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Machine Learning Career Path: Exploring Opportunities in 2022 and Beyond – insideBIGDATA

In this special guest feature, George Tsagas, Owner of eMathZone, discusses how machine learning professionals can work as data scientists, computer engineers, robotics engineers, or managers. But if you want to make a career, the first step in finding opportunities in the field of machine learning is to understand the different types of jobs and skills needed.
Machine Learning can’t be overstated these days due to its high value in the technology business. This field has the potential to touch every industry and transform them all. 
To sum up a little, machine learning is a branch of artificial intelligence that enables computers to learn from data without being explicitly programmed.
These new technologies are so important and their application is so extensive that most companies can benefit from them. That is the case of the biopharmaceutical giant Amgen which applies machine learning to maximize the efficiency of visual inspection systems. According to the Massachusetts Institute of Technology, “This technique pays off by increasing particle detection by 70 percent and reduces the need for manual inspections.
Machine learning professionals can work as data scientists, computer engineers, robotics engineers, or managers. But if you want to make a career, the first step in finding opportunities in the field of machine learning is to understand the different types of jobs and skills needed.
Machine Learning Engineers
Machine learning engineers are professional programmers developing artificial intelligence (AI) systems that can explore, develop, learn and predict large data sets. Generally, professionals in this area are responsible for the supervision and general improvements of the machine learning process to design data organization systems. This includes data analysis and configuration, testing, and application development. Over time, they will learn skills that will help them use advanced programming tools such as Python, C++, or Java. After having fulfilled all the requirements, skills, and knowledge of the career, the machine learning engineers will be able to perform the following tasks without difficulty:
Robotics Engineers
Robotics is a broad field that combines data analysis, engineering, and computer science. People in this position use mechanical hardware and software to design, build and test robots and machine-based systems. Besides, they distinguish themselves from other engineers because they are curious, methodical, analytical, and logical.
All robotics engineers, regardless of position, must have a good understanding of electronics, computer science, and mathematic estimations. They should have at least a basic knowledge of coding languages and should be able to work well in a team. Some of the responsibilities of robotic engineers after finishing their career and starting to work in an AI company are the following:
Computer Vision Engineers
A machine vision engineer must have at least a bachelor’s degree in computer science or a related field. Knowledge of C++ programming language is mandatory for this career. The general idea of this industry is to make a machine or computer look like a person. The goal of computer vision engineering is to create programs that not only see visual information but interpret it.
Computer vision engineers work with visual data. This content can come in many forms including digital signals, analog images, or computer-encoded video sources. There are a few common tasks that most computer vision engineers perform regularly:
Data Scientists
A data scientist is a professional who collects, analyzes, and interprets large volumes of data to extract relevant information from them, applying their knowledge in mathematics, statistics and programming. Its main functions are:
New technologies have taken much prominence in recent years. The amount of information available and its processing has become possible thanks to the emergence and evolution of disciplines such as Machine Learning. This evolution together with the advance in digitization has made companies take advantage of the potential of data and professions such as data scientists are booming.
General Skills Required in Machine Learning Careers
The skills of a machine learning professional are flexible and varied depending on their responsibilities. However, there are key areas that anyone seeking a career in machine learning should focus on, such as mathematics, statistics, computer science fundamentals, and programming skills.
Machine Learning Techniques
Knowing all the common machine learning algorithms is important to know which algorithm to use at any given time. Most ML algorithms fall into three general categories: supervised, unsupervised, and machine learning techniques. In more detail, some of the most common are:
Fundamentals of Computer Science and Programming
This is another important requirement to be a good machine learning engineer. Must be familiar with various computer science concepts such as:
Must be familiar with various programming languages such as Spark and Hadoop for distributed computing, Python and R for ML and statistics, SQL for database management, and Apache Kafka.
Data Sampling and Analysis
As machine learning Engineers, all professionals should have experience in data modeling and analysis. Data modeling is about understanding the underlying structure of the data and finding patterns that are not visible. They also need to evaluate data using a data matching algorithm.
Statistics and Probability
Many machine learning techniques use statistical methods, so they are easy to follow if practitioners have a solid background in mathematics. Knowledge of statistical data such as:
A solid understanding of probabilistic topics such as conditional probability, likelihoods, Markov decision, and Bayes’ rule processes are essential skills for a career in machine learning.
How To Start a Career in Machine Learning Careers
Below you can see what you need to start a Machine Learning career:
1. Have a Bachelor’s Degree
Acceptable degree options are Mathematics, Computer Science, Computer, Mathematics, or Physics. Business knowledge is also helpful.
2. Go into Lower Level Careers
Generally, you cannot work as a machine learning engineer, so start as a software engineer, data scientist, or computer scientist.
3. Complete a Master’s Degree and/or PhD
Most machine learning engineering jobs require more than an undergraduate degree in data science, computer science, or software engineering.
4. Don’t Stop Learning
A career in machine learning engineering means your education never ends. As technology advances, the need to constantly research AI and understand new technologies becomes increasingly important.
Machine Learning Career Path – Summarizing
Machine learning is an important part of any modern business. It is a powerful tool for predicting outcomes, and it is being used in everything from shopping sites that recommend products to web searches.
To start a career in this field you need to be prepared to constantly learn new content and strategies as technology advances. It’s a never-ending experience in which you prioritize data over everything else.
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In this special guest feature, Chris Plescia, Chief Technology Evangelist, Aware, highlights how conversational platforms and tools such as Slack, Microsoft Teams, Yammer, and Workplace from Meta have made the digital workplace more productive, social and collaborative but they’ve also introduced a non-standard data set into the enterprise. The article will help readers to understand the new data set by describing IT challenges, governance risks, and other considerations for the collaboration ecosystem.

The survey associated with this report, commission by Immuta, focused on identifying the limiting factors in the data “supply chain” as it relates to the overall DataOps methodology of the organization. DataOps itself is the more agile and automated application of data management techniques to advance data-driven outcomes, while the data supply chain represents the technological steps and human-involved processes supporting the flow of data through the organization, from its source, through transformation and integration, all the way to the point of consumption or analysis.

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