Important questions:

• What are the people's needs?
• Why is the problem important?
• What are the problem constraints?
• What are the important variables to consider?
• What are the relevant metrics?
• What is the data we need?
• Do we need ML?
• ...

The systems engineering approach is better equipped than the ML community to facilitate the adoption of this technology by prioritising the problems and their context before any other aspects.

Results contrast with the way we work today.

"Move Fast and Break Things" (Zuckerberg, 2014)

• Move fast and deliver working software
• Embrace failure as a learning opportunity
• Prioritise speed and agility
• ...

AI as a Service (AIaaS) enables us to access and expose AI capabilities over the internet. We can integrate AI tools such as machine learning models, natural language processing, and computer vision into our applications leveraging SOA and microservices features.

MLOps is a set of practices and tools that support deploying and maintaining ML models in production reliably and efficiently. The goal is to automate and streamline the ML pipeline. These practices and tools include all the pipeline stages from data collection, model training, and deployment to monitoring and governance. We aim to ensure that ML models are robust, scalable, and continuously delivering value.

• Automated data collection
• Automated model training and validation
• Continuous integration and continuous deployment
• Monitoring and logging
• Governance and compliance
• Scalability and reliability

import mlflow
import logging
from datetime import datetime

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class ModelMonitor:
    def __init__(self, model_name):
        self.model_name = model_name
        mlflow.set_tracking_uri("http://localhost:5000")
    def log_prediction(self, input_data, prediction, 
                      actual=None, model_version="1.0"):
        """Log model predictions for monitoring"""
        with mlflow.start_run():
            mlflow.log_params({
                "input_size": len(input_data),
                "model_version": model_version,
                "timestamp": datetime.now().isoformat()
            })
        
            mlflow.log_metric("prediction", prediction)
        
            if actual is not None:
                mlflow.log_metric("actual", actual)
                mlflow.log_metric("error", abs(prediction - actual))
                
            logger.info(f"Prediction logged: {prediction}")
            
    def monitor_drift(self, current_stats, baseline_stats):
        """Monitor for data drift"""
        drift_score = self.calculate_drift(current_stats, baseline_stats)
        mlflow.log_metric("drift_score", drift_score)
        if drift_score > 0.1:  # Threshold
            logger.warning(f"Data drift detected: {drift_score}")

import mlflow
import logging
from datetime import datetime

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class ModelMonitor:
    def __init__(self, model_name):
        self.model_name = model_name
        mlflow.set_tracking_uri("http://localhost:5000")
    def log_prediction(self, input_data, prediction, 
                      actual=None, model_version="1.0"):
        """Log model predictions for monitoring"""
        with mlflow.start_run():
            mlflow.log_params({
                "input_size": len(input_data),
                "model_version": model_version,
                "timestamp": datetime.now().isoformat()
            })
        
            mlflow.log_metric("prediction", prediction)
        
            if actual is not None:
                mlflow.log_metric("actual", actual)
                mlflow.log_metric("error", abs(prediction - actual))
                
            logger.info(f"Prediction logged: {prediction}")
            
    def monitor_drift(self, current_stats, baseline_stats):
        """Monitor for data drift"""
        drift_score = self.calculate_drift(current_stats, baseline_stats)
        mlflow.log_metric("drift_score", drift_score)
        if drift_score > 0.1:  # Threshold
            logger.warning(f"Data drift detected: {drift_score}")

Focus on Operations

Focus on Operations

Focus on Operations

• Separation of concerns
• High availability
• Scalability
• Low latency

Focus on Operations

• Separation of concerns
• High availability
• Scalability
• Low latency

The Data Dichotomy: “While data-driven systems are about exposing data, service-oriented architectures are about hiding data.” (Stopford, 2016)

Data-Oriented Architectures

Data-First Systems

• Data is available by design
• Traceability and monitoring
• Interpretability

Data-Oriented Architectures

Data-First Systems

• Data is available by design
• Traceability and monitoring
• Interpretability

Data-Oriented Architectures

Prioritise Decentralisation

• Super-low latency requirements
• Privacy by design

Data-Oriented Architectures

Openness

• Sustainable solutions
• Data ownership

Overview

• Service Oriented Architectures
• AI as a Service
• MLOps
• Data-Oriented Architectures

Overview

• Service Oriented Architectures
• AI as a Service
• MLOps
• Data-Oriented Architectures

Course Overview

• AI History and ML Context
• Scientific Approach
• ML Adoption Process
• The Systems Engineering Approach
• Data Science Process and ML Pipeline
• AI as a Service

"It seems to me what is called for is an exquisite balance between two conflicting needs: the most skeptical scrutiny of all hypotheses that are served up to us and at the same time a great openness to new ideas. Obviously those two modes of thought are in some tension. But if you are able to exercise only one of these modes, whichever one it is, you’re in deep trouble. (The Burden of Skepticism, Sagan, 1987)

The systems engineering approach is better equipped than the ML community to facilitate the adoption of this technology by prioritising the problems and their context before any other aspects.