Einblick: a canvas-based approach with Python
At Einblick, we are reimagining the data science workflow, and producing a next generation data science platform for the community. As a part of these larger pursuits, Einblick is thrilled to be entering the holiday season as one of the Python Software Foundation’s newest Partner-level sponsors.
We, as part of the data community, need to adapt to an increasingly dynamic world. As such, we need to be ready to pivot quickly, and adjust to new challenges, faster than ever before, both within our own data science teams, and within our larger organizations.
Given this environment, we could not imagine a better cause to support than the Python ecosystem, to help ensure that the libraries, packages, documentation, and everything in between continue to be well-maintained. We are grateful to the stewardship of the Python community thus far, and how they have made a tool like Einblick possible.
Flexibility: critical to data science
There are many reasons why Python has become the backbone of the data science community. As our Head of Data Science, Benedetto Buratti states, “Python has been a wonderful companion in my data science and machine learning journey since my undergraduate years. The beauty of Python is that its flexibility allows for quick prototyping and allows for the creation of complex tools like Einblick.”
Einblick extends Python’s flexibility to mirror the need for the data science workflow, to be flexible–to be able to pivot, to be able to chase down different trains of thought as you explore data. After all, data science requires not only technical skills, but also curiosity, storytelling, and communication with stakeholders. As the father of exploratory data analysis, John Tukey, posited decades ago, we cannot focus solely on confirmatory data analysis, which seeks to confirm existing hypotheses. Many times, we need to ask our data and our stakeholders questions with an open mind.
Python is a powerful tool that allows data scientists to test out different models, build visualizations, and transform data as needed. But a flexible language can only take the data science community so far, when legacy tools, like Python notebooks, are rigid and too highly structured.
To combat the friction between the flexibility of Python, and the rigidity of Python notebooks, we created Einblick as a data science canvas. The canvas environment gives you the ability to tackle any part of the data science workflow such that you can branch from one part to another quickly and easily, all in the same workspace. You can:
- Build complex machine learning pipelines in parallel, using our progressive computation engine
- Use Python cells to create custom code, models, and visualizations
- Create visualizations quickly for exploratory data analysis using the built-in chart cell
- Use SQL cells to write custom queries
- Mix Python and no-code operators for data cleaning tasks like filtering, joining, and profiling data
Collaboration: the Python community
One part of the Python ecosystem our CEO, Emanuel Zgraggen most appreciates, is “the approachable and well-maintained libraries that you can use to solve all sorts of problems.” These Python libraries, their associated documentation, forums, and open source contributors are all due to the steadfast and committed Python community that push the software forward. Matplotlib, seaborn, scikit-learn, statsmodels, pandas, and numpy are just a few libraries and packages that provide core data science functionality. This incredible suite of software is only possible through collaboration.
Similarly, when working on data science problems, you need input from a variety of stakeholders. You need data engineers to organize and publish data, data analysts to create dashboards, domain experts to provide context, data scientists to build models, and executives to make high-level decisions.
Given how important it is for multiple stakeholders to align, we need data science tools that are innately collaborative. In fact, one of the main pain points for data scientists is that it is too easy to become siloed from team members and other stakeholders. To address this issue, at Einblick, we have incorporated collaboration into the DNA of our product.
- Bring your team into your canvas, toggling View and Edit permissions as needed
- Live collaborate with team members and stakeholders using Einblick’s audio and video capabilities
- Share and collaborate on dashboards for live reporting
- Embed canvases into your live web app
We have a variety of cells that remove the need for repetitive tasks. As we highlighted in our post on the Python Software Foundation website, Python-based operators have played a key role in our work at Einblick:
- Chart cells create different visualizations, including scatter plots, histograms, bar charts, line charts, and heat maps
- Expression cells support Python 3 syntax, take in a dataframe and add a new column based on a logical expression. We currently support many operations, including arithmetic, comparison, and bitwise operators, as well as mathematical functions
- AutoML cells build more accurate predictive models in much less time than it would take to hand-tune. You just have to select the target and feature columns from a dataframe, as well as the training and testing datasets
You can read more about our story on the Python Software Foundation Success Stories section.
PyCon 2023
As part of our sponsorship, we are thrilled to be attending PyCon 2023! Community is such an integral part of the Python experience, and what better way to show our support than by celebrating the 20th anniversary of PyCon, which will bring together like minded people and organizations to connect and push innovations forward. We know that Python and its community and ecosystem will continue to play a vital role in our company’s development and growth.
Please let us know if you’ll be in Salt Lake City, UT for PyCon 2023, we’d love to hear from you. Otherwise, drop us a line, or join our Discord channel so we can keep building together.
About
Einblick is an agile data science platform that provides data scientists with a collaborative workflow to swiftly explore data, build predictive models, and deploy data apps. Founded in 2020, Einblick was developed based on six years of research at MIT and Brown University. Einblick customers include Cisco, DARPA, Fuji, NetApp and USDA. Einblick is funded by Amplify Partners, Flybridge, Samsung Next, Dell Technologies Capital, and Intel Capital. For more information, please visit www.einblick.ai and follow us on LinkedIn and Twitter.