Vertex AI
Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case.
Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection.
Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.
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Highcharts
Highcharts, a Javascript-based charting library, makes it easy to add interactive charts and graphs to web or mobile projects of any size.
Highcharts is used by more than 80% of the 100 biggest companies in the world, as well as thousands of developers from a variety of industries, including finance, publishing, application development, and data science.
Highcharts is in active development since 2009. It remains a favorite among developers due to its robust feature set and ease-of-use documentation, accessibility features and vibrant community.
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Bokeh
Bokeh simplifies the creation of standard visualizations while also accommodating unique or specialized scenarios. It allows users to publish plots, dashboards, and applications seamlessly on web pages or within Jupyter notebooks. The Python ecosystem boasts a remarkable collection of robust analytical libraries such as NumPy, Scipy, Pandas, Dask, Scikit-Learn, and OpenCV. With its extensive selection of widgets, plotting tools, and user interface events that can initiate genuine Python callbacks, the Bokeh server serves as a vital link, enabling the integration of these libraries into dynamic, interactive visualizations accessible via the browser. Additionally, Microscopium, a project supported by researchers at Monash University, empowers scientists to uncover new functions of genes or drugs through the exploration of extensive image datasets facilitated by Bokeh’s interactive capabilities. Another useful tool, Panel, which is developed by Anaconda, enhances data presentation by leveraging the Bokeh server. It streamlines the creation of custom interactive web applications and dashboards by linking user-defined widgets to a variety of elements, including plots, images, tables, and textual information, thus broadening the scope of data interaction possibilities. This combination of tools fosters a rich environment for data analysis and visualization, making it easier for researchers and developers to share their insights.
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marimo
Introducing an innovative reactive notebook designed for Python, which allows you to conduct repeatable experiments, run scripts seamlessly, launch applications, and manage versions using git.
🚀 Comprehensive: it serves as a substitute for jupyter, streamlit, jupytext, ipywidgets, papermill, and additional tools.
⚡️ Dynamic: when you execute a cell, marimo automatically runs all related cells or flags them as outdated.
🖐️ Engaging: easily connect sliders, tables, and plots to your Python code without the need for callbacks.
🔬 Reliable: ensures no hidden states, guarantees deterministic execution, and includes built-in package management for consistency.
🏃 Functional: capable of being executed as a Python script, allowing for customization via CLI arguments.
🛜 Accessible: can be transformed into an interactive web application or presentation, and functions in the browser using WASM.
🛢️ Tailored for data: efficiently query dataframes and databases using SQL, plus filter and search through dataframes effortlessly.
🐍 git-compatible: stores notebooks as .py files, making version control straightforward.
⌨️ A contemporary editor: features include GitHub Copilot, AI helpers, vim keybindings, a variable explorer, and an array of other enhancements to streamline your workflow.
With these capabilities, this notebook elevates the way you work with Python, promoting a more efficient and collaborative coding environment.
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