In my wandering around the various data science tools and frameworks, I discovered Deepnote, an online framework that allows you to create and run notebooks in Python. Compared to the more famous Jupyterlab and Colab frameworks, Deepnote allows you to write Python notebooks collaboratively and in real time. Your collaborator may even comment your code! Deepnote can be easily integrated with the most popular cloud services, such as Google Drive and Amazon S3, as well as the most popular databases, such as PostgresSQL and MongoDB. In addition, projects can be integrated with Github and published over the Web, since Deepnote provides each user with a dedicated Web page, which can be used as a portfolio.

I have also linked my other profiles and my recent achievements. If you scroll down, you will see my most starred projects and contribution info. I have been approached by CEOs, recruiters, startup founders, students, and researchers through GitHub. Most of them want to know more about my project and how they can change it for the pacific data application. Kaggle is the super-platform for data scientists and machine learning engineers.

This article will show how to use SingleStoreDB with MindsDB using Deepnote. We'll create integrations within Deepnote, load the Iris flower data set into SingleStoreDB, and then use MindsDB to create a Machine Learning (ML) model from the Iris data stored in SingleStoreDB. We'll also make some example predictions using the ML model. Most of the code will be in SQL, enabling developers with solid SQL skills to hit the ground running and start working with ML immediately. The notebook file used in this article is available on GitHub.

I will be sharing my experience with the best cloud notebooks and explaining why they are in the top five. The cloud integrated development environment (IDE) or cloud Jupyter Notebooks has changed my whole perspective about working on data science projects. These platforms comes with pre-installed Python or R packages that are useful for most of the project, and within a few seconds, you are ready to start working on the project. It usually takes me more to load VSCode on my laptop, and then I have to install missing packages. Other than free compute and pre-build environments, cloud notebook platforms offer third-party tool integrations, collaborations, and publication options.

The coalescence of machine learning tools into the Python ecosystem makes sense when you consider all steps that are required to train and test models: cleaning, transformation, visualization, and so on. There's so much iteration involved in machine learning that using a data science programming language seems necessary. However, the marriage of Python and machine learning, while sensible, does have a trade off: Database professionals are more likely to speak SQL than Python. The 2020 Stack Overflow survey bears this out nicely, showing an ML cluster centered around Python, and a separate cluster linking SQL with database technologies. Based on this, if we assume that data/analytics engineers are "closest" to their company's data, then why not put tools in their hands that unlock the full potential of their domain expertise? This is where MindsDB comes in.

The AI area of Natural Language Processing, or NLP, throughout its gigantic language models -- yes, GPT-3, I'm watching you -- presents what it's perceived as a revolution in machines' capabilities to perform the most distinct language tasks. Due to that, the perception of the public as a whole is split: some perceive that these new language models are going to pave the way to a Skynet type of technology, while others dismiss them as hype-fueled technologies that will live in dusty shelves, or HDD drives, in little to no time. Motivated by this, I'm creating this series of stories that will approach NLP from scratch in a friendly way. To join me, you'll need to have little experience with Python and Jupyter Notebooks, and for the most part, I won't even ask you to have anything installed on your machine. This series will differ dramatically from the Stanford course in terms of the depth that we'll approach statistics and calculus.

Deepnote, a startup that offers data scientists an IDE-like collaborative online experience for building their machine learning models, today announced that it has raised a $3.8 million seed round led by Index Ventures and Accel, with participation from YC and Credo Ventures, as well as a number of angel investors, including OpenAI's Greg Brockman, Figma's Dylan Field, Elad Gil, Naval Ravikant, Daniel Gross and Lachy Groom. Built around standard Jupyter notebooks, Deepnote wants to provide data scientists with a cloud-based platform that allows them to focus on their work by abstracting away all of the infrastructure. So instead of having to spend a few hours setting up their environment, a student in a data science class, for example, can simply come to Deepnote and get started. In its current form, Deepnote doesn't charge for its service, despite the fact that it allows its users to work with large data sets and train their models on cloud-based machines with attached GPUs. As Deepnote co-founder and CEO (and ex-Mozilla engineer) Jakub Jurových told me, though, he believes that the most important feature of the service is its ability to allow users to collaborate.