Effective ML Workflows
Integrating Dash Enterprise within the ML Lifecycle
Recorded on March 29, 2022
In this webinar, we demonstrate how organizations can improve the likelihood of a successful outcome by building enterprise-grade Dash apps to visualize the data in every stage of the ML lifecycle. This includes exploratory data analysis, explainability, feature development, model deployment, and model monitoring. By publishing analytic apps on each stage of the ML lifecycle, data scientists can give stakeholders access to intermediate insights and opportunities to realign objectives. Models can be presented to stakeholders as interactive web apps rather than APIs or notebooks.
Dash Enterprise offers a better way to visualize machine learning data and increases the chance of a successful adoption. The platform empowers data scientists to transform Jupyter notebooks and Python scripts into interactive web applications and dashboards that enable actionable insight throughout the project’s lifecycle.
Tune into this on-demand session with Plotly’s engineers to learn about:
- Visualizing data in Dash apps in every stage of the ML lifecycle, including exploratory data analysis, visualizing data from models deployed as APIs, visualizing explainability with SHAP, and more
- Augmenting your data and Dash apps by running scikit-learn analytics in real-time Dash callbacks
- Best practices building analytic apps for machine learning including deployment, authentication, scaling, and embedding
- Weighing the pros/cons of building ML analytic apps and dashboards with Dash Enterprise vs BI vs a full-stack development team