Code Conversations

The "Engineering Room" is a monthly series of conversations with people who are influential in the software industry. In this episode Dave Farley, author of "Continuous Delivery", "Modern Software Engineering" and others, talks to Matthew Skelton co-author of one of the most significant software books of the last 10 years - “Team Topologies”, about the ingredients for long-term, viable, sustainabl...

What is a platform team, and how do you build effective platforms? Platforms are often an important part of the strategy to scale software development beyond small single teams. Dividing work up so that common behaviours and services can be shared, rather than every service team implementing their own version, is a big gain, but it can also be a big cost. Coupling between teams is one of the bigge...

Matthew and Manuel's excellent book Team Topologies described the challenges and proposed patterns for organizing teams effectively. We'll explore their ideas, what patterns are working, and how they might look on Flight Level 2 and 3. https://www.youtube.com/watch?v=ckGjOkMTGKo

AI agents are a type of compound AI system that uses a large language model (LLM) to control its logic. Compound AI systems combine models and other components to solve problems, and they are more adaptable and effective than models alone. Here are some key points about AI agents from the sources: Compound AI Systems: These systems combine different components, including models, programmatic eleme...

https://youtu.be/85RfazjDPwA?si=TM2RugT9QEd1UOZj Comprehensive Overview of PyTorch Tools for Scaling AI Models Scaling AI models often involves adding more layers to neural networks to enhance their ability to capture data nuances and execute complex tasks. However, this scaling process demands increased memory and computational power. To address these challenges, PyTorch offers tools like Distrib...

https://youtu.be/fJ40w_2h8kk?si=YILy9Od6YICopdFf PyTorch is an open-source framework that simplifies the process of building, training, and deploying machine learning and deep learning models. The "Py" signifies its integration with the popular Python programming language, making it easily accessible to the vast community of data scientists who favor Python. PyTorch streamlines the entire model de...

Back propagation is an algorithm that modifies the weights and biases of a neural network to reduce error and improve accuracy. The goal of back propagation is to minimize the difference between the network's output and the desired output. This is an iterative process that continues until the network can reliably produce the desired output. Neural networks consist of layers of interconnected neuro...

Zero-shot prompting asks a question without giving the LLM any other information. It can be unreliable because a word might have multiple meanings. For example, if you ask an LLM to "explain the different types of banks" it might tell you about river banks. Few-shot prompting gives the LLM an example or two before asking the question. This gives the LLM more context so it can give you a better ans...

Supervised learning uses labeled data, like a student learning with a teacher. Unsupervised learning uses unlabeled data and is more like self-study. Supervised learning knows the right answers in advance and aims for accuracy. It can classify data, like filtering spam, or predict values, like stock prices. Unsupervised learning finds hidden patterns on its own. Think about grouping similar custom...

Insurance companies can reduce the stress of car accidents for their customers by using AI to streamline the claims process. Using AI, an insurance company can set up an automated claims processing workflow so customers can provide basic information to begin the claims process immediately after an accident. Customers can also use their smartphones to gather information about the other party, docum...

A Restricted Boltzmann Machine (RBM) is a probabilistic graphical model used for unsupervised learning. RBMs help discover hidden structures in data, making them suitable for applications like video recommendation systems. An RBM consists of two layers: Visible Layer: This layer receives the input data. Hidden Layer: This layer represents features or classifications derived from the input data. Ev...

Knowledge graphs  are a powerful tool for organizing and understanding information. They consist of  nodes , which represent entities such as people, places, or things, and  edges , which represent relationships between those entities. Knowledge graphs can be used to answer questions, make predictions, and find connections between seemingly unrelated information. For example, they can help identif...

Random Forest is a machine learning model used for making predictions. It uses a collection of decision trees, each trained on a random subset of data, to enhance prediction accuracy. Decision trees are a simpler type of model that use a series of decisions to classify data. They are susceptible to problems like bias and overfitting. Imagine deciding whether to play golf. A decision tree might con...

Natural language processing (NLP)  is a field of artificial intelligence that enables computers to understand and process human language. This video from IBM Technology explains the basics of NLP by comparing human language to a computer's understanding. The video describes how NLP uses various techniques to translate unstructured text into structured data that computers can understand. It then hi...

Generative Adversarial Networks (GANs) are a type of unsupervised machine learning algorithm where two submodels, a generator and a discriminator, compete against each other. The generator creates fake samples, while the discriminator attempts to distinguish between real samples from a domain and fake samples from the generator. The adversarial nature of GANs lies in this competition. The generato...

The source is a video from IBM Technology on YouTube that explains the concept of convolutional neural networks (CNNs) in an easy-to-understand manner. The video uses a simple house drawing as an example to illustrate how CNNs, through the use of filters, can identify patterns and perform object recognition. It emphasizes that CNNs are a specialized area of deep learning, consisting of multiple in...

The provided text explains the growing prevalence of  foundation models  in artificial intelligence. These models are large-scale neural networks trained on massive datasets, enabling them to perform various tasks through  transfer learning . The text highlights the  NASA Geospatial Model , developed in collaboration with IBM, which uses satellite imagery to predict climate-related events like flo...

The video transcript from IBM Technology describes a five-stage workflow for creating specialized AI models using  foundation models . The workflow begins with  data preparation , where large datasets are curated, categorized, and filtered. Next, the data is used to  train  a foundation model, which is a pre-trained model that can be adapted for various purposes. After training, the model is  vali...

This excerpt from Andrej Karpathy's YouTube video, "[1hr Talk] Intro to Large Language Models," provides a comprehensive overview of large language models (LLMs), delving into their core components, training process, capabilities, and future directions. The video highlights the fundamental concept of LLMs as "zip files" of the internet, where massive amounts of text data are compressed into neural...

A brief introduction to everything you need to know about Large Language Models (LLMs) to go from knowing nothing to having a solid foundation of understanding to take your learning to the next level. Special thank you to the students at AI Camp who helped craft this video! Matthew Berman https://www.youtube.com/watch?v=osKyvYJ3PRM

Learn about Large Language Models (LLMs), a powerful neural network that enables computers to process and generate language better than ever before. Dale and Nikita share how LLMs work and how you can interact with them via prompts. by Google for Developers https://www.youtube.com/watch?v=iR2O2GPbB0E

Large Language Models (LLMs) and Generative AI intersect and they are both part of deep learning. Watch this video to learn about LLMs, including use cases, Prompt Tuning, and GenAI development tools. By Google Cloud Tech https://www.youtube.com/watch?v=zizonToFXDs

This is an excerpt from a talk at the GOTO 2024 conference about how event-driven architectures can go wrong. The speaker, Matthew Meckes, first establishes the goals of event-driven architectures, which include scalability, agility, and fast feedback loops. Then, he introduces a series of anti-patterns, or common mistakes, that developers often encounter when building these architectures. These p...

Martin Fowler, a renowned software developer, discusses the various interpretations of event-driven architecture. He identifies four common patterns that often fall under this umbrella:  event notification ,  event carried state transfer ,  event sourcing , and  command query responsibility segregation (CQRS) . Fowler delves into the benefits and drawbacks of each pattern, emphasizing the importan...

Event-Driven Architecture has a lot of benefits but comes at a cost of another set of problems. Visibility into workflows and business processes, consistency, idempotency, and consumer lag to name a few.