Machine-Learning-and-Ai

What many people who follow me don’t know, is that I spent a lot of years playing baseball. Early on I played third base, later I pitched, and my last position was usually catcher or first base.

I love the game. What is less known is that I also spent many years umpiring baseball, through to state level. I umpired in the Pan-Pacific games in Queensland.

Umpiring requires a very detailed knowledge of the rules of the game. Umpires must pass annual rules examinations. If you want to umpire at higher levels of competition, you must pass the exams at a higher level.

2024-08-02

Joe Sack is an old friend. (I’ve known him a long time. He’s not actually old). He’s always fascinating to chat to and so I was so pleased to have him on another SQL Down Under podcast today. Last time was in 2017.

Joe is a Senior Product Manager at Microsoft and is working with the integration of data and AI with SQL Copilots. The aim is to use AI and natural language processing to make database management simpler, whether you’ve been doing it for years or just getting started. The experiences he discusses in the show include Azure Copilot integration and Natural language to SQL.

2024-04-27

It’s been a big week for Down Under podcasts. I really enjoyed recording another new Cosmos Down Under podcast this morning. It’s now edited and released.

Show 11 features Azure Cosmos DB product manager Khelan Modi discussing the vector database and search features of Azure Cosmos DB, and particularly how that applies to large language models (LLMs).

Khelan is a product manager on the Azure Cosmos DB team. He leads the AI and Portal (UI) initiatives for the Vector Database service, Azure Cosmos DB for MongoDB vCore.

2024-03-09

One of my more surprisingly popular blog posts in the past talked about Machine Learning: Testing your installation of R and Python in SQL Server 2017. The problem is that SQL Server 2022 changed things.

Now the SQL Server team has published an article on how to make it work.  What I’ve been finding though, is that people are struggling to follow that article. And more concerning, people using named instances of SQL Server (like SQLEXPRESS) couldn’t get it to work at all, no matter how much they tried.

2024-03-04

DevOps has quickly become a core part of how many organizations deliver IT, and in particular, how they deliver applications. But just as quickly as it has become popular, a whole series of XXXOps names have appeared. One of the latest is AIOps. So is it just yet another almost meaningless acronym?

Well as Betteridges Law of Headlines suggests, the answer is no.

When I first saw the term, I was presuming this would be about how to deploy AI based systems, and I wondered why on earth that would need a special name. But that’s not what it is.

2019-05-03

I regularly hear the terms AI and Machine Learning used almost interchangeably, along with a variety of other related terms. I thought it would be useful to add a post that defines some of the common terms and how they differ:

Artificial intelligence (AI) is a fairly generic term. It relates to all intelligent agents that are able to be aware of their environments (in some way), and to take actions where the aim is to achieve a specified goal. Sometimes these goals are terminal ie: they reach a final desired state. Other times, these goals are continuous ie: keep speed at a desired value. It is considered “artificial” intelligence as to an observer, it mimics cognitive functions that humans would imagine other humans performing.

2018-10-19

I’ve flown a lot over the years. What continues to strike me though, is how poorly airlines use machine learning and AI, even when they are in strong competitive environments. A key indicator is detecting and avoiding customer churn. Let me give you an example:

We flew with QANTAS and with their partners in One World for many years. We were both platinum and I’d been platinum for many years. At a recent peak a few years ago, we were flying once or twice a week. That’s not a crazy amount, but it’s enough. And it’s certainly enough to be able to see a purchasing pattern.

2018-10-12

I’ve been spending a lot of time over the last few years working through data science and AI topics. One thing that’s struck me consistently is the total lack of reasonable coding standards in almost all the sample code that I see.

I was doing an AI lab in eDX recently, and one of the questions got me to open some sample Python code for a virtual environment, and asked me to work out how the virtual world that it created operated.

2018-10-02

In a recent post, I discussed issues I found when testing the Machine Learning Services setup in SQL Server 2017.

After that, my old friend Niels Berglund also posted about issues he found after installing CU7 (cumulative update 7) and how he solved them. Niels’ article is here: http://www.nielsberglund.com/2018/06/01/sql-server-machine-learning-services-and-sql-server-2017-cu-7/

What each of those articles discussed though was detail on how temporary files are used by Machine Learning Services in SQL Server to hold R or Python data for sessions. By default, SQL Server configures itself to hold data for up to 20 concurrent sessions.

2018-06-08

In a post the other day, I described how to test if machine learning with R and/or Python was set up correctly within SQL Server 2017.

One of the comments on that post, said that the info was useful but they were still to be convinced why you’d want to have machine learning in the database in the first place.

Fair question.

I see several reasons for this. SQL Server Machine Learning Services is the result of embedding a predictive analytics and data science engine within SQL Server. Consider what happens in most data science groups today, where this type of approach isn’t used.

2018-05-25