ML Pipeline A machine learning (ML) pipeline is a series of steps to build, train, and deploy machine learning models. An ML pipeline typically includes preprocessing data, training models, evaluating models, and deploying models for use in production. ML workflow
Spell, a NY-based startup has developed an innovative MLOps (machine learning operations) platform for the SMB market. Organizations can run ML experiments and workloads cost-effectively using features that fully automate the entire process. The platform leverages the latest in GPU
When it comes to AI frameworks, there are several tools available that can be used for tasks such as image classification, vision, and speech. Some are popular like PyTorch and Caffe, while others are more limited. Below are the highlights
Open-source AutoML tools are rapidly becoming dominant in the entrepreneurial landscape. These solutions help enterprises implement and scale their ML and AI projects. AutoML open-source tools automate the entire life cycle of ideating, conceptualization, development, and deployment of predictive models.
When it comes to developing deep learning predictive models, there are several stages to building a model from raw data. In the first stage, it must be determined if AI can be of use and how, thus, what specific problems
The AI industry is making progress at simplifying distributed machine learning, defined as the process of scheduling AI workloads across multiple GPUs and machines (nodes). One of the primary benefits of distributed machine learning is its ability to reduce model
In 2020, it’s predicted that each person on earth will create 1.7MB/second (147GB/day) of data. ARM believes that 1 trillion IoT devices “will be produced between now and 2035” with some of these devices lasting a short time, while others
San Francisco startup Weights & Biases (WB) is making a name for itself in the AI tools market. As an “experiment tracking platform for deep learning”, WB allows users to collaborate, “visualize model performance”, and track model versions, code, workflows,
The number of startups entering the AI chip market is growing considerably. Although there are dozens of startups developing specialized chips for machine learning, some startups really stand out. One such startup is Groq. And they stand out not only
The AI race in different regions of the world continues. The US leads in both quantitative and qualitative metrics, where quantitative is measured in the number of startups and qualitative is based on a value system that assigns points to
DotData, an AI startup spun off from NEC recently raised $23M, and $43M in total. The startup has experienced a 300% revenue growth over the last 18 months. They describe their service as an “end-to-end data science automation” platform, offering
Habana Labs, an innovative startup has developed an AI processor designed specifically to run deep neural networks (DNN). The software-programmable processor is available as a PCIe card and mezzanine card. The AI processor works for both training models and inference
It was only a matter of time before an AI startup would tackle the GPU problem head-on, the problem being the GPU itself and why it’s needed in the first place. That startup is Neural Magic and they’ve developed an
The rapid growth in AI is having a big impact on the development and sale of specialized chips. Allied Market Research noted in the Artificial Intelligent Chip Market Outlook that AI chip sales are predicted to grow from $6.6B in
