Large cloud vendors like Amazon, Google, Microsoft, and IBM offer APIs enterprises can use to take advantage of powerful AI models. But comparing these models — both in terms of performance and cost — can be challenging without thorough planning. Moreover, the siloed nature of the APIs makes it difficult to unify services from different vendors into a single app or workflow without custom engineering work, which can be costly.
These challenges inspired Samy Melaine and Taha Zemmouri to found Eden AI (previously AI-Compare) in 2020. The platform draws on AI APIs from a range of sources to allow companies to mix and match models to suit their use case. Eden AI recently launched what it calls an AI management platform, which the company claims simplifies the use — and integration — of various models for end users. Read More
Tag Archives: MLaaS
Scale Neural Network Training with SageMaker Distributed
As a machine learning practitioner, you might find yourself in the following situations. You might have found just the perfect state of the art transformer-based model, only to find that when you try to fine-tune it you run into memory issues. You might have just added billions of parameters to your model, which should improve your model performance, but this too only gets you an out of memory issue. You might be able to comfortably fit a model on a single GPU, but are struggling to take advantage of all your GPU’s and find that your model still takes days to train.
Should you just accept the status quo and limit your applications to models that fit within the existing hardware capacity or that train within an acceptable time? Of course not! Enter model parallelism and data parallelism on Amazon SageMaker. Read More
Microsoft’s new Lobe app lets anyone train AI models
A Very Simple Introduction to Deep Learning on Amazon Sagemaker
Low-code platforms and the democratization of AI
Tech giants like IBM and Amazon are developing products that will make it easier for people without a coding background to build apps that integrate with AI services.
Key Takeaway: Low-code and no-code platforms are seeing new life, as access to artificial intelligence and fast deployment of applications becomes increasingly critical with the popularity of Cloud-based software development. These products enable those without a coding background to more easily access the benefits of AI. Read More
Low-Code Can Lower the Barrier to Entry for AI
Organizations that want to get started quickly with machine learning may be interested in investigating emerging low-code options for AI. While low-code techniques will never completely replace hand-coded systems, they can help accelerate smaller, less experienced data science teams, as well as help with prototyping for professional data scientists.
First of all, what is low-code? Well, the phrase can mean different things to different people, and its applicability to AI is not entirely nailed down. Mainstream developers have been using low-code (or no-code) approaches to creating business and consumer applications for years, and that largely forms the basis for low-code approaches in AI. Read More
A Tour of End-to-End Machine Learning Platforms
Machine Learning (ML) is known as the high-interest credit card of technical debt. It is relatively easy to get started with a model that is good enough for a particular business problem, but to make that model work in a production environment that scales and can deal with messy, changing data semantics and relationships, and evolving schemas in an automated and reliable fashion, that is another matter altogether. If you’re interested in learning more about a few well-known ML platforms, you’ve come to the right place! Read More
Why Best-of-Breed is a Better Choice than All-in-One Platforms for Data Science
All-in-one platforms built from open source software make it easy to perform certain workflows, but make it hard to explore and grow beyond those boundaries.
So you need to redesign your company’s data infrastructure.
Do you buy a solution from a big integration company like IBM, Cloudera, or Amazon? Do you engage many small startups, each focused on one part of the problem? A little of both? We see trends shifting towards focused best-of-breed platforms. That is, products that are laser-focused on one aspect of the data science and machine learning workflows, in contrast to all-in-one platforms that attempt to solve the entire space of data workflows.
This article, which examines this shift in more depth, is an opinionated result of countless conversations with data scientists about their needs in modern data science workflows. Read More
Architectures Every Data Scientist And Big Data Engineer Should Know
Comprehensive and Comparative List of Feature Store Architectures for Data Scientists and Big Data Professionals.
Feature store has become an important unit of organizations developing predictive services across any industry domain.
… This blog post highlights the features supported by different Feature Store frameworks, that are primarily developed by different leading industry giants. Read More
ABBYY Open-Sources NeoML, Machine Learning Library to Develop Artificial Intelligence Solutions
The framework provides software developers with powerful deep learning and traditional machine learning algorithms for creating applications that fuel digital transformation
ABBYY, a Digital Intelligence company, today announced the launch of NeoML, an open-source library for building, training, and deploying machine learning models. Available now on GitHub, NeoML supports both deep learning and traditional machine learning algorithms. The cross-platform framework is optimized for applications that run in cloud environments, on desktop and mobile devices. Compared to a popular open-source library, NeoML offers 15-20% faster performance for pre-trained image processing models running on any device.[1] The combination of higher inference speed with platform-independence makes the library ideal for mobile solutions that require both seamless customer experience and on-device data processing. Read More
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