Machine learning as a service (MLaaS) refers to a number of services that offer machine learning tools as a part of cloud computing services. The main benefit of this solution is that customers can get started with machine learning applications quickly without installing specific software or provisioning their own servers. All the actual computations are handled by the provider’s own data centers.
MLaaS providers offer services for data transformation, predictive analytics, data visualization, and advanced machine learning algorithms. Currently, the major MLaaS platforms suggest ready-made solutions for the majority of popular machine learning applications, including recommender systems, forecasting, image and video analysis, advanced text analytics, machine translation, automated transcription, speech generation, and conversational agents. Read More
Tag Archives: MLaaS
18 Handy Resources for Machine Learning Practitioners
Machine Learning is a diverse field covering a wide territory and has impacted many verticals. It is able to tackle tasks in language and image processing, anomaly detection, credit scoring sentiment analysis, forecasting alongside dozens of other downstream tasks. A proficient developer, in this line of work; has to be able to draw, borrow, and steal from many adjacent fields such as mathematics, statistics, programming, and most importantly common sense. I for one have drawn tremendous benefits from myriad of tools available to break down complex tasks into smaller more manageable components. It turns out that developing and training a model only takes a small fraction of the project duration. The bulk of the time and resources are spent on data acquisition, preparation, hyperparameter tuning, optimization, and model deployment. I have been successful in building a systematic knowledge base that has helped my team to tackle some common yet tough challenges. Read More
The Iguazio Data Science Platform
The Iguazio Data Science Platform (“the platform”) is a fully integrated and secure data science platform as a service (PaaS), which simplifies development, accelerates performance, facilitates collaboration, and addresses operational challenges. It provides a complete data science workflow in a single ready-to-use platform that includes all the required building blocks for creating data science applications from research to production. Read More
Deploy Your First Serverless AWS ML Solution Fast
I’ve been working with AWS SageMaker for a while now and have enjoyed great success. Creating and tuning models, architecting pipelines to support both model development and real-time inference, and data lake formation have all been made easier in my opinion. AWS has proven to be an all encompassing solution for machine learning use cases, both batch and real-time, helping me decrease time to delivery. Read More
#devops, #mlaasAutoML-Zero: Evolving Machine Learning Algorithms From Scratch
Machine learning research has advanced in multiple aspects, including model structures and learning methods. The effort to automate such research, known as AutoML, has also made significant progress. However, this progress has largely focused on the architecture of neural networks, where it has relied on sophisticated expert-designed layers as building blocks—or similarly restrictive search spaces. Our goal is to show that AutoML can go further: it is possible today to automatically discover complete machine learning algorithms just using basic mathematical
operations as building blocks. We demonstrate this by introducing a novel framework that significantly reduces human bias through a generic search space. Despite the vastness of this space, evolutionary search can still
discover two-layer neural networks trained by backpropagation. These simple neural networks can then be surpassed by evolving directly on tasks of interest, e.g. CIFAR-10 variants, where modern techniques emerge in
the top algorithms, such as bilinear interactions, normalized gradients, and weight averaging. Moreover, evolution adapts algorithms to different task types: e.g., dropout-like techniques appear when little data is available. We
believe these preliminary successes in discovering machine learning algorithms from scratch indicate a promising new direction for the field. Read More
Stop Experimenting With Machine Learning And Start Actually Using It
It turns out there’s a fatal flaw in most companies’ approach to machine learning, the analytical tool of the future: 87% of projects do not get past the experiment phase and so never make it into production.
Why do so many companies, presumably on the basis of rational decisions, limit themselves simply to exploring the potential of machine learning, and even after undertaking large investments, hiring data scientists and investing resources, time and money, fail to take things to the next level?
Quite simply, an inbuilt experimental mindset. Read More
The Top Cloud-Based AI Services (2018)
The major cloud providers have each rolled out a cloud-based AI service. We take a deep dive on the big players, whose AI services are markedly different from one another. Read More
AI Trends 2018: Machine Learning as a Service (MLaaS)
n recent years driven by a major move into digital platforms and microservices. This shift has been made possible because of the cloud computing revolution, particularly the massive growth of public cloud services provided by enterprise companies such as Amazon, Microsoft and Google, among others. These enterprises have put an enormous emphasis on the “as a service” business model, which allows outside companies to pick and choose necessary microservices provided by the enterprises. Read More
Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI, IBM Watson
For most businesses, machine learning seems close to rocket science, appearing expensive and talent demanding. And, if you’re aiming at building another Netflix recommendation system, it really is. But the trend of making everything-as-a-service has affected this sophisticated sphere, too. You can jump-start an ML initiative without much investment, which would be the right move if you are new to data science and just want to grab the low hanging fruit. Read More
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