Martin Heller

12 capabilities every cloud machine learning platform should provide to support the complete machine learning lifecycle—and which cloud machine learning platforms provide them.

Come for the fast editing. Stay for the debugging, source code management support, and huge ecosystem of extensions.

PyTorch 1.10 is production ready, with a rich ecosystem of tools and libraries for deep learning, computer vision, natural language processing, and more. Here’s how to get started with PyTorch.

WebStorm, Visual Studio 2017, Visual Studio Code, NetBeans, Komodo, and Eclipse pull out the stops for JavaScript, Node.js, and friends.

Behind the pay-as-you-go pricing model, the public cloud is teeming with the latest and greatest development, devops, and AI tools for building better and smarter applications faster.

Amazon Textract, Azure Form Recognizer, and Google Document AI can parse your unstructured documents and produce structured information for all kinds of digital transformation use cases.

Vector similarity search uses machine learning to translate the similarity of text, images, or audio into a vector space, making search faster, more accurate, and more scalable.

Enterprise data warehouses are comprehensive structured data stores designed for analysis. They often serve as the data sources for BI systems and machine learning.

Transactional cloud databases come in all shapes and sizes, from simple key-value stores to planet-scale distributed relational databases. Here’s how to choose the right cloud database for your application.

Data rarely comes in usable form. Data wrangling and exploratory data analysis are the difference between a good data science model and garbage in, garbage out.

CockroachDB 19.2.2 impresses with lower latency, higher throughput, greater scalability, and new fully managed CockroachCloud service

Using a machine learning model’s own predictions on unlabeled data to add to the labeled data set sometimes improves accuracy, but not always

If you actually need a deep learning model, PyTorch and TensorFlow are the two leading options

Deep neural networks can solve the most challenging problems, but require abundant computing power and massive amounts of data.

Machine learning uses algorithms to turn a data set into a model. Which algorithm works best depends on the problem