Women are making meaningful headway in the field of machine learning and artificial intelligence. Read on to learn more about the challenges women are overcoming and the opportunities that are helping close the gender gap.
Here’s what we’ll cover:
It’s no secret that STEM professions—shaped by years of gender and racial bias—lack diversity. Machine learning engineering and research is no exception. Women currently hold around 25% of all computer science-related jobs, and only 12% of machine learning roles, with factors such as a lack of pay and career advancement transparency and a lack of women role models contributing to those numbers.
But leaders in the machine learning and AI industry have in recent years woken to the value that women bring to the workforce. It doesn’t just look good for a company to have diversity—it’s integral to the success of organizations that build machine learning algorithms and artificial intelligence. Having more women working on machine learning and AI can alleviate the issue of selection bias, which is one of the biggest problems facing AI enterprises. “In order for organizations to achieve the highest AI maturity levels, it is necessary to mobilize women on a mass scale and include them as part of all enterprise endeavors in artificial intelligence, from research to product launch,” according to a report from Forbes.
To that end, tech execs have made it their mission to diversify their hires and teams, overhaul toxic cultures, and prevent attrition. Many women machine learning engineers and researchers have also taken matters into their own hands, creating opportunities and support networks for women who are either considering or have already embarked on their technology careers, supporting young women through mentorships, and clearing some of the hurdles that stand in the way of women who have a technical background.
Many of the challenges that stand in the way of women building long and satisfying careers in machine learning engineering and research are systemic and have proven difficult for organizations to dismantle. The good news is that many companies, industry leaders, and women have stepped up to the challenge—the issue of diversity is being prioritized in professional conversations, organizations are attempting to build a more supportive environment, and women are finding ways to exchange ideas and share information in ways that help each other advance their careers.
Every facet of machine learning, whether it’s AI, deep learning, robotics, machine learning research, statistics, or text mining benefits from diversity at all levels. Recognizing some of the barriers to entry that can deter women from careers in the profession or discourage them from climbing the ranks, machine learning engineer leaders, women leaders in AI, and prominent researchers from the machine learning community have launched conferences and initiatives to support women from the time they’re in school through to when they’re in the workforce.
Some of the more notable conferences, organizations, meetups, and networking opportunities include:
A growing number of organizations have thrown their support behind helping tech companies achieve gender diversity, namely through offering scholarships, fellowships, grants, and outreach programs. Machine Learning scholarships are available for undergraduate and graduate students who are pursuing machine learning and artificial intelligence, computer science, natural language processing, or related fields, and there are also internship programs and hackathons that aim to give students from historically underrepresented groups hands-on industry experience.
On the online courses/bootcamp front, Springboard has partnered with Women Who Code to offer ten scholarships worth $1,000 each to women who enroll in Springboard’s Data Science Career Track, Software Engineering Career Track, or the Machine Learning Career Track.
Springboard also offers a number of women-in-tech scholarships
Many graduates of the program credit Springboard’s three-pronged approach to their success:
“The self-paced structure can be stressful, and that was where interacting with my mentor and scheduling calls with Springboard coaches/advisors was helpful. It definitely challenged me and made me more comfortable, not just casually self-learning with the help of others, but taking it a serious step further to enter another career.”
Despite the poor diversity numbers in the field of machine learning engineering, women occupy some of the most influential and pioneering roles in the profession. A few industry leaders include:
“Five most important things that I have learned and that are most important to me are the following,” said Ivana Bartoletti, technical director at Deloitte.
Is machine learning engineering the right career for you?
Knowing machine learning and deep learning concepts is important—but not enough to get you hired. According to hiring managers, most job seekers lack the engineering skills to perform the job. This is why more than 50% of Springboard's Machine Learning Career Track curriculum is focused on production engineering skills. In this course, you'll design a machine learning/deep learning system, build a prototype, and deploy a running application that can be accessed via API or web service. No other bootcamp does this.
Our machine learning training will teach you linear and logistical regression, anomaly detection, cleaning, and transforming data. We’ll also teach you the most in-demand ML models and algorithms you’ll need to know to succeed. For each model, you will learn how it works conceptually first, then the applied mathematics necessary to implement it, and finally learn to test and train them.
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