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Isaiah Bennett
Isaiah Bennett

Event €? Making AI Easier With Azure CognitiveServices

Building solutions with machine learning often requires a data scientist. Azure Cognitive Services enable organizations to take advantage of AI with developers, without requiring a data scientist. We do this by taking the machine learning models and the pipelines and the infrastructure needed to build a model and packaging it up into a Cognitive Service for vision, speech, search, text processing, language understanding, and more. This makes it possible for anyone who can write a program, to now use machine learning to improve an application. However, many enterprises still face challenges building large-scale AI systems. Today we announced container support for Cognitive Services, making it significantly easier for developers to build ML-driven solutions.

Event – Making AI easier with Azure CognitiveServices

Azure User Group Portugal is a user group for anyone interested in Cloud Computing with a great focus on Microsoft Azure. If you work with or have an interest in the Microsoft Cloud this is the user group to attend and follow our events. Join our group to keep posted about all the new meetings. Looking forward to having you as a member.

Azure Event Hubs is a highly scalable publish-subscribe service that can ingest millions of events per second and stream them to multiple consumers. This lets you process and analyze the massive amounts of data produced by your connected devices and applications. Once Event Hubs has collected the data, you can retrieve, transform, and store it by using any real-time analytics provider or with batching/storage adapters.

A lot of learnings are accumulated from events. Events like Azure Summit enables developers, engineers, solutions architects, and enthusiasts to learn new skills. Do check out the website of Azure Summit to be in touch with the recent happenings in Azure.

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IVR applications can increase customer loyalty, improve sales and marketing, cut costs, and boost efficiency. It is one of the best azure hands-on projects to conduct a market search since IVR apps have multiple use cases. You can measure and improve customer support with phone surveys, generate phone leads, and receive customer feedback.

A new area is emerging at the intersection of machine learning (ML) and systems design. This birth is driven by the explosive growth of diverse applications of ML in production, the continued growth in data volume, and the complexity of large-scale learning systems. Addressing the challenges in this intersection demands a combination of the right abstractions -- for algorithms, data structures, and interfaces -- as well as scalable systems capable of addressing real world learning problems. Designing systems for machine learning presents new challenges and opportunities over the design of traditional data processing systems. For example, what is the right abstraction for data consistency in the context of parallel, stochastic learning algorithms? What guarantees of fault tolerance are needed during distributed learning? The statistical nature of machine learning offers an opportunity for more efficient systems but requires revisiting many of the challenges addressed by the systems and database communities over the past few decades. Machine learning focused developments in distributed learning platforms, programming languages, data structures, general purpose GPU programming, and a wide variety of other domains have had and will continue to have a large impact in both academia and industry.As the relationship between the machine learning and systems communities has grown stronger, new research in using machine learning tools to solve classic systems challenges has also grown. Specifically, as we develop larger and more complex systems and networks for storing, analyzing, serving, and interacting with data, machine learning offers promise for modeling system dynamics, detecting issues, and making intelligent, data-driven decisions within our systems. Machine learning techniques have begun to play critical roles in scheduling, system tuning, and network analysis. Through working with systems and databases researchers to solve systems challenges, machine learning researchers can both improve their own learning systems as well impact the systems community and infrastructure at large.The goal of this workshop is to bring together experts working at the crossroads of ML, system design and software engineering to explore the challenges faced when building practical large-scale machine learning systems. In particular, we aim to elicit new connections among these diverse fields, identify tools, best practices and design principles. The workshop will cover ML and AI platforms and algorithm toolkits (Caffe, Torch, TensorFlow, MXNet and parameter server, Theano, etc), as well as dive into the reality of applying ML and AI in industry with challenges of data and organization scale (with invited speakers from companies like Google, Microsoft, Facebook, Amazon, Netflix, Uber and Twitter).The workshop will have a mix of invited speakers and reviewed papers with talks, posters and panel discussions to facilitate the flow of new ideas as well as best practices which can benefit those looking to implement large ML systems in academia or industry.Focal points for discussions and solicited submissions include but are not limited to:- Systems for online and batch learning algorithms- Systems for out-of-core machine learning- Implementation studies of large-scale distributed learning algorithms --- challenges faced and lessons learned- Database systems for Big Learning --- models and algorithms implemented, properties (fault tolerance, consistency, scalability, etc.), strengths and limitations- Programming languages for machine learning- Data driven systems --- learning for job scheduling, configuration tuning, straggler mitigation, network configuration, and security- Systems for interactive machine learning- Systems for serving machine learning models at scale

Azure functions are used in serverless computing architectures where subscribers can execute code as an event driven Function-as-a-Service (FaaS) without managing the underlying server resources.[37] Customers using Azure functions are billed based on per-second resource consumption and executions.[38]

Explanation:Azure's Computer Vision API includes Optical Character Recognition (OCR) capabilities that extract printed or handwritten text from images. You can extract text from images, such as photos of license plates or containers with serial numbers, as well as from documents - invoices, bills, financial reports, articles, and more.Reference: -us/azure/cognitive-services/computer-vision/concept-recognizing-text

The Create ML app lets you quickly build and train Core ML models right on your Mac with no code. The easy-to-use app interface and models available for training make the process easier than ever, so all you need to get started is your training data. You can even take control of the training process with features like snapshots and previewing to help you visualize model training and accuracy. Dive deeper and gain more control of model creation using the Create ML framework and Create ML Components.

Optionally pass an eventId that uniquelyidentifies this Rank event.If null, the service generates a unique eventId. The eventId willbe used for associating this request with its reward, as well as seeding thepseudo-random generator when making a Personalizer call.

EventId Optionally pass an eventId that uniquely identifies this Rank event.If null, the service generates a unique eventId. The eventId will be used forassociating this request with its reward, as well as seeding the pseudo-randomgenerator when making a Personalizer call.


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