The Rise of AI Functions: Moving Beyond Traditional Data Pipelines

In today’s AI landscape, developers spend more time wrestling with data pipelines than building innovative solutions. As companies race to integrate multimodal AI capabilities—from LLMs to computer vision—the complexity of managing data transformations, model inference, and versioning has reached a breaking point. Enter the era of AI functions: a paradigm shift that’s revolutionizing how we build AI applications.

The Problem with Traditional Pipelines

Traditional data pipelines weren’t designed for the AI era. They struggle with:

• Complex Data Types: Managing video frames, embeddings, and unstructured text requires intricate custom code
• Costly Recomputations: Minor changes often trigger full dataset reprocessing
• Lost Lineage: Tracing model outputs back to source data becomes a detective mission
• Development-Production Gap: Code that works locally needs significant refactoring for production

These challenges force teams to build and maintain extensive infrastructure instead of focusing on their core AI innovation.

Introducing AI Functions: A Declarative Approach

AI functions represent a fundamental shift in how we handle AI workflows. Instead of building complex pipelines, developers express their intent through declarative functions. Here’s a real-world example:

# Traditional Pipeline (Simplified)
def process_videos(video_paths):
    frames = []
    for video in video_paths:
        video_frames = extract_frames(video)
        for frame in video_frames:
            processed = preprocess_frame(frame)
            detection = run_model(processed)
            frames.append((frame, detection))
    return frames

# With Pixeltable
videos = pxt.create_table('videos', {'video': pxt.VideoType()})
frames = pxt.create_view(
    'frames',
    videos,
    iterator=FrameIterator.create(video=videos.video)
)
frames['detections'] = yolox(frames.frame)

The difference is striking. The AI functions approach:

• Eliminates Boilerplate: No need to manage frame extraction, caching, or error handling
• Updates Incrementally: Only processes new or changed data
• Maintains Lineage: Automatically tracks relationships between data and model outputs
• Works in Development and Production: The same code runs everywhere

Real-World Impact

Companies adopting AI functions are seeing dramatic improvements:

• 70% Reduction in compute costs through intelligent incremental processing
• 90% Less Time spent on pipeline maintenance
• Zero Time lost to dev-prod environment mismatches
• Complete Reproducibility of all results

Beyond Basic Transformations

AI functions shine in complex scenarios:

Multimodal Processing

content = pxt.create_table('content', {
    'video': pxt.VideoType(),
    'audio': pxt.AudioType(),
})

# Cross-modal processing as simple functions
content['audio'] = extract_audio(content.video)
content['transcript'] = openai.transcriptions(content.audio)
content['summary'] = gpt4(content.transcript)

RAG Applications

docs = pxt.create_table('knowledge_base',
    {'document': pxt.DocumentType()})

chunks = pxt.create_view(
    'chunks',
    docs,
    iterator=DocumentSplitter.create(
        document=docs.document,
        limit=300
    )
)

# Automatic embedding generation and indexing
chunks['embeddings'] = sentence_transformer(chunks.text)
chunks.add_embedding_index('text', string_embed=e5_embed)

The Future of AI Development

As AI becomes more prevalent across industries, the need for simplified, maintainable AI workflows grows. AI functions represent more than just a technical improvement—they’re enabling a new generation of AI applications that would be impractical to build with traditional approaches.

Key benefits include:

• Rapid Iteration: Experiment with different models and approaches without rebuilding pipelines
• Cost Efficiency: Only process what’s needed, when it’s needed
• Quality Assurance: Complete lineage tracking ensures reproducibility
• Focus on Innovation: Spend time on AI logic, not infrastructure

Getting Started

The transition to AI functions doesn’t require a complete infrastructure overhaul. Teams can start small:

• Identify a complex pipeline that’s causing maintenance headaches
• Convert it to AI functions, focusing on the most problematic parts
• Measure the impact on development velocity and compute costs
• Gradually expand to other workflows

The rise of AI functions marks a turning point in how we build AI applications. By eliminating the complexity of traditional pipelines, they’re enabling teams to focus on what matters most: building innovative AI solutions that drive real business value.

The future of AI development is declarative, efficient, and focused on outcomes rather than infrastructure. The question isn’t whether to adopt AI functions, but how quickly you can start benefiting from them.

Ready to transform your AI workflows? Check out our getting started guide or go to our Github to learn more.