A machine learning engineer primarily deals with Artificial Intelligence. A machine learning engineer is basically a computer programmer who creates programs that assist machines to take action without being specifically directed to perform those set of tasks. Machine learning engineers have an impact on numerous individuals right from providing them with tailored web searches to customized news feeds.
Machine learning engineers work at cutting-edge companies like Spotify, Adobe, Facebook, Google, Linkedin etc.
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Skills that a machine learning engineer utilizes at work
Basic programming – Computer architecture (memory, distributed processing, bandwidth cache), data structures (queues, stacks, trees, graphs, multi-dimensional arrays) and algorithms (searching, sorting, optimization).
Probability and statistics –concepts of Bayes Nets, Bayes rule, Markov decision processes etc. Along with the probability concepts, statistics concepts like median, variance, mean, hypothesis testing, mean, normal distributions, uniform distributions and binomial distributions.
Machine learning algorithms and libraries –A machine learning engineer selects appropriate models like decision tree, neural net, linear regression, boosting, genetic algorithms and bagging. A machine learning engineer is aware of the advantages and disadvantages of different approaches like data leakage, bias and variance, missing data, and overfitting and underfitting.
Data Modeling and evaluation – A machine learning engineer evaluates a dataset’s structure to identify constructive patterns.
Writing skills- Some companies require a machine learning engineer to publish articles about his projects.
Responsibilities of a Machine Learning Engineer include:
- Analysis of machine learning algorithms to find a solution to a problem.
- Identification of differences in data distribution.
- Verification of data quality and to ascertain data quality with the help of data cleaning.
- Exploration and data visualization.
- Supervision of data acquisition processes.
- Feed data into models defined by data scientists.
- Define validation strategies.
- Interpretation of business objectives and development of models.
- Production of project outcomes and isolation of problems which need to be solved to make the programs more effective.
- Use of evaluation strategy and data modeling to predict unforeseen instances.
- Management of resources available to the machine learning scientist such as hardware and personnel.
- Research and implementation of the best practices to improve the current machine learning infrastructure. Explain complex processes to clients and co-workers from non-technical backgrounds
- Support to product managers and engineers in implementation of machine learning in the product. Learn more about machine learning engineer responsibilities.
A typical day in the life of a machine learning engineer consists of reading research papers and applying this knowledge to the current projects, identifying which algorithm works well for the problems they are trying to solve, holding discussions with his reporting manager regarding the solutions that they are working on, responding to emails, attending office meetings and client calls, designing databases and checking metrics for existing models.
He performs all the functions from data collection, preparation, model optimization and deployment. Develop testing tools for monitoring and analyzing data performance and data accuracy.
Schedule of a Machine learning engineer
If a machine learning engineer starts his day at 9.00 AM, he revises the projects and code which have been in operation during the night hours. He checks his to-do-list for his day. He checks his work email and responds to emails.
From 10.00 AM to 12.00 PM, he attends calls related to work. After that, he begins to work with machine learning projects and tools. He designs a database. He utilizes mathematical skill to carry out these computations. He learns new concepts with the assistance of creative tools like Scikit Learn, H20 etc. A machine learning engineer and his team put together a list of research-based techniques and algorithms they would want to implement.
After lunch, around 1.00 PM, he attends office meetings where team members share what they have been working on, the progress they have made in their respective projects and review each other’s progress and discuss what they could have done better. He takes care of client calls.
He discusses the progress of ongoing projects and proposed ideas for novel products and projects. A machine learning engineer needs exceptional communication skills to talk to his co-workers and clients. He designs the systems cautiously in order to avoid bottlenecks.
Between 2.00 PM to 5.00 PM, he writes unit tests, checks completed models and completes the continuing tasks. After finishing these tasks, he checks the existing model’s metrics and compares these metrics to the baseline model. He goes back to coding and reviews requests from the client’s side. He utilizes his strong analytical skills to interpret outcomes and to identify issues to design his projects effectively.
Between 6.00 PM to 8.00 PM, he wraps up the database models, projects and code requests and ensures that no task is pending before he leaves the office.
After reaching home, he checks his work email around 10.00 PM to see if there are any work-related problems and takes action on the issues that need immediate action.
A machine learning engineer who works at a firm said, “The best part is, I’m always given the opportunity to experiment with my models, and my peers are open to listening to and implementing my ideas.”
“I’m constantly learning and always eager to learn new approaches in the field. There is always an opportunity to contribute in a different manner”, he added.
It is imperative for a machine learning engineer to interpret the complete ecosystem for the project he is working on. The excellent news for machine learning engineers is that machine learning has a vast application across several domains. Various fields such as manufacturing, education, finance and information technology would greatly benefit from machine learning. Machine learning engineers design complex systems to solve the complex challenges presented by the world that is changing rapidly.
By the year 2025, global data creation is estimated to reach 175 Zettabytes. This means that Artificial intelligence will create an enormous number of jobs. In the field of artificial intelligence, a machine engineer leads from the front. A machine learning engineer would be able to maintain a prosperous and a flourishing career well into the future.
Soon, there’ll be more such astounding breakthroughs pioneered by Machine Learning, and Machine Learning Engineers will continue to be an integral part of all such ML operations.
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How do data scientists differ from machine learning engineers?
A machine learning engineer does not need to be an expert in the prediction model or the logic that underlies it. This is the responsibility of a data scientist. Machine learning engineers are required to be well-versed in the software technologies that power these models. A data scientist gathers, processes, and extracts significant insights from data. While data scientists develop models for machine learning engineers to feed into, machine learning engineers are in charge of maintaining the ML infrastructure, which allows them to deploy and scale the models created by data scientists. Furthermore, data scientists take advantage of the machine learning infrastructure created by the machine learning engineer.
What are the qualifications required to become a machine learning engineer?
For an engineer, basic knowledge of mathematics, statistics, and logical reasoning is crucial. When it comes to being good at working as a machine learning engineer, you need to be acquainted with deep learning, neural networks, and some other related topics. As far as educational qualifications are concerned, it’s mandatory for you to hold a bachelor’s degree in fields like mathematics or computer science to work efficiently as a machine learning engineer. Undoubtedly, having great communication skills is as essential as having technical skills.
Will mentioning machine learning projects on the resume be helpful?
If you are applying for the position of machine learning engineer, you can and should highlight your previous machine learning projects. However, the project descriptions should be kept brief in order to avoid boredom. You can briefly mention the dataset, model training, libraries used, and accuracy in the description by highlighting just the most important points.