{"id":2463,"date":"2022-04-18T18:53:36","date_gmt":"2022-04-18T18:53:36","guid":{"rendered":"https:\/\/mdr.foobrdigital.com\/?p=2463"},"modified":"2022-04-18T18:53:36","modified_gmt":"2022-04-18T18:53:36","slug":"machine-learning-engineer-2","status":"publish","type":"post","link":"https:\/\/mudassirbackup.infinitycodestudio.com\/index.php\/2022\/04\/18\/machine-learning-engineer-2\/","title":{"rendered":"Machine Learning Engineer"},"content":{"rendered":"\n

Machine Learnin<\/p>\n\n\n\n

A\u00a0machine learning engineer<\/strong>\u00a0is an engineer that uses\u00a0programming languages\u00a0such as, Python, Java, Scala, etc., to run experiments with the appropriate machine learning libraries. To describe in more detail,\u00a0Tomasz Dudek\u00a0says it well:<\/p>\n\n\n\n

\u201c… a person called a machine learning engineer asserts that all production tasks are working properly in terms of actual execution and scheduling, and abuses machine learning libraries to their extremes, often adding new functionalities. (They) ensure that data science code is maintainable, scalable, and debuggable, automating and abstracting away different repeatable routines that are present in most machine learning tasks. They bring the best software practices to the data science team and help them speed up their work…\u201d<\/p>\n\n\n\n

By no means is this career limited to tech-focused businesses though. Machine learning can be applied in many\u00a0industries working with large amounts of data, including financial services, retail, government, healthcare, transportation, and even oil and gas. By gaining, often real-time insights, from this data, these industries are able to work more efficiently and even gain an advantage over their competitors. Because of the wide variety of industries, \u201cmachine learning engineer jobs grew 344% between 2015-2018,\u201d according to Forbes.<\/p>\n\n\n\n

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What is the career path for a machine learning engineer?<\/h4>\n\n\n\n

To become a machine learning engineer<\/strong>, you\u00a0typically have to work your way up\u00a0so you have enough education and work experience under your belt. Here\u2019s a guideline to follow:<\/p>\n\n\n\n

1. Complete your undergraduate degree<\/strong><\/p>\n\n\n\n

Acceptable degree options are math, data science, computer science, computer programming, statistics, or physics. An understanding of business is also helpful.<\/p>\n\n\n\n

2. Entry-level careers<\/strong><\/p>\n\n\n\n

You typically can\u2019t jump into a career as a machine learning engineer so some places to start are as a software engineer, software programmer, software developer, data scientist, or computer scientist.<\/p>\n\n\n\n

3. Earn your master’s degree and\/or PHD.<\/strong><\/p>\n\n\n\n

The majority of machine learning engineer jobs require more education than an undergraduate degree. Aim to receive a master\u2019s degree in data science, computer science, software engineering, or even a PHD in machine learning.<\/p>\n\n\n\n

4. Keep learning<\/strong><\/p>\n\n\n\n

A career as a machine learning engineer means that your education never ends. As technology continually grows, your need to always be researching AI and understand new technologies becomes even more important. A great deal of leadership skills is also useful.<\/p>\n\n\n\n

Fact: Not all organizations can justify a full-time machine learning engineer, so a career as a freelancer is also a good option.<\/em><\/p>\n\n\n\n

Skills Required:<\/strong><\/p>\n\n\n\n