MLflow is inspired by existing ML platforms, but it is designed to be open in two senses:
- Open interface: MLflow is designed to work with any ML library, algorithm, deployment tool or language. It’s built around REST APIs and simple data formats (e.g., a model can be viewed as a lambda function) that can be used from a variety of tools, instead of only providing a small set of built-in functionality. This also makes it easy to add MLflow to your existing ML code so you can benefit from it immediately, and to share code using any ML library that others in your organization can run.
- Open source: We’re releasing MLflow as an open source project that users and library developers can extend. In addition, MLflow’s open format makes it very easy to share workflow steps and models across organizations if you wish to open source your code.
Mlflow is still currently in alpha, but we believe that it already offers a useful framework to work with ML code, and we would love to hear your feedback. In this post, we’ll introduce MLflow in detail and explain its components.
Even in alpha, it looks nice.
Here’s how it works. It starts with a data source such as Event Hub, IoT Hub or Azure Blob Storage, and it uses SQL-like query language that allows transformation on the fly. It helps you process operations like filtering, sorting, aggregating and joining the data together to make it more useable—turning data into information.
From there, when you identify the data that you want/need to use, you can then send that data downstream to be sent to a queue for triggering workflows or further processing of the data. You can also send that data to Power BI for real-time visualization. For example, let’s say you’re looking at a data quality stream and you want to pull certain key words out of Twitter to see how they’re used and watch how that’s being done. By connecting to the Twitter API, you can capture that data, stream it, and then report from it with a Power BI report.
Chris also has a video which you can watch.
Azure Data Factory v2 (ADFv2) has some significant improvements over v1, and we now consider ADF as a viable platform for most of our cloud based projects. But things aren’t always as straightforward as they could be. I’m sure this will improve over time, but don’t let that stop you from getting started now.
This post provides a walk through of using the ‘Lookup’ and ‘If Condition’ activities to do some basic conditional logic depending on the results of a database query.
Assumptions: You already have an ADF pipeline created. If you want to hook into SSIS then you’ll also need the SSIS Integration Runtime set up – although this is not relevant just for the if condition.
Read on for an example.
The file that is used to create a new query window has ANSI encoding but when I save the file on the PowerShell script I save it as UTF-8 because the client have comments on the code with unicode characters.
On this process, the unicode characters are replaced by some symbols.
Read on for the solution.
You may (or may not) have a requirement to setup a linked server to Azure SQL Database from a locally installed SQL Server. One reason could be to pull down some reports from an Azure SQL Database to a local file share. Whatever your reason is hopefully you will find this blog post useful because I ran into some complications on the way.
This is what your linked server creation screens in SSMS (SQL Server Management Studio) should look like.
Take advantage of Arun’s hard-earned experience and read his post.
Sometimes emails from SQL Server go missing, especially when you share an inbox with colleagues. On most occasions it doesn’t always matter as the job that generated the email can simply be re ran to produce the email once again, but what about those emails that contain time specific information? we cannot simple just kick off the job again as it may be coded to evaluate right now and not provide us with the information that was sent 2 hours ago.
I was faced with exactly this issue the other day – i needed the information from the email that was sent at that given time so I wrote a bit of code to get the job done then decided that if I tidy this code up I could make it reusable so here is what I come up with:
Check out Adrian’s helpful script.
You have a DBCC CHECKDB script running, something like the following, and it may take several hours to run to confirm if there is any corruption in your SQL Server Database.
DBCC CHECKDB (
Then someone asks you the age old question… When will it be done?
Click through for a quick script and the answer.