DeepSeek R1 in a Nutshell

DeepSeek R1 is an advanced AI model developed by the Chinese startup DeepSeek AI. It has gained significant attention for the following reasons:

• Open Source, available to for use by anyone.
• Comparable Performance to OpenAI's GPT-4 and ChatGPT o1 models on various benchmarks.
• DeepSeek R1 was reportedly trained on 2,788 GPUs at a cost of around $6 million, significantly less than the estimated $100 million cost to train OpenAI's GPT-4.
• Excels in reasoning tasks and has been trained using large-scale reinforcement learning without supervised fine-tuning.
• Availability on platforms like Azure AI Foundry and GitHub, making it accessible for developers and researchers.
• DeepSeek R1's open-source nature and cost-effective training have made it a notable player in the AI community, challenging the notion that larger models and more data always lead to better performance.

 

To get started, DeepSeek R1 is now available via a serverless endpoint through the model catalog in Azure AI Foundry.

Also, check out the GitHub Models blog post, where you can explore additional resources and step-by-step guides to integrate DeepSeek R1 seamlessly into your applications.

In addition, customers will be able to use distilled flavors of the DeepSeek R1 model to run locally on their Copilot+ PCs, as noted in the Windows Developer blog post.

 

What is Automated Intelligence?

Automated Intelligence refers to the use of technology to automate repetitive, rule-based tasks that typically require minimal human intervention. This includes everything from data entry to workflow management and beyond. The goal of Automated Intelligence is to streamline processes, increase efficiency, and reduce the potential for human error.

 

Artificial Intelligence encompasses a broader scope, including machine learning, natural language processing, and more. AI is designed to simulate human intelligence and can perform complex tasks like understanding language, recognizing patterns, and making decisions based on data. AI systems can learn and adapt over time, improving their performance with more data and experience.

 

Automated Intelligence (AI) and Artificial Intelligence (AI). Although they share the same abbreviation, their applications and implications can differ significantly.

- Scope: Automated Intelligence focuses on automating specific tasks, while Artificial Intelligence aims to replicate human-like intelligence across a wide range of activities.

- Learning Capability: AI systems can learn and evolve, whereas Automated Intelligence typically follows predefined rules and processes without learning capabilities.

- Application: Automated Intelligence is often used for straightforward, repetitive tasks, while Artificial Intelligence tackles more complex and dynamic problems.

 

How Automated Intelligence Can Transform Business Processes:

1. Efficiency Boost: Automating routine tasks frees up employees to focus on higher-value activities, leading to increased productivity.

2. Consistency and Accuracy: By minimizing human intervention, businesses can reduce errors and ensure consistent output quality.

3. Cost Savings: Automation can reduce labor costs and streamline operations, resulting in significant cost savings over time.

4. Scalability: Automated processes can easily be scaled to handle increased workloads without the need for proportional increases in resources.

 

 

"Data Science with .NET and Polyglot Notebooks" By Matt Eland

In the fall of 2024, I had the opportunity to work with Matt Eland and be one of the editors for his book “Data Science with .NET and Polyglot Notebooks: Programmer's guide to data science using ML.NET, OpenAI, and Semantic Kernel”.  Matt is a very intelligent and knowledgeable data science developer and it definitely reflected in his work.  He walks the reader through step-by-step directions to demonstrate key concepts in data science, machine learning, as well as polyglot notebooks.  This was one of the rare books that I could hardly put down.  I urge you to pick-up a copy and upgrade your data science skills.

 

 

 

Recap of "How To Tune A Multi-Terabyte Database For Optimum Performance"

On October 29, 2024 at the GroupBy conference, I was moderator for Jeff Taylor's session "How To Tune A Multi-Terabyte Database For Optimum Performance"

The video is available at https://www.youtube.com/watch?v=9j51bD0DPZE

 

Listed below are some take aways and Q&As from his session:

 

Ideal Latency time:

20ms for IO

10ms for TempDB

 

Crystal Disk Mark is a simple disk benchmark software: https://crystalmark.info/en/software/crystaldiskmark/

 

What is the overhead of running these diagnostics (i.e. diskspd and Crystal Disk)?

No adverse effects during mid-day testing, but don't run it during a busy time.

It's best to test it during both busy and non-busy times

 

Mutlipath: multiple network cards between host, switch and SAN appliance

 

For tempdb storage, what's preferable?

Shared space on a disk pool with a lot of drives or dedicated pool with just 2 drives (raid 1)? all drives of the same type (NVMe). 

"Shared" means with other databases

Run in memory for newer versions

Raid10 will be fastest

keep tempDB separate from other DBs

 

By in memory tempdb, does that mean memory optimized tempdb metadata option? Yes

 

Jumbo Frames are 8192 when enabled, should be used for storage network to avoid issues

to transfer more across the network

 

 

Raid 5 is best for economy/performance combination on both SSD and conventional drives

 

RAID 5 for data? What about that write penalty overhead? Why not RAID 10 ? RAID 10 is best but RAID 5 will sufficiently perform but make sure you have enough memory for operational needs

 

Use New Mbsv3 series VMs from Azure

 

Would you consider local raid0 for tempdb? Yes, you can but RAID 1 so it's redundant so it stays live.

 

nvarchar: N for "National" characters for various foreign languages, 2+ bytes per character

 

Prefer to use INT instead of BigInt

 

Datetime2 (8 bytes) is preferred over Datetime(6-8 bytes)

 

Unicode size on disk is one thing, size when in cache is a worse problem

 

What do you think about using IFI (Instant File Initialization) for log file in 2022?  Recommended

 

Avoid Heap tables.  However, Markus Winand, author or SQL Performance Explained, shows some specialized cases where Heap is better.

See https://medium.com/@nhatcuong/sql-performance-explained-by-markus-winand-some-notes-after-the-first-read-1dde208f2fd7 for more info