8 alternatives to TensorFlow Serving

Tensorflow

TensorFlow Serving is an easy-to-deploy, flexible and high performing serving system for machine learning models built for production environments. It allows easy deployment of algorithms and experiments while allowing developers to keep the same server architecture and APIs. TensorFlow Serving provides seamless integration with TensorFlow models, and can also be easily extended to other models and data. 

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No code introduction to neural networks

The simple architecture explained – Neural networks have been around for a long time, being developed in the 1960s as a way to simulate neural activity for the development of artificial intelligence systems. However, since then they have developed into a useful analytical tool often used in replace of, or in conjunction with, standard statistical models such as regression or classification as they can be used to predict or more a specific output.

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Best MLOps Platforms to Manage Machine Learning Lifecycle

MLOps Platforms

The machine learning lifecycle is the process of developing machine learning projects in an efficient manner. Building and training a model is a difficult, long process, but it’s just one step of your whole task. There’s a long process behind the machine learning lifecycle: collecting data, preparing data, analyzing, training, and testing the model. 

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SambaNova raises $676M at a $5.1B valuation to double down on cloud-based AI software for enterprises

Sambanova

SambaNova — a startup building AI hardware and integrated systems that run on it that only officially came out of three years in stealth last December — is announcing a huge round of funding today to take its business out into the world. The company has closed in on $676 million in financing, a Series D that co-founder and CEO Rodrigo Liang has confirmed values the company at $5.1 billion.

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What the Floq: The secret mission Of Google

Google

Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs. 

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Aerobotics improves training speed by 24 times per sample with Amazon SageMaker and TensorFlow

AWS cloud

Aerobotics is an agri-tech company operating in 18 countries around the world, based out of Cape Town, South Africa. Our mission is to provide intelligent tools to feed the world. We aim to achieve this by providing farmers with actionable data and insights on our platform, Aeroview, so that they can make the necessary interventions at the right time in the growing season.

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Choosing the right Machine Learning Framework

Machine Learning Frameworks

Companies can choose to build their own custom machine learning framework, but most organizations choose an existing framework that fits their needs. In this article, we’ll show key considerations for selecting the right machine learning framework for your project and briefly review four popular ML frameworks. How to choose the right ML Framework.

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Machine learning speeding up patent classifications at USPTO

US Patent and Trademark Office

Machine learning is helping the U.S. Patent and Trademark Office shorten the time it takes to assign patent applications to examiners, instead of having to redo its entire classification process, according to CIO Jamie Holcombe. USPTO sent its top engineers to Google on the East and West coasts to learn more about ML and TensorFlow application programming interfaces.

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Introducing Model Search: An Open Source Platform for Finding Optimal ML Models

Google Model Search

he success of a neural network (NN) often depends on how well it can generalize to various tasks. However, designing NNs that can generalize well is challenging because the research community’s understanding of how a neural network generalizes is currently somewhat limited: What does the appropriate neural network look like for a given problem? How deep should it be? Which types of layers should be used? Would LSTMs be enough or would Transformer layers be better? Or maybe a combination of the two? Would ensembling or distillation boost performance? These tricky questions are made even more challenging when considering machine learning (ML) domains where there may exist better intuition and deeper understanding than others.

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3D Scene understanding with TensorFlow 3D

3D Scene understanding

The growing ubiquity of 3D sensors (e.g., Lidar, depth sensing cameras and radar) over the last few years has created a need for scene understanding technology that can process the data these devices capture. Such technology can enable machine learning (ML) systems that use these sensors, like autonomous cars and robots, to navigate and operate in the real world, and can create an improved augmented reality experience on mobile devices. r.

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Why ML in production is (still) broken and ways we can fix it

By now, chances are you’ve read the famous paper about hidden technical debt by Sculley et al. from 2015. As a field, we have accepted that the actual share of Machine Learning is only a fraction of the work going into successful ML projects. The resulting complexity, especially in the transition to “live” environments, lead to large amounts of failed ML projects never reaching production.

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AI/ML Model Development Platform for Embedded MCU IoT Edge Gateway

As billions of devices getting connected to the Internet is not a distant reality anymore, there are more and more number of intelligent gateways needed to act as a backbone of complex infrastructure for internet-of-things (IoT). Need for more gateways are increasing which could work on low power in constrained environments as well as having capability to process as much data as possible, also termed as edge gateway.

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Training better Deep Learning Models for Structured Data using Semi-supervised Learning

In this post, we will use semi-supervised learning to improve the performance of deep neural models when applied to structured data in a low data regime. We will show that by using unsupervised pre-training we can make a neural model perform better than gradient boosting.

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