America’s cousin to the north has always been kinder and cooler than its rowdy neighbor. And despite the racism that abo

At version 1.20, you will get a deprecation warning for Docker. This change is coming, and like any other, it will likely cause some issues at first. But it isn’t catastrophic, and in the long run, it’s going to make things easier.

I am still very much a novice in this area (I must admit!), but I believe there are several tools that can be used to deploy models like Docker, Heroku, Streamlit, and Airflow so I want to explore the different options and compare where they see fit.

I’ve also noticed that deep learning is becoming a bigger focus throughout the community. An increasing proportion of Kaggle competitions require deep learning, more job postings require experience in deep learning, and more articles are about deep learning!

My name is Dimitris Poulopoulos, and I’m a machine learning engineer working for Arrikto. I have designed and implemented AI and software solutions for major clients such as the European Commission, Eurostat, IMF, the European Central Bank, OECD, and IKEA.

Nothing screams “Black people minding their damn business” like the OG escape spot for freedom fighters. We’ll just get our teeth cleaned before we go, and keep the floss on deck.

Below, I’ve identified five areas that I believe will help me become ten times more valuable than right now. For each area, I’ve explained why I’ve decided to dedicate my time learning it and the resources that I plan on using to help me.

It’s becoming increasingly important that data scientists not only know how to build models but how to deploy them as well. In fact, a lot of job postings are now requiring some experience in model deployment.

Similar to how I started learning machine learning, I don’t know what I don’t know. Therefore, I’m going to initially be skimming through a lot of different resources. I’ll most likely be starting with Docker, as it seems to be the most widely used for deployment.

My name is Dimitris Poulopoulos, and I’m a machine learning engineer working for Arrikto. I have designed and implemented AI and software solutions for major clients such as the European Commission, Eurostat, IMF, the European Central Bank, OECD, and IKEA.

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solved in Excel using linear regression? Well, with some degree of creativity, maybe…but I will not be sure about the quality of such work. More advanced modeling in Python will certainly give us higher flexibility and very likely better results.

It’s increasing in relevancy. Deep learning has significantly improved over the past few years — the techniques in deep learning have been refined, the efficiency of deep learning algorithms have improved, and there’s a lot more support online around deep learning (more open source projects and more educational resources).

On the other hand, if you are using a managed Kubernetes service, like GKE or EKS, you will need to make sure that your nodes are running a supported container runtime before Docker support is removed re-apply or update your custom configurations if you use any. If you are running Kubernetes on-premise, you should also need to make changes to avoid unwanted problems and surprises.

We don’t know much about Argentina beyond Manu Ginobili, but Buenos Aires is gorgeous and it literally translates to “good air,” which in this moment of ever present wildfires and generalized planetary decline we’re really hoping is a literal description.

The reason that it’s so important to learn how to deploy models is that a model is only as valuable as its ease of use and ease of interpretability. And so, being able to deploy it in a way that anyone can interact with will be extremely useful.

Let’s be real. 2020 wasn’t the best year. So why not spend the remainder of this year to plan for the next? As 2021 approaches, I wanted to plan ahead and think about what I want to learn next year to make myself a better data scientist.