Build, Deploy, and Monitor ML Models in Data 360 : The complete Einstein Studio Demo playlist

Welcome to the Einstein Studio blogs aimed to help Data 360 users build predictive models easily.

Read all our previous blogs here: Model Builder – salesforceblogger.com

👋 Intro to the Einstein Studio Demo playlist

If you have been following our previous blogs, you have seen how Einstein Studio makes it possible to go from raw Data 360 data to production-ready predictive models — no notebooks, no code, no separate ML team required.

Each of our blogs covered How-to articles or Best practices on framing problems or building models.

This post is different. Instead of one topic, this is the full playlist — a demo-by-demo walkthrough for the end-to-end ML Lifecycle, starting from a business problem taking it all the way through training, deployment, inference, and monitoring and for each of the models in detail.

At the end of this demo playlist, you should be able to start your own ML project in Einstein Studio.

🚀 Let’s dive in.

🏗️ Section 1: Building a model

🎯 Audience: ML-curious admins and data analysts taking their first swing at a model.

✨ Outcome: Go from a fuzzy business problem to a trained, deployed binary classification model

1. Use case intro — Loan approval, and how to convert a business problem into ML
2. Data prep for features and goal column — via Transforms, CIs and Data Graphs
3. Building a binary classification model
4. How to decide which algo to use?
5. Assessing model Training metrics + Alerts + SHAP and Deploying to Production

⚡ Section 2: Inferencing

🎯 Audience: Teams with a trained model who now need predictions where the business actually works.

✨ Outcome: Deliver predictions to segments, records, flows, and agents — pick the right channel every time.

6. Inferencing channels — how to decide which one to use
7. Inferencing via Batch Transforms
8. Inferencing via Predict Jobs
9. Inferencing via Flows
10. Using predictions in Segments (Actionable Audiences)
11. Displaying predictions in object pages / Core via LWC, fields, related lists
12. Using predictions in agents as context

🩺 Section 3: Monitoring

🎯 Audience: Owners of a model already running in production.

✨ Outcome: Spot drift, data-quality breaks, and lineage gaps before the business notices.

13. Monitoring — live data quality
14. Monitoring — drift + retraining
15. Model Lineage — list and graph view

🧩 Section 4: Other model types in Einstein Studio

🎯 Audience: Builders past their first binary model, ready to fit the model shape to the business shape.

✨ Outcome: Know when to reach for regression, multiclass, clustering, forecasting, or NLP — and build one.

16. Building models — Regression
17. Building models — Multiclass
18. Building models — Clustering + label generation
19. Building models — Forecasting
20. Building models — Topic Classification and Sentiment

🚀 Section 5: …and more

🎯 Audience: Power users pushing the platform’s edges — templates, generative, reuse at scale.

✨ Outcome: Ship faster with pre-built model templates and take ownership of your generative models. 

21. Creation of Model Templates (Coming soon)
22. Building models using AI Model Templates
23. Managing Generative Models

🎬 Wrapping up

That’s the full Einstein Studio Model Builder playlist. If there’s a topic you’d love to see next, whether it’s a quick overview, a deep technical dive, or something in between, drop a comment on the LinkedIn post or reach out to us.

Written by