A Fine Slice Of SQL Server
Josephine Bush has an after-action report:
I did a couple of posts previously on dumping/restoring Azure PostgreSQL databases and also using the Azure migration tool. I had to ultimately do a combo of those because of the limitations of the Azure migration tool.
Read on for the process that Josephine went through to complete migration.
Leave a CommentMika Sutinen rounds up the usual suspects:
AWS, Azure and GCP all have a fully managed services for SQL Server databases. In this post, I’ll provide a brief overview of the offerings from these hyperscalers. While the main promise of the service remains the same across the hyperscalers, the capabilities, scale, and occasionally, the best use scenarios for each, differ.
Read on for a quick comparison of four offerings from the three cloud providers.
Leave a CommentAndy Brownsword has the right price in mind:
Its the time of year where things may winding down for the new year and we can get a bit of breathing room. With that free time you might want to try something new, let’s say some SQL Server in the cloud?
It could be a good time to start brushing up on new skills, seeing what the services have to offer, or maybe you want to start blogging!
You’re not going to be able to do a tremendous amount at these tiers, but it’s hard to beat that price.
Leave a CommentJon Voege has a collection of links for us:
This week, to round off the year, we try something different. I wanted to throw a shout out to all the community heroes out there, who also help make the most of Microsoft Fabric, through the use of Microsoft Power Platform (and vice versa).
Also, I wanted to highlight some of their contributions, and hopefully give you all a list of resources to peruse.
Click through for more than 20 links, showing how you can work with Power Automate, Power Apps, Power Pages, and data in Dataverse from Microsoft Fabric.
Comments closedTomaz Kastrun wraps up a series on Azure AI. First up is tracing in Azure AI Foundry:
Tracing is a powerful tool that offers developers an in-depth understanding of the execution process of their generative AI applications. Though still in preview (in the time of writing this post), It provides a detailed view of the execution flow of the application and the essential information for debugging or optimisations.
After that, we can see how to evaluate model results:
With evaluation you performing iterative, systematic evaluations with the right evaluators and measure and address potential response quality, safety, or security concerns throughout the AI development lifecycle, from initial model selection through post-production monitoring.
With the Evaluation in Azure AI Foundry, you can evaluation the GenAI Ops Lifecycle production. In addition, it also gives you the ability to assess the frequency and severity of content risks or undesirable behavior in AI responses.
Finally, Tomaz wraps up the series with some notes on documentation:
Documentation and material for Azure AI Foundry are plentiful and growing on a daily basis, since the topic on AI and GenAI is evermore so popular.
I appreciate the challenge that Tomaz has of putting together 25 blog posts in a month, especially when they’re all tied to a single theme.
Comments closedKevin Sookocheff noodles on a core concept:
At AWS re:Invent 2023, Amazon CTO Werner Vogels delivered a talk on the laws of frugal architecture. While I initially filed away those insights to review later, a year of cloud architecture experience crystallized a fundamental truth: in cloud computing, cost isn’t just a financial consideration — it is a first-class architectural concern through which we should design and optimize our systems.
Cloud providers charge for every conceivable resource: servers, API calls, data transfer, and computational milliseconds. But cost is more than just a line item on a monthly bill, it is a powerful forcing function that drives better architectural decisions.
Read on for more thoughts on the matter.
Comments closedTomaz Kastrun continues a series on Azure AI:
Models from the model catalog can be deployed using programming languages or using the Foundry studio.
Model deployment has two types: Deploy from the base model or deploy from the fine-tuned model. The difference is that fine-tuned model is model taken from the model catalog and later tuned to an additional dataset, as the base model is the model as it is available in Azure AI Foundry.
Click through for a bit more information on the process.
Comments closedTomaz Kastrun continues a series on Azure AI:
The management center is a part of the Azure AI Foundry portal. It gives you the overview on activities and governance. From management center you can manage Azure AI Foundry hubs, projects, resources, access, endpoints, models and other resources and general settings.
Click through for an overview of the management center and what it includes.
Comments closedPaul Hernandez describes the current state of production-ready vector search options in Azure:
Vector databases and vector search are becoming increasingly important in modern applications due to their ability to handle complex and high-dimensional data efficiently. In today’s data-driven world, applications such as recommendation systems, image and video retrieval, natural language processing, and anomaly detection rely heavily on the ability to search and analyze large volumes of data quickly and accurately. Vector databases store data in the form of vectors, which allows for more sophisticated and nuanced searches compared to traditional databases. Vector search techniques enable these applications to find similar items, detect patterns, and make predictions by comparing the distances between vectors. This capability is crucial for delivering personalized user experiences, improving search accuracy, and enhancing overall application performance. As a result, vector databases and vector search are essential components in the toolkit of modern data scientists and engineers.
In this article, we will discuss several Azure services that support vector search, including Azure Database for PostgreSQL Flexible Server, Azure Cosmos DB, and Azure Cognitive Search. Each of these services offers unique features and capabilities that make them suitable for implementing vector search in various applications.
Click through for details, as well as links to more resources. Paul didn’t include Azure SQL Database’s vector capabilities, though that’s in preview right now and I’m not actually sure how well it will perform compared to these other options.
2 CommentsTomaz Kastrun has a pair of posts. First up, an overview of Azure OpenAI:
Let’s first address the elephant in the room. We have explored the Azure AI Foundry and the we have also Azure OpenAI. So what is the core difference? Let’s take a look:
The services in the back:
- Azure AI Services has much broader AI capabilities and simpler integration into applications and usage of the real world. With mostly pre-build API for all services (face recognition, document recognition, speech recognition, computer vision, image recognition, and more) that will allow better interoperabilty and and connection to machine learning services (Azure Machine Learning Service).
- Azure OpenAI is focusing primarly on OpenAI LLM models (Azure AI services supports many others) and provides great agents for conversations, content tools, RAG and natural language services.
After that comes an overview of the Azure AI Hub and AI projects:
Comments closedIn AI Foundry portal, hubs provide the environment for a team to collaborate and organize work, and help you as a team lead or IT admin centrally set up security settings and govern usage and spend. You can create and manage a hub from the Azure portal or from the AI Foundry portal, and then your developers can create projects from the hub.
In essence, Hubs are the primary top-level Azure resource for AI Foundry. Their purpose is to to govern security, connectivity, and computing resources across playgrounds and projects.