As businesses of every size push forward with cloud projects in 2018, the demand for cloud skills is accelerating. Public cloud adoption is expected to climb significantly and the IDC predict spending will reach £197 billion in just three years. But as cutting-edge technologies, like machine learning, continue to reshape the job market the skills gap looms large across the industry. With over 350,000 specialists needed to help fill cloud roles there's clearly a massive opportunity for professionals that can prove their skills. Whether you're taking your first steps into cloud or are aiming to increase your marketability, this is your opportunity to expand your cloud skill set in 2018.
Artificial intelligence is no longer reserved for sci-fi flicks, but the technology is still alien to many enterprise IT teams. Nevertheless, one adoption trend is clear: the public cloud will be the go-to destination for most enterprise AI workloads. "I'm not saying that AI won't happen in the enterprise in people's data centers, but this is a workload that will predominantly happen in the cloud," said Rob Koplowitz, principal analyst at Forrester Research. Some organizations will choose to keep AI applications -- particularly those that contain sensitive customer data -- in-house, as they do for other workloads with strict security or compliance requirements. But, in general, public cloud AI services will be the predominant model, agreed Adrian Bowles, lead analyst at Aragon Research.
For most businesses, machine learning seems close to rocket science, appearing expensive and talent demanding. And, if you're aiming at building another Netflix recommendation system, it really is. But the trend of making everything-as-a-service has affected this sophisticated sphere, too. You can jump-start an ML initiative without much investment, which would be the right move if you are new to data science and just want to grab the low hanging fruit.
For most businesses, machine learning seems close to rocket science, appearing expensive and talent demanding. And, if you're aiming at building another Netflix recommendation system, it really is. But the trend of making everything-as-a-service has affected this sophisticated sphere, too. You can jump-start an ML initiative without much investment, which would be the right move if you are new to data science and just want to grab the low hanging fruit. One of ML's most inspiring stories is the one about a Japanese farmer who decided to sort cucumbers automatically to help his parents with this painstaking operation. Unlike the stories that abound about large enterprises, the guy had neither expertise in machine learning, nor a big budget. But he did manage to get familiar with TensorFlow and employed deep learning to recognize different classes of cucumbers. By using machine learning cloud services, you can start building your first working models, yielding valuable insights from predictions with a relatively small team. We've already discussed machine learning strategy. Now let's have a look at the best machine learning platforms on the market and consider some of the infrastructural decisions to be made.
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