Run Logic app if new virtual machine is created
Hello, I'm building logic app that get triggered on resource creation event by connecting it to event grid. my goal is only to run this if new vm is created however logic app get executed on every create success event. I noticed whenever there is deletion or creation on VM the logic app get triggered. Even in the event payload there is no difference between create and delete VM. how to limit the call of logic app only if new VM is created?How to Create an AI Model for Streaming Data
A Practical Guide with Microsoft Fabric, Kafka and MLFlow Intro In today’s digital landscape, the ability to detect and respond to threats in real-time isn’t just a luxury—it’s a necessity. Imagine building a system that can analyze thousands of user interactions per second, identifying potential phishing attempts before they impact your users. While this may sound complex, Microsoft Fabric makes it possible, even with streaming data. Let’s explore how. In this hands-on guide, I’ll walk you through creating an end-to-end AI solution that processes streaming data from Kafka and employs machine learning for real-time threat detection. We’ll leverage Microsoft Fabric’s comprehensive suite of tools to build, train, and deploy an AI model that works seamlessly with streaming data. Why This Matters Before we dive into the technical details, let’s explore the key advantages of this approach: real-time detection, proactive protection, and the ability to adapt to emerging threats. Real-Time Processing: Traditional batch processing isn’t enough in today’s fast-paced threat landscape. We need immediate insights. Scalability: With Microsoft Fabric’s distributed computing capabilities, our solution can handle enterprise-scale data volumes. Integration: By combining streaming data processing with AI, we create a system that’s both intelligent and responsive. What We’ll Build I’ve created a practical demonstration that showcases how to: Ingest streaming data from Kafka using Microsoft Fabric’s Eventhouse Clean and prepare data in real-time using PySpark Train and evaluate an AI model for phishing detection Deploy the model for real-time predictions Store and analyze results for continuous improvement The best part? Everything stays within the Microsoft Fabric ecosystem, making deployment and maintenance straightforward. Azure Event Hub Start by creating an Event Hub namespace and a new Event Hub. Azure Event Hubs have Kafka endpoints ready to start receiving Streaming Data. Create a new Shared Access Signature and utilize the Python i have created. You may adopt the Constructor to your own idea. import uuid import random import time from confluent_kafka import Producer # Kafka configuration for Azure Event Hub config = { 'bootstrap.servers': 'streamiot-dev1.servicebus.windows.net:9093', 'sasl.mechanisms': 'PLAIN', 'security.protocol': 'SASL_SSL', 'sasl.username': '$ConnectionString', 'sasl.password': 'Endpoint=sb://<replacewithyourendpoint>.servicebus.windows.net/;SharedAccessKeyName=RootManageSharedAccessKey;SharedAccessKey=xxxxxxx', } # Create a Kafka producer producer = Producer(config) # Shadow traffic generation def generate_shadow_payload(): """Generates a shadow traffic payload.""" subscriber_id = str(uuid.uuid4()) # Weighted choice for subscriberData if random.choices([True, False], weights=[5, 1])[0]: subscriber_data = f"{random.choice(['John', 'Mark', 'Alex', 'Gordon', 'Silia' 'Jane', 'Alice', 'Bob'])} {random.choice(['Doe', 'White', 'Blue', 'Green', 'Beck', 'Rogers', 'Fergs', 'Coolio', 'Hanks', 'Oliver', 'Smith', 'Brown'])}" else: subscriber_data = f"https://{random.choice(['example.com', 'examplez.com', 'testz.com', 'samplez.com', 'testsite.com', 'mysite.org'])}" return { "subscriberId": subscriber_id, "subscriberData": subscriber_data, } # Delivery report callback def delivery_report(err, msg): """Callback for delivery reports.""" if err is not None: print(f"Message delivery failed: {err}") else: print(f"Message delivered to {msg.topic()} [partition {msg.partition()}] at offset {msg.offset()}") # Topic configuration topic = 'streamio-events1' # Simulate shadow traffic generation and sending to Kafka try: print("Starting shadow traffic simulation. Press Ctrl+C to stop.") while True: # Generate payload payload = generate_shadow_payload() # Send payload to Kafka producer.produce( topic=topic, key=str(payload["subscriberId"]), value=str(payload), callback=delivery_report ) # Throttle messages (1500ms) producer.flush() # Ensure messages are sent before throttling time.sleep(1.5) except KeyboardInterrupt: print("\nSimulation stopped.") finally: producer.flush() You can run this from your Workstation, an Azure Function or whatever fits your case. Architecture Deep Dive: The Three-Layer Approach When building AI-powered streaming solutions, thinking in layers helps manage complexity. Let’s break down our architecture into three distinct layers: Bronze Layer: Raw Streaming Data Ingestion At the foundation of our solution lies the raw data ingestion layer. Here’s where our streaming story begins: A web service generates JSON payloads containing subscriber data These events flow through Kafka endpoints Data arrives as structured JSON with key fields like subscriberId, subscriberData, and timestamps Microsoft Fabric’s Eventstream captures this raw streaming data, providing a reliable foundation for our ML pipeline and stores the payloads in Eventhouse Silver Layer: The Intelligence Hub This is where the magic happens. Our silver layer transforms raw data into actionable insights: The EventHouse KQL database stores and manages our streaming data Our ML model, trained using PySpark’s RandomForest classifier, processes the data SynapseML’s Predict API enables seamless model deployment A dedicated pipeline applies our ML model to detect potential phishing attempts Results are stored in Lakehouse Delta Tables for immediate access Gold Layer: Business Value Delivery The final layer focuses on making our insights accessible and actionable: Lakehouse tables store cleaned, processed data Semantic models transform our predictions into business-friendly formats Power BI dashboards provide real-time visibility into phishing detection Real-time dashboards enable immediate response to potential threats The Power of Real-Time ML for Streaming Data What makes this architecture particularly powerful is its ability to: Process data in real-time as it streams in Apply sophisticated ML models without batch processing delays Provide immediate visibility into potential threats Scale automatically as data volumes grow Implementing the Machine Learning Pipeline Let’s dive into how we built and deployed our phishing detection model using Microsoft Fabric’s ML capabilities. What makes this implementation particularly interesting is how it combines traditional ML with streaming data processing. Building the ML Foundation First, let’s look at how we structured the training phase of our machine learning pipeline using PySpark: Training Notebook Connect to Eventhouse Load the data from pyspark.sql import SparkSession # Initialize Spark session (already set up in Fabric Notebooks) spark = SparkSession.builder.getOrCreate() # Define connection details kustoQuery = """ SampleData | project subscriberId, subscriberData, ingestion_time() """ # Replace with your desired KQL query kustoUri = "https://<eventhousedbUri>.z9.kusto.fabric.microsoft.com" # Replace with your Kusto cluster URI database = "Eventhouse" # Replace with your Kusto database name # Fetch the access token for authentication accessToken = mssparkutils.credentials.getToken(kustoUri) # Read data from Kusto using Spark df = spark.read \ .format("com.microsoft.kusto.spark.synapse.datasource") \ .option("accessToken", accessToken) \ .option("kustoCluster", kustoUri) \ .option("kustoDatabase", database) \ .option("kustoQuery", kustoQuery) \ .load() # Show the loaded data print("Loaded data:") df.show() Separate and flag Phishing payload Load it with Spark from pyspark.sql.functions import col, expr, when, udf from urllib.parse import urlparse # Define a UDF (User Defined Function) to extract the domain def extract_domain(url): if url.startswith('http'): return urlparse(url).netloc return None # Register the UDF with Spark extract_domain_udf = udf(extract_domain) # Feature engineering with Spark df = df.withColumn("is_url", col("subscriberData").startswith("http")) \ .withColumn("domain", extract_domain_udf(col("subscriberData"))) \ .withColumn("is_phishing", col("is_url")) # Show the transformed data df.show() Use Spark ML Lib to Train the model Evaluate the Model from pyspark.sql.functions import col from pyspark.ml.feature import Tokenizer, HashingTF, IDF from pyspark.ml.classification import RandomForestClassifier from pyspark.ml import Pipeline from pyspark.ml.evaluation import MulticlassClassificationEvaluator # Ensure the label column is of type double df = df.withColumn("is_phishing", col("is_phishing").cast("double")) # Tokenizer to break text into words tokenizer = Tokenizer(inputCol="subscriberData", outputCol="words") # Convert words to raw features using hashing hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=100) # Compute the term frequency-inverse document frequency (TF-IDF) idf = IDF(inputCol="rawFeatures", outputCol="features") # Random Forest Classifier rf = RandomForestClassifier(labelCol="is_phishing", featuresCol="features", numTrees=10) # Build the ML pipeline pipeline = Pipeline(stages=[tokenizer, hashingTF, idf, rf]) # Split the dataset into training and testing sets train_data, test_data = df.randomSplit([0.7, 0.3], seed=42) # Train the model model = pipeline.fit(train_data) # Make predictions on the test data predictions = model.transform(test_data) # Evaluate the model's accuracy evaluator = MulticlassClassificationEvaluator( labelCol="is_phishing", predictionCol="prediction", metricName="accuracy" ) accuracy = evaluator.evaluate(predictions) # Output the accuracy print(f"Model Accuracy: {accuracy}") Add Signature to AI Model from mlflow.models.signature import infer_signature from pyspark.sql import Row # Select a sample for inferring signature sample_data = train_data.limit(10).toPandas() # Create a Pandas DataFrame for schema inference input_sample = sample_data[["subscriberData"]] # Input column(s) output_sample = model.transform(train_data.limit(10)).select("prediction").toPandas() # Infer the signature signature = infer_signature(input_sample, output_sample) Run – Publish Model and Log Metric: Accuracy import mlflow from mlflow import spark # Start an MLflow run with mlflow.start_run() as run: # Log the Spark MLlib model with the signature mlflow.spark.log_model( spark_model=model, artifact_path="phishing_detector", registered_model_name="PhishingDetector", signature=signature # Add the inferred signature ) # Log metrics like accuracy mlflow.log_metric("accuracy", accuracy) print(f"Model logged successfully under run ID: {run.info.run_id}") Results and Impact Our implementation achieved: 81.8% accuracy in phishing detection Sub-second prediction times for streaming data Scalable processing of thousands of events per second Yes, let me continue the blog post by explaining the deployment and operation phase of our ML solution: From Model to Production: Automating the ML Pipeline After training our model, the next crucial step is operationalizing it for real-time use. We’ve implemented one Pipeline with two activities that process our streaming data every 5 minutes: All Streaming Data Notebook # Main prediction snippet from synapse.ml.predict import MLFlowTransformer # Apply ML model for phishing detection model = MLFlowTransformer( inputCols=["subscriberData"], outputCol="predictions", modelName="PhishingDetector", modelVersion=3 ) # Transform and save all predictions df_with_predictions = model.transform(df) df_with_predictions.write.format('delta').mode("append").save("Tables/phishing_predictions") Clean Streaming Data Notebook # Filter for non-phishing data only non_phishing_df = df_with_predictions.filter(col("predictions") == 0) # Save clean data for business analysis non_phishing_df.write.format("delta").mode("append").save("Tables/clean_data") Creating Business Value What makes this architecture particularly powerful is the seamless transition from ML predictions to business insights: Delta Lake Integration: All predictions are stored in Delta format, ensuring ACID compliance Enables time travel and data versioning Perfect for creating semantic models Real-Time Processing: 5-minute refresh cycle ensures near real-time threat detection Automatic segregation of clean vs. suspicious data Immediate visibility into potential threats Business Intelligence Ready: Delta tables are directly compatible with semantic modeling Power BI can connect to these tables for live reporting Enables both historical analysis and real-time monitoring The Power of Semantic Models With our data now organized in Delta tables, we’re ready for: Creating dimensional models for better analysis Building real-time dashboards Generating automated reports Setting up alerts for security teams Real-Time Visualization Capabilities While Microsoft Fabric offers extensive visualization capabilities through Power BI, it’s worth highlighting one particularly powerful feature: direct KQL querying for real-time monitoring. Here’s a glimpse of how simple yet powerful this can be: SampleData | where EventProcessedUtcTime > ago(1m) // Fetch rows processed in the last 1 minute | project subscriberId, subscriberData, EventProcessedUtcTime This simple KQL query, when integrated into a dashboard, provides near real-time visibility into your streaming data with sub-minute latency. The visualization possibilities are extensive, but that’s a topic for another day. Conclusion: Bringing It All Together What we’ve built here is more than just a machine learning model – it’s a complete, production-ready system that: Ingests and processes streaming data in real-time Applies sophisticated ML algorithms for threat detection Automatically segregates clean from suspicious data Provides immediate visibility into potential threats The real power of Microsoft Fabric lies in how it seamlessly integrates these different components. From data ingestion through Eventhouse ad Lakehouse, to ML model training and deployment, to real-time monitoring – everything works together in a unified platform. What’s Next? While we’ve focused on phishing detection, this architecture can be adapted for various use cases: Fraud detection in financial transactions Quality control in manufacturing Customer behavior analysis Anomaly detection in IoT devices The possibilities are endless with our imagination and creativity! Stay tuned for the Git Repo where all the code will be shared ! References Get Started with Microsoft Fabric Delta Lake in Fabric Overview of Eventhouse CloudBlogger: A guide to innovative Apps with MS FabricAnnouncing the o1 model in Azure OpenAI Service, plus cool new fine-tuning advancements
The o1 model is coming soon to the Microsoft OpenAI Service. This model brings advanced capabilities and improvements that will enable developers to apply reasoning capabilities to tasks such as inventory management, customer support inquiries, financial analysis and more. Announcing the o1 model in Azure OpenAI Service Also, as we continue to push the boundaries of AI capabilities, we are thrilled to announce several new fine-tuning features in Azure OpenAI Service. Learn more about o-1 mini-reinforcement fine tuning (optimize model behavior in highly complex or dynamic environments), direct preference optimization (adjust model weights based on human preferences), prompt caching (reduce request latency and costs by reusing recently seen input tokens) and more! Introducing New Fine-tuning Techniques and Capabilities in Azure OpenAI Service
Azure Build and Modernize AI Apps infographic protected
Hello! I am reaching out because I am trying to use an Infographic from Build and Modernize AI Apps Azure Resources. In specific a PMCMarketing Campaign. The infographic is not working as expected, it is protected by Information Rights Management and I cannot request access either. Cree y modernice aplicaciones de IA Hope you can bring some light into this! Thanks!
Differences in the payment schedules
When selling through the Azure Marketplace there are various payout schedules. In the reports i see different types of 'Sales Channels'. There is: Enterprise Go to Market Pay as you Go But in the documentation (link) there are other terms that are being used EA MCA CSP Can anyone else to explain in detail when we can expect to get the money for each type of Sales Channel? Thank you.
Test Setup for Logic Apps AS2 Msg Exchange with Encryption
Recently I had a scenario where I needed to test AS2 Msg Send/receive with encryption & Decryption. I realized there a lots of good guidance samples are available but they were in lots of different places and had some dependencies. I am documenting the steps I went through to make the sample work. Step 1: To start this is great sample to try the whole end to end scenario and very easy to deploy. Only thing missing is Encryption and decryption. https://github.com/Azure/azure-quickstart-templates/tree/master/quickstarts/microsoft.logic/logic-app-as2-send-receive Once you have deployed the solution which has all the Integration Accounts, Partners, Agreements and Logic Apps to Send and receive the messages, test out the solution. Now you need your certificates to Encrypt and decrypt your messages. You can use Public Certificates purchased from Certificate Authorities and they don't require any Keys If you plan to use self-signed certificates which was my case, you will need Private Key in Azure Key Vault. Step 2: Create Azure Key Vault Note: Make sure you give appropriate access to "Azure Logic Apps" to Key Vault using Access Policies or now preferred RBAC approach. Step 3: Create Self Signed Certificate (for testing purposes only) If you already have the public Certificate and Private Key, you can upload the private Key in the Key Vault. Step 4: Go to the Contoso Integration Account (receiving party) and add the Private Key to the integration account You might encounter an error or wont see any Keys when you select the KeyVault. This happens if you have not given access to the Azure Logic Apps to Key Vault. Click on Add to add the Private Key Step 5: Go to Agreements and click on edit button to edit the Contoso-FabrikamSales agreement. Click on Receive Settings and select Enable Message Encryption. Select the Private Key added in earlier step in the drop down. Step 6: Make sure Contoso AS2Receive Logic Apps has the decrypt Step/Action Step 7: Go to the Fabrikam Integration Account (sending party) and add the public certificate to the integration account. Step 8: Go to Agreements and click on edit button to edit the FabrikamSales-Contoso agreement. Click on Send Settings and select Enable Message Encryption. Select the Public Certificate added in earlier step in the drop down. Step 9: Go to your Send Logic App and Configure the Encode Step Action. Make sure you populate the AS2 From and AS2 To Parameter with correct Values. Step 10: Go to Http Action add below parameters For Header Body('Encode_to_AS2_message')?['AS2Message']?['OutboundHeaders']" For Body @base64ToBinary(body('Encode_to_AS2_message')?['AS2Message']?['Content']) Make Sure You have correct URI to the Receiving Logic App. Step 11: Last step is to test the flow. Go to FabrikamSales-AS2Send logic app and run it manually. Validate if it successfully executed. Go to Contoso-AS2Receive and confirm if it also ran successfully. GO to the Run History identifier click on it and check the Decode AS2 Msg action Output and check dispositionType is not giving any errors. References: Add certificates to secure B2B messages in workflows - Azure Logic Apps | Microsoft Learn Exchange AS2 messages in B2B workflows - Azure Logic Apps | Microsoft Learn Azure Logic Apps - AS2 Send Receive - Code Samples | Microsoft Learn Solutions for common errors and problems in B2B scenarios - Azure Logic Apps | Microsoft Learn
