Machine learning orchestration is not a mature discipline yet. Building ML pipelines per se is a complex process, which is why we developed new ML add-ons to take the load off your shoulders: • Flyte Decks 📊 is Flyte’s way of visualizing data, such as outputs and intermediary data. • The Flyte PyTorch types enables automatic conversion of tensors from GPU ➡️ CPU. • Bridge the gap between developing and deploying ML models with Flyte ONNX types for ScikitLearn, PyTorch, and TensorFlow. • Spark ✨ Pipelines can now be passed seamlessly among the Flyte tasks. • Data and model monitoring are important phases of ML pipelines. They help gauge how efficient your data and ML models are. Use WhyLab’s whylogs 🪵 from within Flyte to monitor your data and models. More details in @Samhita Alla's latest blog How We Are Simplifying the Orchestration of Machine Learning Pipelines.