We've been using ChatGPT quite a lot at work. As we used it more and more we found that keeping data within our own environment has become more important. In this post we'll explore how you can use Azure OpenAI as an alternative to OpenAI to keep your chat data private. Take a look!
Read moreWe’ve seen a lot of demonstrations lately with large language models. Everyone is racing to get on board with ChatGPT and other large language models (LLMs). I’ve never seen so many experts pop up in such a short time. But is there a method to the madness? Are we doing the right thing by staying away from the hype or should we go all in? It’s a question I get a lot this week. Here’s my answer.
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Created 5/29/2023 9:03:31 AM
Filed in Machine Learning
While chat applications have received much attention, there is so much more you can achieve by integrating language models into your projects. Imagine automating budgeting, generating engaging tweets, transforming documents, or creating compelling conference abstracts. Semantic Kernel offers a lightweight SDK that simplifies the process, allowing you to build language applications effortlessly. In this post, we'll explore how you can leverage Semantic Kernel to add powerful language skills, from installation to building skills and integrating them into your C# application. Unleash the potential of large language models and take your projects to new heights.
Read moreLet's explore fun prompts for improving your Python code using GPT-4. These prompts include writing docstrings, writing automated tests, checking your code for optimizations and bugs, and refactoring a method. While GPT-4 could be better, it's a significant improvement over doing these tasks by hand.
Read moreMachine learning operations (MLOps) is a rapidly evolving field that is transforming the way businesses approach data science. One of the most critical aspects of MLOps is the ability to create and test experiments quickly and efficiently. While it may be tempting to use your production ML pipeline as a testing ground, it's essential to have a separate sandbox environment for several reasons.
Read moreWe’re starting with a pilot to test Github Copilot at Info Support. This is awesome news for us because we can now learn how much copilot is going to help us with our code. In the past few months, I’ve been testing Github Copilot on my own projects and learned a ton about things like use cases and plugins for various IDEs.
Read moreWe hit a nice roadblock yesterday while working on a dataset with 5 million samples. It took us 8 hours to upload the dataset and 11 hours to download and unpack it. We learned some valuable lessons there that I’ll discuss in this post so you don’t have to run into the same issues.
Read moreOne of the downsides to building machine learning models is that you need quite a bit of power in your machine. I've just upgraded my home desktop that I use for side projects and experiments to 64GB of memory because it was clearly no longer up to the task. However, when I booted up my WSL linux distro, it couldn't get more than 32GB of memory. Time to figure out how to fix that! In this quick post I'm showing you how you can limit or increase the memory available to your WSL2 distributions. Let's go!
Read moreIn the past weeks we've been working on a new computer vision project at Aigency. As part of the project we have to process images using OpenCV image filters. This is a rather tedious process that takes a lot of time, so we made a nice workflow for it. In this blog post I'll show you how we used Prefect and Ray to build a scalable image processing workflow.
Read moreWorking with machine-learning code in Windows can be quite a hassle. Often I run into problems with my tools not working properly on Windows. Recently, I even decided to convert my laptop to Ubuntu 22.04 for the time being. Which is really nice for data sciency stuff, but not great for my Powerpoint and Excel since LibreOffice sucks big time. Needless to say, I needed another solution. Especially for my deep learning setup. In this quick post I'll show you how I configured my Ubuntu 22.04 installation on Windows 11 to run CUDA and PyTorch.
Read moreI've shown how to use DVC to manage data for your machine-learning project on my blog before. Today it's time to take things to the next level. Let's build a pipeline to train a machine-learning model with DVC.
Read moreDVC is a data versioning tool that is designed to make it easier to work with data for machine-learning projects. It integrates with git so that you can easily keep track of your data files and track changes to them over time. It also allows you to easily share data files between different projects and collaborators. In this post I'll show you how you can start using DVC in your ML project.
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