An organised codebase enables you to implement changes faster and make less mistakes, ultimately leading to higher code and model quality. Read more to learn how to structure your ML projects with Tensorflow Extended (TFX), the easy and straightforward way. — Project Structure: Requirements Enable experimentation with multiple pipelines Support both alocal execution mode and a deployment execution mode. This ensures the creation of 2 separate running configurations, with the first being used for local development and end-to-end testing and the second one used for running in the cloud.

Quit depending on positional indices and input value ordering. Start relying on named inputs and outputs. Avoiding data wiring errors — Named inputs and outputs are essentially dictionaries with string keys and tensor values. Benefits Defence Against Feature Reordering Self — Sufficient Model Serving Signatures and Metadata Renaming and Absent Feature Protection Most machine learning pipelines read data from a structured source ( database, CSV files/ Pandas Dataframes , TF Records), perform…

Namely: Hyperparameter Search, Convolution Variants, Network-in-Network, Weight Sharing, Pruning, Quantization — The following is a very, very brief and non-technical summary of a chapter in the author’s dissertation. Motives A very deep neural network that is designed to solve hard problems might take a few seconds to run on modern computers with hardware accelerators for linear algebra operations. Similarly, smaller neural networks…

A short guide on how to use image embeddings from OpenAI’s CLIP and clustering techniques in order to group near-duplicate images together. Overview We’re going to use huggingface/transformers to quickly load openai/clip-vit-base-patch32 from the hub We’re going to perform inference in order to extract image features We will use those feature…

Hard-coded string values that linger around your project impact your ability to change negatively, but there’s an easy way around that. But first, what’s wrong with magic strings? We’re going to demonstrate the usage of using static classes to keep track of constants, in 2 scenarios: Declaring routes in a FastAPI app Keeping track of metrics and…

Safely store and handle incoming heterogeneous data in a functional, type-safe, rusty way. — Problem Statement You’ve got to make a web API that accepts JSON objects (or anything that serde supports, actually). You need some code — first way of declaring the structure of those incoming entities, so that they can be processed accordingly and deserialized into easy-to-use structs and traits. Consider the following example:

Extracting labels, windowing multivariate series, multiple TF Record file shards and other useful tips for dealing with sequential data — The tf.data.Dataset API is a very efficient pipeline builder. Time Series Tasks can be a bit tricky to implement properly. In this article, we are going to dive deep into common tasks: Windowing Labelled Data Windowing Unlabelled Data by Looking Ahead Sharding TF Record Files Tips for Efficiency and No…

A creative PoseNet application that runs on your browser and tries to predict if you’re jumping, crouching, or staying still — You all know what this game is about. This is the best service-offline-sorry page in the world. People have made simple bots that time the dino’s jump to beat the game to reinforcement learning agents with CNN state encoders. It’s a game and we’re supposed to have fun. Today, I’ll…

A quick api overview and a self-contained example of fluent-tfx — If this production e2e ML pipelines thing seems new to you, please read the TFX guide first. On the other hand, if you’ve used TFX before, or planning to deploy a machine learning model, you’re in the right place. But Tensorflow Extended is already fully capable to construct e2e pipelines by itself, why bother to use another API ? Verbose and long code definitions. Actual preprocessing and training code can…