Microsoft Build 2025 Preview: What to Expect for Data and AI

Microsoft Build 2025 is approaching, and expectations are high for major announcements in Data and AI. Based on current trends, product roadmaps, and industry direction, here’s what we might see.

Expected Themes

1. AI-First Development

Build 2025 will likely emphasize AI as a core part of every developer workflow:

Expected Announcements:
├── GitHub Copilot Workspace GA
├── Copilot for Azure Portal enhancements
├── AI-assisted debugging in VS Code
├── Natural language infrastructure deployment
└── Copilot for data engineering

2. Agent Platform Maturity

Azure AI Agent Service is expected to mature:

# Speculative: Enhanced Agent SDK at Build 2025
# Using existing patterns from Azure AI and Semantic Kernel

from azure.ai.projects import AIProjectClient
from azure.identity import DefaultAzureCredential
import semantic_kernel as sk
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion

# Multi-agent systems with improved memory
credential = DefaultAzureCredential()
client = AIProjectClient(
    credential=credential,
    endpoint="https://your-project.api.azureml.ms"
)

# Create agent with persistent memory
agent = client.agents.create_agent(
    model="gpt-4o",
    name="data-analyst",
    instructions="You are a data analyst agent.",
    tools=[
        {"type": "code_interpreter"},
        {"type": "file_search"}
    ]
)

# Enhanced orchestration with Semantic Kernel
kernel = sk.Kernel()
kernel.add_service(AzureChatCompletion(
    deployment_name="gpt-4o",
    endpoint="https://your-resource.openai.azure.com/"
))

# Future: Native MCP server support expected

3. Fabric Evolution

Microsoft Fabric is expected to receive significant updates:

Predicted Fabric Updates:
├── Real-Time Intelligence GA enhancements
├── Copilot for all Fabric experiences
├── Cross-cloud data sharing
├── Enhanced governance (Purview integration)
├── Fabric for startups (free tier?)
└── Native vector search in OneLake

4. Model Innovation

New models and capabilities expected:

# Speculative: New model capabilities
from openai import AzureOpenAI

client = AzureOpenAI(
    api_version="2024-12-01-preview",
    azure_endpoint="https://your-resource.openai.azure.com/"
)

# Phi-4: Next generation small model
response = client.chat.completions.create(
    model="phi-4-mini",
    messages=[{"role": "user", "content": "Analyze this data..."}]
)
# Future: On-device deployment options expected

# GPT-4.5 or GPT-5 preview?
response = client.chat.completions.create(
    model="gpt-5-preview",  # Speculative
    messages=[...],
    # Enhanced reasoning capabilities expected
)

# Multimodal improvements
response = client.chat.completions.create(
    model="gpt-4o-next",  # Speculative
    messages=[{
        "role": "user",
        "content": [
            {"type": "video_url", "video_url": {"url": "..."}},  # Native video
            {"type": "text", "text": "Analyze this meeting recording"}
        ]
    }]
)

Data Platform Predictions

1. Unified Data + AI Platform

Current State:
├── Azure AI Foundry (AI development)
├── Microsoft Fabric (Data platform)
├── Power Platform (Low-code)
└── Dynamics 365 (Business apps)

Predicted Convergence:
└── Single unified platform with:
    ├── Seamless data flow
    ├── Integrated AI capabilities
    ├── Unified governance
    └── Single billing/management

2. Vector Search Native in Fabric

# Speculative: Native vector operations in Fabric

# In Fabric Warehouse (T-SQL)
"""
CREATE TABLE documents_with_vectors (
    id INT PRIMARY KEY,
    content VARCHAR(MAX),
    embedding VECTOR(1536)  -- Native vector type (speculative)
);

-- Native vector search (speculative)
SELECT id, content
FROM documents_with_vectors
ORDER BY VECTOR_DISTANCE(embedding, @query_vector)
LIMIT 10;
"""

# Current approach: Use Spark with vector libraries
from pyspark.sql import SparkSession
from pyspark.sql import functions as F
from pyspark.ml.linalg import Vectors
import numpy as np

spark = SparkSession.builder.getOrCreate()

# Read documents
df = spark.read.table("lakehouse.documents")

# Generate embeddings using Azure OpenAI
from openai import AzureOpenAI

client = AzureOpenAI(
    api_version="2024-02-15-preview",
    azure_endpoint="https://your-resource.openai.azure.com/"
)

def get_embedding(text: str) -> list:
    response = client.embeddings.create(
        model="text-embedding-3-large",
        input=text
    )
    return response.data[0].embedding

# Apply to dataframe (using UDF)
from pyspark.sql.functions import udf
from pyspark.sql.types import ArrayType, DoubleType

embed_udf = udf(get_embedding, ArrayType(DoubleType()))
df_vectors = df.withColumn("embedding", embed_udf(F.col("content")))

# Save with embeddings
df_vectors.write.format("delta").saveAsTable("lakehouse.documents_with_embeddings")

3. Real-Time AI Pipelines

# Current approach: Streaming AI with Spark and Azure OpenAI
from pyspark.sql import SparkSession
from pyspark.sql import functions as F
from pyspark.sql.types import StringType
import json

spark = SparkSession.builder.getOrCreate()

# Read from EventHub
stream_df = spark.readStream \
    .format("eventhubs") \
    .options(**eventhub_config) \
    .load()

# Parse events
parsed = stream_df.select(
    F.from_json(F.col("body").cast("string"), schema).alias("data")
).select("data.*")

# AI enrichment function
def classify_risk(transaction_json: str) -> str:
    from openai import AzureOpenAI
    client = AzureOpenAI(
        api_version="2024-02-15-preview",
        azure_endpoint="https://your-resource.openai.azure.com/"
    )

    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{
            "role": "user",
            "content": f"Classify risk level (low/medium/high) for this transaction: {transaction_json}"
        }],
        max_tokens=10
    )
    return response.choices[0].message.content

# Register UDF
classify_udf = F.udf(classify_risk, StringType())

# Apply AI classification
enriched = parsed.withColumn(
    "risk_level",
    classify_udf(F.to_json(F.struct("*")))
)

# Write results
query = enriched.writeStream \
    .format("delta") \
    .outputMode("append") \
    .option("checkpointLocation", "/checkpoints/ai_enrichment") \
    .toTable("enriched_transactions")

Developer Experience Predictions

1. Natural Language Development

# Speculative: Natural language code generation
# Current approach using Azure OpenAI

from openai import AzureOpenAI

client = AzureOpenAI(
    api_version="2024-02-15-preview",
    azure_endpoint="https://your-resource.openai.azure.com/"
)

def generate_pipeline_code(description: str) -> str:
    """Generate data pipeline code from natural language description."""
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {
                "role": "system",
                "content": """You are a data engineering assistant. Generate Python/PySpark
                code for data pipelines based on user descriptions. Use best practices and
                include error handling."""
            },
            {
                "role": "user",
                "content": description
            }
        ]
    )
    return response.choices[0].message.content

# Generate entire pipeline from description
pipeline_code = generate_pipeline_code("""
    Create a data pipeline that:
    1. Reads from Salesforce
    2. Joins with customer master in Fabric
    3. Enriches with AI classification
    4. Writes to gold layer
    5. Refreshes Power BI
""")

print(pipeline_code)

2. AI-Assisted Debugging

# Current approach: AI-assisted error analysis
from openai import AzureOpenAI
import traceback

client = AzureOpenAI(
    api_version="2024-02-15-preview",
    azure_endpoint="https://your-resource.openai.azure.com/"
)

def analyze_error(error: Exception, code_context: str = "") -> dict:
    """Use AI to analyze an error and suggest fixes."""
    error_info = {
        "type": type(error).__name__,
        "message": str(error),
        "traceback": traceback.format_exc()
    }

    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {
                "role": "system",
                "content": """You are a debugging assistant. Analyze the error and provide:
                1. Root cause analysis
                2. Suggested fix
                3. Similar issues that might be related
                Respond in JSON format."""
            },
            {
                "role": "user",
                "content": f"Error: {json.dumps(error_info)}\n\nCode context:\n{code_context}"
            }
        ],
        response_format={"type": "json_object"}
    )

    return json.loads(response.choices[0].message.content)

# When an error occurs
try:
    result = my_pipeline.run()
except Exception as e:
    # AI analyzes the error
    analysis = analyze_error(e, code_context="...")

    print(f"Root cause: {analysis['root_cause']}")
    print(f"Suggested fix: {analysis['suggested_fix']}")
    print(f"Similar issues: {analysis['similar_issues']}")

Enterprise Features

1. Enhanced Security

# Speculative: AI-aware security policies
# Current approach: Use Azure Policy and Purview

security_policy:
  ai_governance:
    - rule: "no_pii_in_prompts"
      action: "redact"
    - rule: "audit_all_ai_calls"
      destination: "azure_monitor"
    - rule: "model_access_by_role"
      config:
        gpt-4o: ["data_scientists", "ml_engineers"]
        gpt-4o-mini: ["all_developers"]

2. Cost Management

# Current approach: Track AI costs with custom logging
from openai import AzureOpenAI
from azure.monitor.opentelemetry import configure_azure_monitor
import logging

configure_azure_monitor()
logger = logging.getLogger(__name__)

class CostTrackingClient:
    """Wrapper to track AI API costs."""

    # Pricing per 1M tokens (example rates)
    PRICING = {
        "gpt-4o": {"input": 2.50, "output": 10.00},
        "gpt-4o-mini": {"input": 0.15, "output": 0.60},
        "text-embedding-3-large": {"input": 0.13, "output": 0}
    }

    def __init__(self, monthly_limit: float = 10000):
        self.client = AzureOpenAI(
            api_version="2024-02-15-preview",
            azure_endpoint="https://your-resource.openai.azure.com/"
        )
        self.monthly_limit = monthly_limit
        self.monthly_spend = 0

    def chat_completion(self, model: str, messages: list, **kwargs):
        response = self.client.chat.completions.create(
            model=model,
            messages=messages,
            **kwargs
        )

        # Calculate cost
        usage = response.usage
        pricing = self.PRICING.get(model, {"input": 0, "output": 0})
        cost = (usage.prompt_tokens * pricing["input"] +
                usage.completion_tokens * pricing["output"]) / 1_000_000

        self.monthly_spend += cost

        # Log for monitoring
        logger.info(f"AI API call: model={model}, cost=${cost:.4f}, total=${self.monthly_spend:.2f}")

        # Alert if approaching limit
        if self.monthly_spend > self.monthly_limit * 0.8:
            logger.warning(f"AI spend at {100*self.monthly_spend/self.monthly_limit:.1f}% of monthly limit")

        return response

# Usage
cost_client = CostTrackingClient(monthly_limit=10000)
response = cost_client.chat_completion("gpt-4o-mini", messages=[...])

What to Watch For

1. Keynote announcements: Major platform changes
2. Model announcements: New versions, capabilities
3. Pricing changes: Often announced at Build
4. Preview releases: Early access to new features
5. Partner integrations: Ecosystem expansions

Preparing for Build

1. Review current architecture: Know what you have
2. Identify gaps: What problems need solving?
3. Budget planning: New features may mean new costs
4. Skills assessment: Will your team need training?
5. Watch sessions: Plan which talks to attend

Build 2025 promises to be significant for data and AI professionals. Stay tuned for the actual announcements and be ready to experiment with new capabilities.