AI inference startup Baseten reportedly raising $1.5B months after its last mega-round

Introduction

In the rapidly evolving world of artificial intelligence, inference has become a critical component for deploying AI models in real-world applications. Baseten's recent $1.5B funding round highlights the growing importance of AI inference platforms. In this tutorial, you'll learn how to deploy and run a simple AI model using a basic inference framework, understanding the core concepts behind AI inference that companies like Baseten are building upon.

What You'll Learn

This tutorial will teach you how to:

• Understand what AI inference means
• Set up a basic AI model deployment environment
• Run a simple inference example
• Interpret model predictions

Prerequisites

Before starting this tutorial, you should have:

• Basic computer literacy
• Python installed on your system (Python 3.7 or higher recommended)
• Internet connection for downloading packages
• Text editor or IDE (like VS Code or PyCharm)

Step-by-Step Instructions

Step 1: Understanding AI Inference

What is AI Inference?

AI inference is the process of using a trained machine learning model to make predictions on new, unseen data. Think of it like using a trained teacher to answer questions - the model has been 'taught' patterns, and now it applies that knowledge to new situations.

When companies like Baseten raise millions of dollars, they're investing in making this inference process faster, more reliable, and easier to deploy at scale.

Step 2: Setting Up Your Environment

Installing Required Packages

First, we need to install the necessary Python packages for our AI inference example. Open your terminal or command prompt and run:

pip install scikit-learn numpy

Why this matters: These packages provide the tools we need to create and run our simple machine learning model. Scikit-learn is a popular machine learning library, and NumPy provides numerical computing capabilities.

Step 3: Creating a Simple Model

Building Our First AI Model

Let's create a simple Python script that builds a basic model. Create a new file called simple_model.py and add the following code:

import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

# Create sample data
X = np.array([[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]])
y = np.array([2, 4, 6, 8, 10, 12, 14, 16, 18, 20])

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train the model
model = LinearRegression()
model.fit(X_train, y_train)

print("Model trained successfully!")
print("Model coefficients:", model.coef_)
print("Model intercept:", model.intercept_)

Why this matters: This code creates a basic linear regression model that learns the relationship between input values (X) and output values (y). In real AI systems, this process is much more complex, but it demonstrates the fundamental concept of training a model.

Step 4: Running Inference

Testing Our Model with New Data

Now let's extend our script to perform inference on new data:

# Add this code after the model training section

# Perform inference on new data
new_data = np.array([[11], [12], [13]])
predictions = model.predict(new_data)

print("\nInference Results:")
for i, (input_val, prediction) in enumerate(zip(new_data.flatten(), predictions)):
    print(f"Input: {input_val}, Predicted Output: {prediction:.2f}")

Why this matters: This is the core of what Baseten and similar companies provide - making it easy to take trained models and use them to make predictions on new data. In production environments, this process happens at scale with millions of requests.

Step 5: Running Your Complete Script

Executing Your AI Inference Example

Save your complete script and run it in your terminal:

python simple_model.py

You should see output similar to:

Model trained successfully!
Model coefficients: [2.]
Model intercept: 0.0

Inference Results:
Input: 11, Predicted Output: 22.00
Input: 12, Predicted Output: 24.00
Input: 113, Predicted Output: 26.00

Why this matters: This demonstrates the complete inference workflow - training a model and then using it to make predictions. The model learned that the relationship between input and output is y = 2x, so when we input 11, 12, and 13, we get predictions of 22, 24, and 26 respectively.

Step 6: Understanding Production Inference

How Companies Like Baseten Scale This

While our example is simple, real-world AI inference systems like those at Baseten handle:

• Thousands of model requests per second
• Multiple model types (not just linear regression)
• Model versioning and updates
• Performance optimization and scaling
• Monitoring and logging for production use

Companies like Baseten build infrastructure that makes these complex operations simple for developers and businesses.

Summary

In this tutorial, you've learned the fundamental concept of AI inference by creating and running a simple machine learning model. You've seen how:

1. AI models are trained on existing data
2. Trained models can make predictions on new data (inference)
3. This process is the foundation of AI deployment systems

While our example is basic, it demonstrates the core workflow that companies like Baseten are investing heavily in - making AI inference faster, more reliable, and easier to deploy at scale. As the AI inference market continues to grow, understanding these fundamentals will become increasingly important for anyone working with artificial intelligence technologies.