🚜 Infrastructure for AI Models

Today's Highlights

• Tecton.ai - creating infrastructure for machine learning models
• This Week On BuzzBelow - a recap on this week's topics.
• In Other News - a few interesting developments we're tracking.

Tecton.ai - creating infrastructure for machine learning models

Tecton.ai is a data platform that specializes in infrastructure for machine learning (ML). The platform enables enterprises to design, manage, and deploy machine learning models at scale. It supports ML activities from start to finish, including data preparation, feature engineering, model training, and model serving.

Tecton’s platform has several components that work together in order to provide a comprehensive solution for a variety of machine learning infrastructure. The components include the feature store (centralized repository that manages data for models), data pipeline (scalable processing system that prepares data for models), model registry (version control system for models that tracks changes to models and enables reproducibility), and serving infrastructure (platform for serving and deploying scalable models).

Tecton.ai works with major machine learning technologies like TensorFlow, PyTorch, and scikit-learn, as well as data systems including Apache Kafka, Amazon S3, and Google Cloud Storage. The platform also includes Python, Java, and Scala APIs and SDKs, making it simple to connect with current processes and tools.

One of the most significant advantages of adopting Tecton.ai is that it allows enterprises to speed their ML development lifecycle. Data scientists may use Tecton.ai to focus on model development rather than infrastructure management. The platform automates data preparation, feature engineering, and model training, reducing the time and effort necessary to create and deploy models.

Mike Del Blasio and Kevin Stumpf founded Tecton in 2019 after they met working at Uber. They have raised $160 million to date.

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