Wether you're a beginner in the space or already an experienced Deep Learning veteran, podcasts are one of the best ways to stay up to date with all the change and new stuff happening so quickly. It is also probably one of the methods requiring the least amount of effort to do so. Especially because you can listen to podcasts while doing other things (on a commute, during a walk in the park or while cooking, etc.). From our own experience this has already paid huge dividends over the years, discovering things that you otherwise would have never heard of or probably much later. It's nice staying ahead of the curve and listening to people often way smarter than yourself, or at least you think that must be the case So we took a quick break and compiled a list of the currently 10 most interesting podcasts out there that probably every Data Scientist, ML Engineer or Analyst should subscribe to.

The ratio of remote jobs on our site during May this year was about 35.5% of all job openings published, compared to 23% during the same month in 2021. That's quite an increase and a clear sign that remote working is here to stay and becoming quite common for at least one third of applicants that find their new role in the AI/ML space through our site. Now there are different kinds of companies out there that hire remotely. For example the ones that have been forced to go remote during the past two years for obvious reasons. Many of them now seem to adapt a hybrid working model with some time remote and some time on-site/in the office.

Reading scientific papers is hard! If you are working in the field of AI, you are aware of how fast the pace of progress has become. With hundreds of new papers published every day, there are countless new ideas, methods, and architectures which move the whole field forward. If you want to keep your knowledge/project/job competitive, you have to at least follow the basics of new discoveries. But reading those papers is not an easy job.

We thought it would be interesting to go deeper into the analytics part of running our AI/ML/DS job board – in the most privacy focused way for our users, of course. So here we are now starting to expose some of our aggregated internal job data in a systematic fashion through a simple REST API with a list of generalized job titles that are posted on ai-jobs.net. The cool thing with this is that we are able to see what's popular in terms of the roles employers are hiring for most often in the AI/ML and Big Data space. Our "methodology" here is quite simple: we're primarily counting the occurence of job titles over the last 12 months, cleaned and adjusted for certain specialties that some companies like to do, e.g. We also left out many titles with indicators regarding seniority or other requirements as we already have dedicated attributes for these.

Thanks to the rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML) across industries, AI and ML have found a place in the common vocabulary. Almost every sector of the industry (healthcare, e-commerce, IoT, banking & finance, etc.) are leveraging AI and ML to streamline business operations and create innovative products/services. So, when everyone in the industry is using AI and ML, what can you do differently to up your game? The answer is MLOps or Machine Learning Operationalization. In simple terms, MLOps is the Machine learning equivalent of DevOps.