essential part of this estimation process is to consistently evaluate how better a given model is. While depending upon the task at hand, you will have to select an appropriate accuracy/error measure including log-loss for classification, sum-of-squared-errors for regression, etc. and also an evaluation strategy (training-testing split, sequential vs randomized cross-validation, etc.). 

In addition, iterative learning algorithms can often directly utilize resulting errors for tweaking the model (e.g. backpropagation for neural networks), so having a better understanding of these measures is also very useful even for just applying standard algorithms.

3. Probability and Statistics

A machine learning engineer also needs a formal characterization of probability including conditional probability, Bayes rule, likelihood, independence, etc. as well as essential techniques derived from it such as Bayes Nets, Hidden Markov Models, Markov Decision Processes etc. that are considered key in many machine learning algorithms. 

Besides, machine learning also needs a fair understanding of the field of statistics that offers various measures including mean, median, variance, etc., distributions such as uniform, normal, binomial, Poisson, etc. and also useful analysis methods such as ANOVA, hypothesis testing, etc. that are essential for building and validating successful models from observed data. 

4. Application of Machine Learning Algorithms and Libraries

As far as machine learning algorithms are concerned, their standard implementations are widely available through libraries/packages/APIs (e.g. scikit-learn, TensorFlow, Theano, Spark MLlib, H2O, etc.). Still, their effective implementation involves choosing an ideally suitable model (decision tree, nearest neighbor, neural net, support vector machine, an ensemble of multiple models, etc.), a well-planned learning procedure to fit the data (linear regression, genetic algorithms, bagging, boosting, gradient descent, and other model-specific methods), and also a better understanding of how hyper-parameters can affect learning.

A machine learning engineer should also be well aware of the relative pros and cons of various approaches. The different gotchas can trip you (bias and variance, data leakage, overfitting and underfitting, missing data, etc. 

5. Software Development and System Design

It all comes to the point where we expect that a machine learning engineer’s final output or deliverable is software. And often it is a small part that comes into a bigger ecosystem of products and services. 

A machine learning engineer needs to understand how these various pieces work together, coordinate with them by making the most of library calls, REST APIs, database queries, etc. and create appropriate interfaces for your part that others will depend on. 

It is also necessary to know that careful system design may be essential to avoid bottlenecks and allow your algorithms to scale well with enhancing volumes of data. Besides, software engineering key practices such as requirements analysis, version control, testing, documentation, system design, and modularity, to name a few are invaluable for productivity, quality, collaboration, and maintainability.

 Machine Learning Engineer salary

As per details put forward by Payscale.com, the average salary of a machine learning engineer can be $111,000 per year, plus bonuses and profit sharing. Besides, experience also has a huge impact on the machine learning engineer’s earning capacity. 

As far as the entry-level positions are concerned, machine learning engineers receive about $95,000 a year, while those with five to nine years of experience are paid from $135,000 per annum on average. You will also be amazed to know that machine learning engineers with 20-plus years of experience earn as many as $179,000 per year on average. 

As per Glassdoor revelations, the average salary of a machine learning engineer is $121, 863, with an annual salary range spanning $84,000 to $163,000 depending upon experience and location. 

Machine Learning Engineer Job Description

As we have already discussed, the job of machine learning engineers is to work with Big Data; especially, they feed data into models. Moreover, they are also involved in taking theoretical data science models while scaling them out for production-level models so that they can take care of the resulting terabytes of real-time data. They also build programs for the successful controlling of robots and computers. 

A machine learning engineers successfully develop algorithms that can help a machine to have a look at its programming data while identifying patterns in it and in so doing teaching itself how to comprehend commands and finally think for itself.

Some of the key responsibilities of a machine learning engineer include:

  • Making the most of mathematical skills in order to perform computations and successful working with the algorithms involved in this sort of programming
  • Understanding and making the most of computer science fundamentals such as data structures, algorithms, computability as well as complexity, and computer architecture
  • Collaborating with data engineers in order to build data and model pipelines
  • Successfully managing the infrastructure and data pipelines that are essential for bringing code to production
  • Coming up with producing project outcomes and separating issues that need resolution in order to make the programs more efficient
  • Demonstrating an end-to-end comprehension of applications that are being created
  • Creating machine learning algorithms based on statistical modeling procedures, and building as well as maintaining scalable machine learning solutions in production
  • Making the most of data modeling and evaluation strategy in order to find patterns and predict instances not seen before
  • Application of machine learning algorithms as well as libraries
  • Keeping in touch with stakeholders to carefully analyse business problems, clarifying essentials, and then defining the needed resolution scope
  • Analyzing large, complicated datasets in order to extract insights, and also for deciding on the appropriate techniques
  • Researching and implementing the best practices for improving the prevailing machine learning infrastructure

Bottom Line 

It won’t be an overestimation to say that the future belongs to Machine Learning and Artificial Intelligence owing to the limitless applicability. There are so many fields that are hugely influenced by Machine Learning, such as education, finance, and computer science to name a few and there doesn’t exist virtually any field in which machine learning doesn’t apply. In various cases, machine learning techniques are needed to a huge extent. 

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