Have you ever been in a situation where you want to provide your model predictions to a frontend developer without them having access to model related code? Or has a developer ever asked you to create an API that they can use? I have faced this a lot. As Data Science and Web developers try to collaborate, API’s become an essential piece of the puzzle to make codes as well as skills more modular.

Creating my own workstation has been a dream for me if nothing else. I knew the process involved, yet I somehow never got to it. But this time I just had to do it. So, I found out some free time to create a Deep Learning Rig with a lot of assistance from NVIDIA folks who were pretty helpful. On that note special thanks to Josh Patterson and Michael Cooper.

Have you ever wondered how Facebook takes care of the abusive and inappropriate images shared by some of its users? Or how Facebook’s tagging feature works? Or how Google Lens recognizes products through images? All of the above are examples of image classification in different settings. Multiclass image classification is a common task in computer vision, where we categorize an image into three or more classes. In the past, I always used Keras for computer vision projects.

With the advent of so many computing and serving frameworks, it is getting stressful day by day for the developers to put a model into production. If the question of what model performs best on my data was not enough, now the question is what framework to choose for serving a model trained with Sklearn or LightGBM or PyTorch. And new frameworks are being added as each day passes.

Big Data has become synonymous with Data engineering. But the line between Data Engineering and Data scientists is blurring day by day. At this point in time, I think that Big Data must be in the repertoire of all data scientists. Reason: Too much data is getting generated day by day And that brings us to Spark which is one of the most used tools when it comes to working with Big Data.

Every few years, some academic and professional field gets a lot of cachet in the popular imagination. Right now, that field is data science. As a result, a lot of people are looking to get into it. Add to that the news outlets calling data science sexy and various academic institutes promising to make a data scientist out of you in just a few months, and you’ve got the perfect recipe for disaster.

Recently, I was reading Rolf Dobell’s The Art of Thinking Clearly, which made me think about cognitive biases in a way I never had before. I realized how deeply seated some cognitive biases are. In fact, we often don’t even consciously realize when our thinking is being affected by one. For data scientists, these biases can really change the way we work with data and make our day-to-day decisions, and generally not for the better.