Experiment tracking has been one of the most popular topics in the context of machine learning projects. It is difficult to imagine a new project being developed without tracking each experiment's run history, parameters, and metrics. While some projects may use more "primitive" solutions like storing all the experiment metadata in spreadsheets, it is definitely not a good practice. It will become really tedious as soon as the team grows and schedules more and more experiments. Many mature and actively developed tools can help your team track machine learning experiments. In this article, I will introduce and describe some of these tools, including TensorBoard, MLFlow, and Neptune.ai,

PyTorch is an open source machine learning framework, primarily developed by Meta (previously Facebook). PyTorch is extensively used in the research space and in recent years it has gained immense traction in the industry due to its ease of use and deployment. Vertex AI, a fully managed end-to-end data science and machine learning platform on Google Cloud, has first class support for PyTorch making it optimized, compatibility tested and ready to deploy. We started a new blog series - PyTorch on Google Cloud - to uncover, demonstrate and share how to build, train and deploy PyTorch models at scale on Cloud AI Infrastructure using GPUs and TPUs on Vertex AI, and how to create reproducible machine learning pipelines on Google Cloud . This blog post is the home page to the series with links to the existing and upcoming posts for the readers to refer to.

MLOps is the new terminology defining the operational work needed to push machine learning projects from research mode to production. While Software Engineering involves DevOps for operationalizing Software Applications, MLOps encompass the processes and tools to manage end-to-end Machine Learning lifecycle. Machine Learning defines the models' hypothesis learning relationships among independent(input) variables and predicting target(output) variables. Machine Learning projects involve different roles and responsibilities starting from the Data Engineering team collecting, processing, and transforming data, Data Scientists experimenting with algorithms and datasets, and the MLOps team focusing on moving the trained models to production. Machine Learning Lifecycle represents the complete end-to-end lifecycle of machine learning projects from research mode to production.

These steps are often executed manually by running scripts, notebooks, and applying human judgment to validate the model as successfully trained. After model training for production, the process of training an ML model is more likely to be automated with an ML model training pipeline. This way, the model training can be reviewed, reproduced, tuned, and debugged at any point. When triggered, the components in the above pipeline will run in sequential order, and only if they are all successful, a micro-service that serves the trained model will be created or updated. Most of the components in the above pipeline can be reused and have open-source implementations.

How many machine learning platforms run on Kubernetes? Which machine learning platforms can run in air-gapped environments? How common are feature stores in current machine learning platforms? There are many commercial and open-source machine learning platforms on the market today. While Gartner's Magic Quadrants and Forrester's Waves can inform, these views onto the marketplace are not neutral: they are based on vendor demonstrations and customer surveys rather than hands-on software evaluations or in-depth studies of available documentation.

Open-source workflow managers are popular because they make it easy to orchestrate machine learning (ML) jobs for productions. Taking models into productions following a GitOps pattern is best managed by a container-friendly workflow manager, also known as MLOps. Kubeflow Pipelines (KFP) is one of the Kubernetes-based workflow managers used today. However, it doesn't provide all the functionality you need for a best-in-class data science and ML engineer experience. A common issue when developing ML models is having access to the tensor-level metadata of how the job is performing.

Kubernetes is one of the most important pieces of software produced in the last decade and one of the most influential open source projects ever. Kubernetes has completely revolutionized how applications are developed and how infrastructure is deployed and managed. With Kubernetes' explosive rise, more and more physical hardware is being managed by Kubernetes. This trend has coincided with an explosion in the popularity of deep learning, an extremely computation-demanding technology that can result in a single data scientist occupying dozens of GPUs for weeks at a time. Give data scientists access to more hardware.

IBM Corp. said today it's hoping to provide a standardized solution for developers to create and deploy machine learning models in production and make them portable to any cloud platform. To do so, it said it's open-sourcing the Kubeflow machine learning platform on Tekton, a continuous integration/continuous development platform developed by Google LLC. It's popular with developers who use Kubernetes to manage containerized applications, which can run unchanged across many computing environments. IBM said it created Kubeflow Pipelines on Tekton in response to the need for a more reliable solution for deploying, monitoring and governing machine learning models in production on any cloud platform. That's important, IBM says, because hybrid cloud models are rapidly becoming the norm for many enterprises that want to take advantage of the benefits of running their most critical business applications across distributed computing environments.

A Data Scientist spends most of his time inside a Jupyter Notebook exploring the data and drafting ideas. Usually, when we try to version our work, we end up with a bunch of duplicated ipynb files, assuming different naming schemes. Can we have something that automatically snapshots our work, before and after every step in an ML pipeline? Moreover, can we get started using it without a ton of configuration needed? Just open a Notebook, do our thing and be sure that everything else will take care of itself.

Google Cloud is rolling out an "AI Hub" supplying machine learning content ranging from data pipelines and TensorFlow modules. It also announced a new pipeline component for the Google-backed Kubeflow open-source project, the machine learning stack built on Kubernetes that among other things packages machine learning code for reuse. The AI marketplace and the Kubeflow pipeline are intended to accelerate development and deployment of AI applications, Google said Thursday (Nov. The new services follow related AI efforts such as expanding access to updated Tensor processing units (TPUs) on the Google Cloud. The AI Hub is described as a community for accessing "plug-and-play" machine learning content.