AI News, QA with Sentient: on Artificial Intelligence, Open Source Machine Learning Model Management Framework, and the Studio.ML Platform

QA with Sentient: on Artificial Intelligence, Open Source Machine Learning Model Management Framework, and the Studio.ML Platform

The meteoric rise of artificial intelligence (AI), particularly machine learning (ML), is significantly disrupting the business landscape and spurring workforce change across different industries such as manufacturing, transportation, retailing, finance, healthcare, advertising, insurance, entertainment, and more.

As businesses enter into a new era ruled by data, organizations can now leverage machine learning algorithms to identify key trends and gain actionable insights by analyzing vast reams of data to make faster and smarter business decisions.

With virtually every industry transforming their business models and core processes to take advantage of machine learning tools, companies should invest in a unifying framework that can help them solve data problems and lessen overhead involved with running, scheduling, monitoring, and managing their machine learning experiments.

Navruzyan also discusses the benefits of using machine learning model management frameworks and shares how Studio.ML can support data scientists and DevOps to accelerate their scientific research and produce models that help analyze complex and large volumes of data in order to deliver faster, more accurate results.

First, digital marketing, through a product called Sentient Ascend, which provides a new approach to conversion rate optimization (CRO) using genetic algorithms to automate and accelerate the testing of thousands of web page combinations in the same time as a single A/B test.

Second, in retail with a product called Sentient Aware, which provides AI-powered product recommendations using deep learning and online learning to curate products for each customer based on their individual, in-the-moment visual preferences.

We’ll be able to leverage AI systems to help get things to where they need to go faster and cheaper, we’ll be able to enable people to buy things they weren’t even aware existed or even knew they wanted, we’ll be able to help predict fatal diseases before they get past the point of no return.

The role of the ML model management frameworks is, on one hand, to partially automate the trial-and-error via so-called hyperparameter optimization or search (in essence, search for what training options work best for a given problem), and on the other hand, to keep track of what has been tried and help researchers reuse those results (both their own and the colleagues’ results) –

approaches such as reinforcement learning of neural network architecture or neuroevolution) and by aggregation of knowledge from multiple domains and datasets (transfer learning) that should help in the situations when high-quality labelled data is not available or too expensive.

For example, when captioning images for visually impaired people, direct training data (images with proper descriptions) is relatively scarce, however, one has a virtually infinite supply of text or images with simple attributes from the internet that transfer learning should be able to tap into.

At Sentient, our research team is heavily focused on both architecture search using neuroevolution (in fact, there have been several recent breakthroughs in that area whereby neuroevolution was able to find seemingly strange neural network architectures that nonetheless performed better than a hand-crafted state of the art), and on transfer learning.

While (fortunately) these are steadily getting cheaper by virtue of Moore’s law, managing an increasing amount of compute resources can generate an increasing amount hassle, so the role of machine learning management frameworks like Studio.ML will increase.

Getting started with Studio.ML

Studio.ML is a Python machine learning model management framework developed to help schedule, run, monitor and manage artifacts of your ML models.

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