Main image of article These Are the Skills You Need to Land a Machine Learning Job
Machine learning (ML) is hot right now, and seems to be poised for a broader takeover of tech in the future. As jobs open up, we’ve identified the skills needed to land your next ML job. A recent study by Univa shows 96 percent of tech pros say the number of machine learning projects will expand over the next 24 months. Some 68 percent of respondents say their company has used machine learning increasingly in the past two years, too. Interest in a technology or skill typically aligns with job growth. We’ve seen ML jobs rise steadily over time, and there’s been a strong boost the past year or so as Google, Apple, and Microsoft have gotten serious about what machine learning is capable of. As each company rolls out their own developer tools, developer engagement and interest climbs. Tech pros can’t just apply for machine learning jobs without correlating skills, though. As we see in the chart below, the total number of ML jobs over the past two years has grown nearly 600 percent. From the beginning, one skill – ‘data science’ – has led the way. There have been 50 percent more total data science jobs posted since the beginning of 2016, compared to the second-most-popular skill (artificial intelligence, or A.I.). And A.I. job postings during this time have the same lead (50 percent) over third-place Amazon Web Services (AWS). The remaining two skills on our list, TensorFlow and Apache Kafka, trail the other three significantly. We view Kafka as more a complimentary service for companies than a favored one (Kafka works very well with AWS, for instance, so it’s likely benefitting from some synergy). Tensorflow makes significant gains quarterly. It started with very few job postings, and has risen sharply – especially compared to Kafka, which has seen mostly stagnant growth after a sharp spike at the end of 2017. We’ll also note ‘data science’ and ‘artificial intelligence’ are general terms, which tells us companies hiring for machine learning jobs don’t have a preferred framework yet, and possibly don’t know exactly what they’re using ML for. Our findings with skills and machine learning jobs intersect with the cloud, as well. AWS makes a strong showing, and Univa’s survey shows 82 percent of tech pros plan to use a hybrid cloud scheme for their machine learning projects. The sky is the limit, too. Only 22 percent of Univa’s respondents say their company has machine learning projects in production; 28 percent are in the beta/pilot phase, and a full 50 percent say they’re in the research phase. This is likely because machine learning and A.I. are definitely not ready for primetime. We should remember that, while it’s 2018 and computers can do all kinds of interesting things, they must be taught, and we’re just learning how to teach them. Which is why now's a great time to get into machine learning.