What you need to know about a STEM position as a Machine Learning Developer

Top reasons of interest to easily understand everything about the STEM position as a Machine Learning Developer

When it comes to STEM, it’s not just about coding and lab coats. It’s the foundation of manufacturing, food production, healthcare, and so much more that, frankly, we might take for granted, but surely we can’t live without.

In this blog, I will share with you what I have found by answering the following questions about the STEM position as a machine learning developer, which I am particularly interested in.

1. What does “STEM” mean?

STEM is an acronym for the fields of science, technology, engineering, and mathematics. Experts generally agree that STEM workers use their knowledge
science, technology, engineering or math to try to understand how the world works and solve problems. His job often involves the use of computers and other tools. (Vilorio, 2014)

STEM is important because it permeates every part of our lives. Science is everywhere in the world around us. Technology is continually expanding in all aspects of our lives. Engineering is the basic design of roads and bridges, but it also addresses the challenges of the changing global climate and ecological changes in our home. Mathematics is in all occupations, in all the activities we do in our lives. («Why is STEM Education So Important?», 2021)

A STEM job is any occupation that requires STEM education and uses STEM skills. STEM jobs don’t just include programming or coding, or the tasks of a computer technician, engineer, etc.
Here are some examples of the STEM jobs of the future:
Robot and Automated Systems Repair, Green Power Creator, Tech Tutor and Trainer, Drone Technician, Space Exploration, Future Farmers, 3D Printing Engineers, Data Managers, Senior Wellness Managers, Broadcast Transmitters, Biotech Engineers, Specialists in scientific ethics / technology advocates, chrono currency agents, digital enforcers, artificial intelligence coach and technician, climate analyst and weather moderators.

2. Why Machine Learning Developer position is important in the STEM fields?

The position of a machine learning developer in STEM fields is important because through their acquired skills, they allow them to better apply their knowledge to create or improve products or features and have a positive impact on millions of people. (LinkedIn, 2021)

Machine learning developers work primarily in the field of technology. Their task is to create algorithms that allow the machine to analyze the input information and understand the causal relationships between events, they are also working to improve these algorithms. To become a machine learning developer, you must have excellent logic, analytical thinking, and programming skills. (Gavrilova, 2021)

3. What makes Machine Learning interesting and unique?

In simple words, machine learning allows the user to feed a computer algorithm with an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based only on the input data, and this is something unique!

Machine learning is fascinating because programs learn from the data you have collected. A machine learning method can automatically analyze and learn the structure that already resides in that data to provide a solution to the problem you are trying to solve.

For example, machine learning algorithms make autonomous cars possible. They allow a car to collect data about its environment from cameras and other sensors, interpret it, and decide what actions to take. Machine learning even allows cars to learn to perform these tasks as well as (or even better) than humans.

4. What makes the Machine Learning similar to others?

Data Science — The manipulation of data to produce insights and graphs which are used to monitor and provide visibility to organizational processes. Typically this involves data scientists analyzing data to produce insights for various business units and executives.

Predictive Analytics — The use of current and historical data to make predictions about the future. In general predictive analytics leverages predictive models like neural networks and decision trees to make predictions. Predictive analytics can often be used as part of a one-time analysis for a data science project.

Machine Learning — Machine learning also uses current and past data to make predictions. And machine learning also uses models like neural networks and decision trees to make predictions. The key difference between machine learning and predictive analytics is that machine learning technology is continually adapting and adjusting to new data, while predictive analytics technology is typically used in a more static fashion for a one-time analysis.

5. What specific programming languages and tools could one expect to work with as a Machine Learning developer?

The tools of the trade are machine learning platforms, which are then used as the basis for complex programs that ingest data and learn to make the identifications, predictions, or any other more precise modeled output that is required. They are mainly: Microsoft Azure, Google Cloud, IBM Watson, and Amazon. (discoverdatascience.org, 2021)

• Microsoft-Azure Machine Learning: is a service that lets developers and data scientists build, train, and deploy machine learning models. The product features productivity for all skill levels via a code-first and drag-and-drop designer, and automated machine learning. It also features expansive MLops capabilities that integrate with existing DevOps processes. The service touts responsible machine learning so users can understand models with interpretability and fairness, as well as protect data with differential privacy and confidential computing. Azure Machine Learning supports open-source frameworks and languages like MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R.
• Google Cloud AI Platform: offers one of the largest machine learning stacks in the space and offers an expanding list of products for a variety of use cases. The product is fully managed and offers excellent governance with interpretable models. Key features include a built-in Data Labeling Service, AutoML, model validation via AI Explanations, a What-If Tool which helps you understand model outputs, cloud model deployment with Prediction, and MLOps via the Pipeline tool.
• IBM Watson Studio: enables users to build, run, and manage AI models at scale across any cloud. The product is a part of IBM Cloud Pak for Data, the company’s main data and AI platform. The solution lets you automate AI lifecycle management, govern and secure open-source notebooks, prepare and build models visually, deploy and run models through one-click integration, and manage and monitor models with explainable AI. IBM Watson Studio offers a flexible architecture that allows users to utilize open-source frameworks like PyTorch, TensorFlow, and scikit-learn.
• Amazon SageMaker: is a managed service in the Amazon Web Services (AWS) public cloud. It provides the tools to build, train and deploy machine learning (ML) models for predictive analytics applications. The platform automates the tedious work of building a production-ready artificial intelligence (AI) pipeline. SageMaker also enables developers to deploy ML models on embedded systems and edge-devices.

Commonly used programming languages ​​include, but are not limited to, the following: Python, C, C ++, Java, JavaScript, R, and Scala. (discoverdatascience.org, 2021)

• Python: is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.
• C: is a procedural computer programming language that supports structured programming. It was designed to be compiled to provide low-level access to memory and language constructs that map efficiently to machine instructions, all with minimal runtime support. Despite its low-level capabilities, the language was designed to encourage cross-platform programming. A standards-compliant C program written with portability in mind can be compiled for a wide variety of computing platforms and operating systems with few changes to its source code.
• C++: is a general-purpose programming language, it was created as an extension of the C programming language, or “C with classes.” The language has expanded significantly over time, and modern C ++ now has functional, generic, and object-oriented features, as well as facilities for low-level memory manipulation. It is almost always implemented as a compiled language, and many vendors provide C ++ compilers, including Free Software Foundation, LLVM, Microsoft, Intel, Oracle, and IBM, making it available on many platforms.
• Java: is a programming language and computing platform first released by Sun Microsystems in 1995. It has evolved from humble beginnings to power a large share of today’s digital world, by providing the reliable platform upon which many services and applications are built.
• JavaScript: is a scripting or programming language that allows you to implement complex features on web pages — every time a web page does more than just sit there and display static information for you to look at — displaying timely content updates, interactive maps, animated 2D/3D graphics, scrolling video jukeboxes, etc. — you can bet that JavaScript is probably involved.
• R: is a language and environment for statistical computing and graphics. One of the strengths of R is the ease with which it can produce quality graphics. well-designed publication, including mathematical graphics symbols and formulas where needed.
• Scala: combines object-oriented and functional programming in one concise, high-level language. Scala’s static types help avoid bugs in complex applications, and its JVM and JavaScript runtimes let you build high-performance systems with easy access to huge ecosystems of libraries.

6. What is an example of a problem or a challenge someone in machine learning could solve or be asked to work on?

The applications of machine learning are many, including external (customer-centric) applications such as product recommendation, customer service and demand forecasting, and internally to help companies improve products or accelerate processes time-consuming and manual.

Machine learning algorithms are typically used in areas where the solution requires continuous improvement after implementation. Adaptive machine learning solutions are incredibly dynamic and embraced by companies across all verticals.

Here are some common machine learning problems or challenges and how you can overcome them:

• Not enough training data: while Machines are not as fast as the human beings when it comes to learning what an apple looks like! It may take thousands of examples to learn what an apple is! Now think of a more advanced tasks, like image or speech recognition, it may take it millions of examples.
• Poor Quality of data: Obviously, if your training data has lots of errors, outliers, and noise, it will make it impossible for your machine learning model to detect a proper underlying pattern. Hence, it will not perform well.
• Irrelevant features: “Garbage in, garbage out (GIGO).”Our training data must always contain more relevant and less to none irrelevant features.
• Non representative training data: If we train our model by using a non-representative training set, it won’t be accurate in predictions it will be biased against one class or a group. Use representative data during training, so your model won’t be biased among one or two classes when it works on testing data.

7. What are some positives and negatives of the Machine Learning Developer?

Positive:

• The machine learning developer will allow you to work and create real world products, from autonomous cars to security drones. Everything you create has a real world application. Imagine the satisfaction of seeing that something you have created helps someone in their daily life! Simply put, the effort you put in day after day is for the work that matters.
• As a machine learning developer, you will also develop the skills necessary to be a data scientist. Becoming proficient in both fields will make you a unique product for employers. As a data scientist, you will be able to analyze data and extract value from it. As a machine learning developer, you can use that information to train a machine learning model to predict results. In several organizations, machine learning engineers work with data scientists to better synchronize work products.

Negative:

• Model training, data management, as well as prototyping and testing on a daily basis can lead to mental exhaustion. As a machine learning developer, data manipulation will also be a painful part of your job. Data manipulation simply means: the process of transforming and mapping data from a “raw” data form to another format with the intention of making it more appropriate and valuable for a variety of downstream purposes, such as analytics.
• Machine learning occurs over time. Therefore, there will be a period when your interface or algorithm will not develop sufficiently for the needs of your business. The precise amount of time required will depend on the nature of the data, the data source, and how it will be used. You will simply have to wait for new data to be generated; Sometimes this can take days, weeks, months, or even years.

Finally:

The prospects for the field of machine learning are extremely high in today’s data science landscape. In fact, Forbes recently listed it as one of the Top 10 Tech Job Skills Projected to Grow the Fastest in 2021. Forbes determined that demand for artificial intelligence and machine learning skills will grow 71% through 2025 and estimated that there are almost 200,000 vacant positions. today requires experience in machine learning. Machine learning engineers from major industries include manufacturing, information technology, finance and insurance, business marketing and advertising, and professional services.(Schmitt, 2019)

I hope you had fun reading this blog. Happy learning!

Reference:

Vilorio, D. (2014). STEM 101: Intro to tomorrow’s jobs. https://www.datarevenue.com/en-blog/hiring-machine-learning-engineers-instead-of-data-scientists

Why is STEM Education So Important?. (2021). https://www.engineeringforkids.com/about/news/2016/february/why-is-stem-education-so-important-/

LinkedIn. (2021). How your passion for STEM can positively impact millions of people. https://www.businessnews.ie/women-in-stem/celebrating-individuality-through-a-career-in-technology/#

Gavrilova, Y. (2021). Top Machine Learning Career Paths in 2021. https://serokell.io/blog/top-ml-jobs

discoverdatascience.org. How to Become a Machine Learning Engineer-A Complete Career Guide (2021). https://www.discoverdatascience.org/career-information/machine-learning-engineer/

Schmitt, M. (2019). Why to Hire Machine Learning Engineers, Not Data Scientists. https://www.datarevenue.com/en-blog/hiring-machine-learning-engineers-instead-of-data-scientists