As businesses around the world are embarking on building innovative solutions, we're seeing a growing trend adopting data science workloads across various industries. Recently, we've seen a greater push towards reducing the friction between data engineers and data scientists. Data scientists are now enabled to run their experiments on their local machine and port to it powerful clusters that can scale without rewriting the code. You have many options for running data science workloads, such as running it on your own managed Spark cluster. Alternatively there are cloud options such as Amazon SageMaker, Amazon EMR and Amazon Elastic Kubernetes Service (Amazon EKS) clusters.

If there's a silver lining to social distancing, it's the fact that it gives us a chance to catch up on content we otherwise might have missed. There are always too many sessions to attend at cloud conferences -- from service introductions and updates to best practices and use cases -- that could change the way you use cloud technologies. The global health crisis has made it unlikely any of us will gather for a conference in 2020. Given the dangers of COVID-19, it seems unwise for thousands of professionals from around the world to gather in a crowded convention center. While the in-person conference experience is off the table for the near future, there are plenty of resources still available to review from cloud conferences over the past year.

Machine learning is computationally expensive -- and because serving real-time predictions means running your ML models in the cloud, that computational expense translates into real dollars. Put another way, if you wanted to add a translation feature to your app that automatically translated text to your user's preferred language, you would deploy an NLP model as a web API for your app to consume. To host this API, you would need to deploy it through a cloud provider like AWS, put it behind a load balancer, and implement some kind of autoscaling functionality (probably involving Docker and Kubernetes). None of the above is free, and if you're dealing with a large amount of traffic, the total cost can get out of hand. This is especially true if you aren't optimizing your spend.

For the last six years running, the most important event in cloud computing has been AWS re:Invent, where the market leader announces its latest improvements. This year, 44,000 people descended upon a very crowded set of Las Vegas venues spread across multiple hotels for breakout sessions, certification exams, a diverse expo floor, and the all-important keynotes where the newest offerings were announced. Increasingly, the public cloud arms race is being waged on four fronts, with a fifth quickly emerging. All five had a healthy set of announcements--here are some of the highlights. AWS started the cloud revolution with its S3 object storage service in 2006, which was quickly followed by its EC2 compute offering and a set of other IaaS products.