Intelligent content that gives practitioners, innovators and leaders an inside look at the present and future of ML & AI technologies.
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Our conversations with hundreds of ML/AI practitioners and teams have demonstrated that effective tools and platforms are the key to delivering ML and AI at scale—allowing teams to innovate more quickly and consistently.
The TWIML Solutions guide helps you identify technologies and solutions that can help your organization deliver models into production more quickly and efficiently.
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For the last decade, as deep learning has become prominent, practitioners have been focused on accumulating as much data as possible, labeling it, preparing it for use, and then iterating on model architectures and hyperparameters in order to achieve our desired performance levels.
Long before starting the TWIML podcast, I worked at the intersection of the two technology shifts that ultimately enabled modern artificial intelligence: cloud computing and big data. AWS was the clear leader in cloud even back then, so I jumped at the opportunity to attend the company’s first re:Invent conference way back in 2012.
Pachyderm provides the ability to modularize, orchestrate, and scale the steps of your ML pipeline within a language-agnostic platform — with the added ability to trace the lineage and versioning of both code and data.
A recent tweet from Soft Linden illustrated the importance of strong responsible AI, governance and testing frameworks for organizations deploying public-facing machine learning applications.
Following a search for “had a seizure now what”, the tweet showed that Google’s “featured snippet” highlighted actions that a University of Utah healthcare site explicitly advised readers NOT to take.
We’re proud to announce the new TWIML Solutions Guide, a directory of machine learning tools and platform technologies for data scientists, ML engineers and other AI practitioners and leaders. The Guide aims to help them explore and compare open source and commercial offerings for building, delivering, and improving their ML and AI projects. This post explains why we think the guide is important and highlights some of its key features.
In order to help enterprise machine learning, data science, and AI innovators understand how model-driven enterprises are successfully scaling machine learning, we have conducted numerous interviews on the topic.
Explore Solutions
Build better models faster by using state-of-the-art hyperparameter optimization and supervised early stopping tools. Focus on adding business value to your data pipeline while Comet automates the rest.
Dataiku Data Science Studio is the collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build, and deliver their own data products more efficiently.
Run:ai Atlas is a compute orchestration platform that speeds up data science initiatives by pooling all available GPU resources and then dynamically allocating resources as you need them. One-click execution of experiments, no code changes required by the user, and most importantly, no more waiting around to access GPUs. Atlas automates provisioning of multiple GPU or fractions of GPU across teams, users, clusters and nodes, and IT gains control and visibility over the full AI infrastructure stack through comprehensive, easy-to-use dashboards.
SigOpt is the only experimentation platform that empowers AI developers to design experiments, explore their modeling problem space, and optimize model selection. By combining seamless experimentation with powerful optimization, SigOpt helps teams like Two Sigma realize 8x faster hyperparameter optimization and OpenAI scale their experiments with sample efficiency to maximize compute utilization.
Introducing the first enterprise-ready feature store for machine learning. Built by the creators of Uber Michelangelo, Tecton provides the first enterprise-ready feature store that manages the complete lifecycle of features — from engineering new features to serving them online for real-time predictions.
Experiment tracking, Datasetset tracking, Dataset visualization
Community
The TWIML Community is a global network of machine learning, deep learning and AI practitioners and enthusiasts.
We organize ongoing educational programs including study groups for several popular ML/AI courses such as Fast.ai Deep Learning, Machine learning and NLP, Stanford CS224N, Deeplearning.ai and more. We also host several special interest groups focused on topics like Swift for Tensorflow, and competing in Kaggle competitions.
Work with Us
TWIML creates and curates intelligent content that helps makers build better experiences for their users, and gives executives an inside look at the real-world application of intelligence technologies. We also build and support communities of innovators who are as excited about these technologies as we are. We advise a variety of leading organizations as well, helping to craft strategies for taking advantage of the vast opportunities created by ML and AI.
