AI as a Platform (AIaaP) for Enterprise

AI has become critical for companies to gain competitive advantages and enable digital transformation. However, seamlessly integrating AI capabilities into business applications remains challenging.

AI as a Platform (AIaaP) aims to accelerate enterprise AI adoption by providing internal teams robust access to production-grade AI models with governance, tooling, and infrastructure to build business solutions rapidly and responsibly.

In this blog, I will cover the need for internal AI platforms, where AIaaP fits organizationally, how engineering teams will leverage it, use cases, integrating multiple models like Claude-2, GPT-4, PaLM-2 and LLaMA-2, and best practices based on examples.

The Need for Enterprise AI Platforms

First, what are the core problems an internal AI platform aims to solve?

• Model fragmentation — Models scattered across siloed teams and cloud vendors
• Operational overhead — Maintaining production AI is complex with limited expertise
• Technology mismatch — Notebooks and prototypes don’t translate to apps easily
• Poor discoverability — No centralized catalog makes reusing models difficult
• Limited governance — Lack of visibility into model metrics, usage and costs
• Accessibility — Complex for business teams to leverage AI autonomously
• Responsible AI — Hard to monitor model ethics, fairness, and explainability

As a result, despite large investments in AI initiatives, companies struggle to achieve enterprise-wide adoption, hampering ROI.

An internal AI platform addresses these issues by making models consumable throughout the organization with appropriate oversight.

AIaaP Organization and Adoption

Typically, an enterprise AI platform starts as an initiative driven by internal IT, MLOps, architecture, or innovation teams.

It aims to provide self-service access to AI services, best practices, and tooling to application developers, data scientists, and business teams throughout the company.

For example:

Application Teams

• Add conversational interfaces via chatbot APIs
• Integrate virtual assistant capabilities
• Build predictive analytics into apps
• Implement search, recommendations, personalization

Data Scientists

• Leverage production models for experiments
• Contribute new models to the platform
• Use platform tooling like notebooks, feature stores

Business Teams

• Consume analytics APIs for forecasts and insights
• Create automated document summarization
• Monitor customer sentiment analysis
• Translate content to new languages

The goal is scaling access to shared, governed AI capabilities from a centralized platform.

Adoption starts with pilot applications and users, then expands organically as the platform value becomes apparent. Promoting the platform, documenting integrations, and evangelizing help drive maturity.

Now let’s examine a reference architecture for an enterprise AIaaP.

AIaaP Reference Architecture

A high-level AIaaP reference architecture consists of:

User Interfaces

• Portal — Discover, browse, manage models
• SDKs — Integrate into apps in different languages
• Notebooks — Interactively use models
• CLI — Scripting and automation

Model Storage

• Model registry — Catalog model metadata and versions
• Object storage — Store model files and artifacts
• Feature store — Shared datasets for training and serving

Infrastructure

• Container registry — Manage Docker containers for models
• Kubernetes — Dynamically scale model serving
• Serverless — Scalable on-demand prediction APIs

Monitoring and Governance

• Usage metrics — Model and resource consumption
• Drift monitoring — Data and prediction monitoring
• Access controls — Manage model authorization
• Cost transparency — Visibility into spending

This covers the full lifecycle — from discovering models to deploying them in applications with oversight.

Next let’s dig deeper into some key components like the model registry and development interfaces.

Model Registry

The model registry serves as the system of record for models including:

• Model metadata — Name, version, owner, inputs, outputs
• Model lineage — Training code and data sources
• Model artifacts — Files like weights, TensorFlow graphs
• Deployments — Endpoints, containers, infrastructure
• Model performance — Accuracy, fairness, drift metrics
• Usage metrics — Request volume, latency, errors

For example, a model registry entry:

name: Invoice Processing Model
version: 1.2.4
owner: Finance Team
description: Extracts fields from invoice PDFs 
inputs: 
  - invoice_pdf
outputs:
  - invoice_number
  - invoice_date
  - total_amount
evaluation:
  accuracy: 0.92
  precision: 0.81
  recall: 0.94
deployed:
  - endpoint: apigw.company.com/invoice-processor

This consolidated metadata helps teams effectively discover, understand, and consume shared models.

Popular options to build a model registry include MLflow, Seldon Core, Algorithmia, or custom solutions.

Interfaces for Developers

Developers require a variety of interfaces to consume, extend, and manage models:

Python SDK

A Python SDK provides a clean interface for integrating with models:

from company_ai import Client
client = Client()
response = client.extract_invoice(pdf_file)

This abstracts away deployment details and authentication.

Interactive Notebooks

Notebooks like Jupyter allow interactively querying models:

from company_ai.model_catalog import get
sentiment_model = get("sentiment_analyzer")
sentiment = sentiment_model.predict("This produces amazing results!")

Notebooks help explore endpoints and rapidly integrate models.

CLI

A CLI provides automation and devops capabilities:

# Register model
ai register model.py --name mymodel
# List models
ai ls
# Generate SDK for model
ai sdk generate MyModel python

The CLI speeds up CI/CD and scripting model operations.

Documentation

Comprehensive docs enable discovery and integration guidance. Auto-generated SDK docs reduce friction.

With these interfaces, platform adoption accelerates across the organization.

Governance and Security

Robust governance around model usage, monitoring, and security ensures responsible and compliant operations.

For example:

• Role based access control for models
• Model approval workflows to control publishing
• Monitoring for bias, explainability, and drift
• Job-level model encryption and privacy
• Resource consumption controls and quotas
• Model deprecation policies

With integrated governance, risks are mitigated even as accessibility improves.

Now let’s look at some usage examples and patterns.

Platform Usage Examples

Here are some examples of teams leveraging an AIaaP:

Cloud Engineering Team

The cloud engineering team utilizes the AIaaP to deploy and scale the underlying infrastructure for model serving. This includes leveraging containers, Kubernetes, serverless platforms, and cloud services to host models and provide robust APIs for consumption. They contribute best practices around scaling, availability, and cost optimization back to the AIaaP.

Example:

• Implements a multi-cloud strategy for model deployment leveraging Anthos and Azure Arc to provide hybrid portability
• Automates provisioning of GPU clusters to scale intensive model training workloads
• Optimizes model containers for cost efficient inference serving on Kubernetes
• Optimizes model containers for cost efficient inference serving on Kubernetes

MLOps Team

Example:

• Adds a new NLP model to the model registry after evaluating multiple HuggingFace models
• Retrains chatbot models weekly with the latest support transcripts using the AIaaP pipelines
• Monitors production model drift using the built-in dashboard and gets alerts when accuracy drops

The MLOps team leverages the AIaaP model registry to track experiments, model lineage, model performance data, and manage model versions. They leverage the AIaaP pipelines for retraining models on new data. The platform provides ML infrastructure and tools that the MLOps team can standardize on.

DevOps Team

Example:

• Creates a virtual assistant chatbot module using the Conversation API to integrate into a customer portal
• Performance tests invoice processing API scalability before shipping to handle load
• Enables authentication using existing corporate SSO for an AI search service

The DevOps team uses the AIaaP CLI and APIs to automate model deployment, testing, and monitoring. The AIaaP integration helps streamline implementing chatbots, personalization, search, and other AI-powered features into products the DevOps team manages. They provide feedback on developer experience to continuously improve the platform.

Developers

Example:

• Adds personalization to a mobile app using the Recommendation SDK
• Containerizes a legacy Perl script and publishes it as an AI service
• Queries forecasting models from notebooks to build a demand reporting dashboard

Developers interact with the AIaaP via SDKs, notebooks, and documentation to integrate AI capabilities into applications. The self-service access and abstraction of the platform allows them to focus on business logic rather than model implementation details.

Business Teams

Example:

• Trains a classifier to tag customer support tickets using low-code tools
• Generates summaries of lengthy product reports using the NLP APIs
• Analyzes sentiment trends on customer feedback surveys using pre-built analytics

Business teams are able to leverage AI models like demand forecasting, customer churn prediction, and document analysis through APIs and interfaces exposed by the AIaaP. This allows incorporating AI into business processes and workflows with minimal ML expertise.

The AIaaP becomes a hub bringing together efforts from teams across the organization to facilitate AI adoption at scale.

Personalized Recommendations

The retail team builds a next product recommendation model trained on their product and customer data. They contribute the model to the platform, allowing other teams to leverage it.

The web team adds an SDK call to generate recommendations for logged in users. The mobile apps team also integrates this shared model for consistency.

Intelligent Chatbots

The customer support team creates a conversational model to understand and resolve common support queries.

They publish it on the platform so other teams can leverage the same model to answer questions relevant to their domains.

This accelerates extending chatbot capabilities consistently across the company.

Invoice Processing

The finance team trains an invoice parsing model to extract structured fields from vendor PDFs.

They containerize it and register with the model registry so other departments can leverage it for their invoice processing needs.

Unified Analytics

Data teams across divisions publish their analytics models to the platform. Other groups can rapidly consume these models via SDKs and APIs instead of rebuilding expertise.

Sharing analytics models accelerates data insights company-wide.

In each case, central availability and governance prevents duplicate efforts while increasing capabilities.

Enterprise AI platform accelerate practices and deliver value:

Accelerating DevOps

• Automated deployment and testing of models using infrastructure-as-code
• Shared libraries and tooling simplifies adding AI to apps
• Centralized model monitoring and alerts integrated into devops stacks
• Rapid iteration on models with automated retraining pipelines
• Better coordination across app dev, data science, and ops teams

Boosting ML Engineering

• Easy access to production models speeds up experiments
• Compute infrastructure and tooling reduces overhead
• Promotes ML engineering best practices and standards
• Effortless sharing of models and collaboration

Enhancing Data Science

• Discover, reuse curated datasets and feature pipelines
• Accelerate building on pre-trained foundation models
• Promotes reusability of models across business units
• Increased visibility and impact sharing models

Value Proposition

• Accelerates time-to-value of AI initiatives
• Reduces model development overhead and duplication
• Maintains robustness, governance, and control at scale
• Democratizes access to AI capabilities across the enterprise
• Unlocks productivity by amplifying expertise company-wide

The end goal is transforming AI from isolated pockets of experimentation to an enterprise-wide capability that becomes a force multiplier for every team.

Architectural Patterns

Some good architectural patterns for leveraging AIaaP include:

• Thin client — Client apps call AI APIs to offload processing and scale elastically. No model logic needed on client.
• Shared microservices — Centralized AI microservices called from different apps needing same capability.
• Hub and spoke — Dedicated AI services deployed alongside apps to handle local integration. Central spokes connect to shared core AIaaP.
• Layered services — Chains of simple AI services combined to handle complex workflows.
• Orchestration — Central AIaaP coordinates calling multiple AI services to power end-to-end processes.

These patterns optimize integration overhead, governance, and reuse across large enterprises.

Integrating Multiple AI Models

Powerful new multi-framework models like Claude, LLaMA, and FLAN enable consolidating capabilities into unified endpoints.

For example, an AI assistant could combine:

• Claude — Conversational foundation
• GPT-4/LLaMA-2 — Code generation and technical assistance
• Parti — Cross-language understanding
• FLAN — Multimodal reasoning across vision, language, audio

Chaining these together provides:

This allows tailoring an optimal blend of models for each application need.

The AIaaP provides the infrastructure to serve this efficiently while coordinating multi-stage interactions cohesively.

The Future of Enterprise AI

Over time, AI platforms will continue gaining capabilities, ultimately converging into an intelligent fabric that permeates enterprise software systems.

Some future possibilities include:

• End-to-end automation combining task-specific AI into workflows
• Embedding AI universally into apps and interfaces via code auto-completion
• Democratization for business users via natural language interfaces
• Enterprise knowledge graphs tying together tribal knowledge and documents
• Autonomous embodied agents for multimodal assistance
• Continual learning enabling highly adaptive systems over time
• Seamless blending of symbolic and neural approaches

Enterprise AI still remains early but the trajectory points to AI transcending siloed applications into an ambient intelligence accelerating every facet of business.

Key Takeaways

Some key recommendations on implementing an enterprise AI platform:

• Start with high value pilot applications to demonstrate benefits
• Focus on ease of integration, developer experience, and governance
• Build thoughtfully with enterprise scale and longevity in mind
• Become hub for model discovery, access, expertise sharing
• Democratize AI usage beyond central data science teams
• Enable self-service access balanced with oversight guardrails
• Support open architectures that avoid lock-in
• Plan for ongoing evolution vs static solution

Done right, an AI platform amplifies skills and impact of every technologist and business user — unlocking the full potential of AI across the enterprise.

Conclusion

In this blog, I have concluded core motivations for implementing an internal AI platform and how it can accelerate AI adoption for engineering and business teams company-wide.

AIaaP solves challenges like model fragmentation, poor discoverability, lack of governance, and technology mismatches hindering the transition from pilots to production and enterprise-wide usage.

By providing shared access to governed models using interfaces like SDKs, notebooks, and CLI, AIaaP unlocks huge productivity gains and amplifies expertise throughout the organization. Architectural patterns like thin clients and orchestration optimize usage at scale.

Powerful multi-framework models enable consolidating diverse AI capabilities onto the platform. And looking ahead, AIaaP lays the foundation for embedding intelligence seamlessly across business systems.

Internal AI platforms have the potential to transform robust and responsible AI usage from isolated pockets into a true enterprise-wide capability multiplying the effectiveness of every team.