Salesforce Einstein Model Builder, a part of Einstein 1 Studio, provides businesses with a way to create AI models customized to specific needs, all without requiring advanced data science skills. This tool offers a guided, user-friendly experience that enables users to harness Salesforce data to train machine learning models effectively.
Model Builder gives users the freedom to select from various large language models (LLMs) based on their business requirements or data storage preferences. It offers three main choices:
- Salesforce Shared LLMs: Salesforce’s Shared LLMs, including models like CodeGen, CodeT5+, and CodeTF, allow companies to automate code generation, simplify business processes, and improve incident detection. These AI tools, developed by Salesforce AI Research, are accessed through a secure gateway, allowing organizations to use Salesforce’s AI resources safely.
- Hosting Third-Party LLMs: Salesforce’s Einstein platform also hosts third-party LLMs from providers like Amazon, Anthropic, and Cohere within Salesforce’s infrastructure. This approach allows businesses to select models that fit their needs while keeping customer data and prompts secure within Salesforce.
- Bring Your Own Model (BYOM) Option: For organizations with custom-trained models, the “Bring Your Own Model” (BYOM) option allows integration of external models trained on platforms such as Amazon SageMaker or Google Vertex AI. These models connect directly to Einstein through the Einstein Trust Layer, allowing businesses to maintain control over their data in their own infrastructure.
With Shared LLMs, hosted third-party models, and BYOM, Salesforce’s adaptable ecosystem empowers businesses to adopt AI solutions that suit their operations and data needs.
By centralizing data and model management in a single, click-based interface, Model Builder allows companies to develop AI-driven insights tailored to their goals, improving decision-making and maximizing the potential of their data.
How Large Language Models Transform Business Operations?
- Enhanced Customer Interactions: LLMs, trained on vast datasets, understand the context and user intent, enabling businesses to improve customer service through accurate, context-aware responses.
- Ease of Integration: Advances in AI infrastructure allow companies to implement LLMs with minimal coding, using prompt templates to guide responses aligned with business goals.
- Private and Secure LLMs: Businesses can train private LLMs on industry-specific data in secure cloud environments, ensuring relevant and compliant responses while reducing data exposure risks.
- Data Privacy and Decentralization: Decentralized data sources protect individual privacy by limiting access to customer data, aiding compliance and security efforts.
- Complementary to Traditional AI: Traditional AI can predict customer behaviours, while LLMs translate predictions into actionable insights, enhancing personalization and targeted marketing.
- Operational Efficiency: By automating tasks like summarizing cases, creating personalized content, and drafting responses, LLMs free employees to focus on strategic, high-value work.
- Cost and Time Savings: LLMs streamline repetitive processes, boosting productivity and driving growth through efficient and responsive business operations.
Preparing Data for Your Model
The most significant work in building an accurate predictive model comes from preparing the data. In Salesforce’s Einstein Studio, data specialists play a key role here because they understand the data best. Model Builder allows you to train a model using a Data Model Object (DMO) from Salesforce Data Cloud.
Tools like Batch Data Transforms are available to organize and clean the data, creating a single DMO ready for model training.
Want to Learn Salesforce Flows? Checkout our Salesforce Flow Course
Steps to Build Model from Scratch
Step 1: Pick Your Data Source
Start by selecting the data source that will train your model. Make sure your data meets these requirements:
- Rows: At least 400 rows, with a maximum of 20 million rows (5 million if using the XGBoost algorithm).
- Columns: At least 3 (1 outcome variable and two predictors), up to a maximum of 50.
Step 2: Filter Your Data
You can use all the data or apply filters to focus on specific records.
To filter: Select the Filtered Set of Records and define the rules.
Use logical options:
- All Conditions Are Met: Filters apply only when all conditions are true.
- Any Condition Is Met: Filters apply if at least one condition is true.
Specify the details using fields, operators, and values, such as choosing data within a date range or above a certain threshold.
Also Read: What is Agentforce in Salesforce?
Step 3: Set Your Goal
Define the purpose of your model. What problem are you trying to solve? Choose a target variable, like a key performance indicator (KPI), and decide whether to increase or decrease it. Examples include:
- Increase: Revenue or customer lifetime value.
- Decrease: Customer churn or operational costs.
Make sure your target variable is measurable and directly connected to your business needs.
Step 4: Pick an Algorithm
Decide how your model will analyze the data. Einstein offers three algorithms:
- GLM (Generalized Linear Model): Works well when the relationships between variables are simple. It can also show how certain factors combine to affect the outcome.
- GBM (Gradient Boosting Machine): Useful for handling complex patterns in the data.
- XGBoost (Extreme Gradient Boosting): A faster, more efficient version of GBM that handles large datasets effectively.
You can also let Einstein automatically select the best algorithm for your model.
Step 5: Train Your Model
After setting everything up:
- Review your selections and make any final changes.
- Save the model with a clear name and description.
- Click Save & Train to start training.
Einstein will process the data and create your model. You’ll see updates during training, and once it’s complete, the model will appear in the Predictive Models tab in Einstein Studio.
FAQs
1. What is Model Builder in Salesforce?
Model Builder in Salesforce is a tool within Einstein that allows users to create predictive models using historical data. It uses AI, machine learning, and statistical analysis to forecast outcomes and uncover patterns that support data-driven decision-making.
2. Do we need technical expertise to use Model Builder?
No, Model Builder is user-friendly and does not require advanced programming knowledge. Its guided process allows business users and data professionals to create models step-by-step, from selecting data to training the model.
Conclusion
Salesforce Einstein Model Builder helps businesses create AI models using Salesforce or external data without requiring technical expertise. With options like Salesforce LLMs, third-party models, and custom BYOM integrations, it provides flexibility while keeping data secure.
