Anaconda is a distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment.

Jupyter allows you to create and share reports for the client while you develop your machine learning models, analyze data, draw your graphs, or whatever other coding you might need. The open-source application enables the creation of documents containing live code, equations, visualizations, or narrative text. When you finish your document, you can download it as a PDF, HTML web Page, DOC, or most formats needed to send it to clients. You can also extract the code on it to create a script or application and use it in production.

Tensorflow is an open-source machine learning library developed at Google for numerical computation using data flow graphs is arguably one of the best, with Gmail, Uber, Airbnb, Nvidia, and lots of other prominent brands using it. It’s handy for creating and experimenting with deep learning architectures, and its formulation is convenient for data integration such as inputting graphs, SQL tables, and images together.

Scikit-learn is a very popular open-source machine learning library for the Python programming language. With constant updations in the product for efficiency improvements coupled with the fact that its open-source makes it a go-to framework for machine learning in the industry.

Keras is an open-source neural network library written in Python. It is capable of running on top of other popular lower-level libraries such as Tensorflow, Theano & CNTK. This one might be your new best friend if you have a lot of data and/or you’re after the state-of-the-art in AI: deep learning.

Pandas is yet another open-source software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. Pandas works well with incomplete, messy, and unlabeled data and provides tools for shaping, merging, reshaping, and slicing datasets.

Spark MLib is a popular machine learning library. As per survey, almost 6% of the data scientists use this library. This library has support for Java, Scala, Python, and R. Also you can use this library on Hadoop, Apache Mesos, Kubernetes, and other cloud services against multiple data sources.

PyTorch is developed by Facebook’s artificial intelligence research group and it is the primary software tool for deep learning after Tensorflow. Unlike TensorFlow, the PyTorch library operates with a dynamically updated graph. This means that it allows you to make changes to the architecture in the process.

Matplotlib is a plotting library for Python, a library mostly used for data visualization by plotting histograms, scatterplot, 3D plot, etc., and also serves as a numerical extension to the Numpy library. It’s the de facto visualization library used in every data science test case in Python as it makes visualizations easy and interactive giving you the power to produce histograms, scatterplot, 3D plot, image plot, bar charts, power spectra, and many more.

Seaborn is an open-source Python data visualization library based on matplotlib. The main focus of this package is on the visualization of statistical models. visualizations that include heat maps, those which summarize the data but still depict the overall distributions.

Theano Python library is for numerical computation and is similar to Numpy. Some libraries such as Pylearn2 use Theano as their base component for mathematical computation. Theano helps you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

Amazon Sagemaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning models quickly by bringing together a broad set of capabilities purpose-built for machine learning.

Google Vertex AI Workbench is a single development environment for the entire data science workflow. To help you set up an end-to-end notebook-based production environment, Vertex AI Workbench offers a managed notebooks option with built-in integrations. For users who need full control over their environment, Vertex AI Workbench provides a user-managed notebooks option. Both options use Jupyter notebooks, have a preinstalled suite of deep learning packages, and support the TensorFlow and PyTorch frameworks.