A Fine Slice Of SQL Server
I continue a series on Azure ML:
When we use the designer to train and deploy models in Azure ML, we’re actually creating pipelines and we can see the pipeline itself in this visual designer.
But we don’t need the visual designer to create pipelines—we can do so in code as well.
Read on to see ho.
Comments closedI continue a series on Azure ML:
Before we dive into this post, I ask you to read a prior post I wrote about MLflow. That post lays out four key products in MLflow and how they all work together to make model management possible.
Click through to learn more about how Azure ML handles model tracking. That can involve MLflow but does not require it.
Comments closedI continue a series on getting beyond the basics with Azure ML. First up, we get up close and personal in development:
Notebooks are great for ad hoc work or simple data analysis but we will want more robust tools if we wish to perform proper code development, testing, and deployment. This is where Visual Studio Code comes into play, particularly the Azure Machine Learning extension.
Then, I get into the Python SDK:
Over the past two posts, we have started using the Azure Machine Learning SDK for Python but I’ve only touched on the topic. In this post, we are going to dive into the topic.
Read on for more info on each.
Comments closedSanil Mhatre performs a bit of sentiment analysis:
Previous articles in this series have focused on platforms like Azure Cognitive Services and Oracle Text features to perform the core tasks of Natural Language Processing (NLP) and Sentiment Analysis. These easy-to-use platforms allow users to quickly analyze their text data with easy-to-use pre-built models. Potential drawbacks of this approach include lack of flexibility to customize models, data locality & security concerns, subscription fees, and service availability of the cloud platform. Programming languages like Python and R are convenient when you need to build your models for Natural Language Processing and keep your code as well as data contained within your data centers. This article explains how to do sentiment analysis using Python.
Python is a versatile and modern general-purpose programming language that is powerful, fast, and easy to learn. Python runs on interpreters, making it compatible with multiple platforms, and is widely used in applications for web platforms, graphical interfaces, data science, and machine learning. Python is increasingly gaining popularity in data analysis and is one of the most widely used languages for data science. You can learn more about Python from the official Python Software Foundation website.
Click through to see what’s available in the NLP world for Python. The short version is “a lot.”
Comments closedJames Nguyen makes an announcement:
Following the blog post on Turning AML compute into Ray and Dask , we’ve added a new exciting capability to run Spark within AML compute where Spark shares the same context with your ML code. The Spark version is 3.2.1 with support for Delta Lake and Synapse SQL read/write. This enables users of AML to perform powerful data transformation and even Spark ML within AML interactive notebook or in a job run.
Traditionally, Azure ML integrates with Spark Synapse or external compute services via a pipeline step or better via magic command like %synapse, but the computing context is separate from your AML logic so you still need to run Spark in a separate step and persist the output to some storage and load it in your AML script.
With this approach, Spark is available right within your AML code whether it’s AML notebook, python script or pipeline step. It shares the common computing context and most of the cases you can just directly convert the Spark Dataframe to Pandas and Dask Dataframe without persisting first to an intermediary storage.
I’ll have to try this out to see if it makes up for their getting rid of the Spark-based curated environments last year.
Comments closedMuhammad Asad Iqbal Khan performs derivatives in Python:
Derivatives are one of the most fundamental concepts in calculus. They describe how changes in the variable inputs affect the function outputs. The objective of this article is to provide a high-level introduction to calculating derivatives in PyTorch for those who are new to the framework. PyTorch offers a convenient way to calculate derivatives for user-defined functions.
Read on to see how you can use PyTorch to do this.
Comments closedIn the prior series, Low-Code Machine Learning with Azure ML, we saw how to get started with Azure Machine Learning in a fairly pain-free way, especially for developers getting started with machine learning. In this series, I will assume that you already know all of those details and instead, we’re going to go full-code.
There are a few different ways in which we can go full-code with Azure ML. Today, we’re going to look at the easiest of those methods: using Jupyter notebooks within Azure ML Studio.
Read on for the first post in the series.
Comments closedLouise Han announces an update to the anomaly detection service:
We are excited to announce that we are adding more powerful capabilities in Microsoft Azure Multivariate Anomaly Detector (MVAD) today. In the latest version(v1.1-preview.1) of this API, we implemented a new , in a synchronous manner, which means you could get the anomaly detection results immediately once you call this API. This synchronous inference API is a substantial change compared with previous inference process and will be more intuitive and easier-to-use.
Also, we added a new field named ‘interpretation‘ to give more explanations on an anomaly, like which variables have huge correlation changes and cause the anomaly. These updates will support you to better leverage MVAD and get more useful information to analyze and take actions.
Click through for some more details.
Comments closedCem Ayberkin shows off the Azure Form Recognizer:
Shopping malls are facing strong competition and effective loyalty programs boost customer retention. The primary goal of the loyalty scheme is to promote loyalty at the mall, increase footfall whilst understanding shopping habits. With large number of stores and various receipt formats in a mall, the process of manual checking and verification of the data submitted in place did enable rewards to be issued, but proved slow, expensive, inconsistent, and non-scalable. It did not include the valuable line item/product information the mall needed to understand the shopping habits. Therefore, one of the largest shopping malls used Azure Form Recognizer automating receipt scanning and data extraction and feeding the data as rewards points into the customer’s loyalty program, which greatly improved customer shopping experience.
I was pleasantly surprised with how the Form Recognizer works. It’s not perfect but it is useful.
Comments closedI have a post correcting a statement I made before:
The single biggest problem I have with compute instances is that there is no auto-stop functionality to them. This is really frustrating because you’re paying for that virtual machine like you would any other, so if you forget to turn it off when you go home for the weekend, it’ll cost you. I wish there were a built-in option to shut off a compute instance after a certain amount of inactivity. Instead, you’ll need to start and stop them manually.
It turns out that you can and so I wanted to write a post to correct the record.
Click through to see how you can do this. You can bet that I’ve got it enabled now.
Comments closed