Azure Machine Learning service is Azure based service with provided on-prem Python SDK, that combines both worlds, cloud and on-prem, and brings data scientists ability to quickly prepare data, train and deploy machine learning models and consume the predictions. Azure ML service provides broader usage of cloud and improves the productivity, helps burden the knowledge scarcity, reduces costs and does auto-scaling. Azure ML Service embraces also many open-source python frameworks and packages, such as Scikit-Learn, TensorFlow, Pytorch and MXnet. In this session we will explore many of the service features, the ability to combine on-prem with cloud work (using notebooks), connecting to Kubernetes Service, Databricks and other features.

For a subscription service business, there are two ways to drive growth: grow the number of new customers, or increase the lifetime value from the customers that you already have by retaining more of them. Improving customer retention requires the ability to predict which subscribers are likely to cancel (referred to as churn), and to intervene with the right retention offers at the right time. Recently, the use of deep learning algorithms that learn sequential product usage customer behavior to make predictions have begun to offer businesses a more powerful method to pinpoint accounts at risk. This understanding of an account's churn likelihood allows a company to proactively act to save the most valuable customers before they cancel. CSE recently partnered with the finance group of Majid Al Futtaim Ventures (MAF), a leading mall, communities, retail and leisure pioneer across the Middle East, Africa and Asia, to design and deploy a machine learning solution to predict attrition within their consumer credit card customer base. MAF sought to use their customer records – including transaction and incident history plus account profile information – to inform a predictive model.

McKinsey reports AI adopters with a proactive strategy achieve significantly higher profit margins -- between 3% and 15% above the industry average. Today, two-thirds of executives cite AI as vital to the future of their business, with plans to increase investments this year. As a result, IDC reports the global AI market is forecast to accelerate further in 2022 with 18.8% growth and remain on track to break the $500 billion mark by 2024. MLOps--machine learning operations, or DevOps for machine learning--enables data science and IT teams to collaborate and increase the pace of model development and deployment via monitoring, validation, and governance of machine learning models. MLOps, or machine learning operations, refers to the process and tooling of consistently developing, deploying and maintaining reliable, responsible AI.

This repo contains end-to-end recipes to pretrain and finetune the BERT (Bidirectional Encoder Representations from Transformers) language representation model using Azure Machine Learning service. That implementation uses ONNX Runtime to accelerate training and it can be used in environments with GPU including Azure Machine Learning service. Details on using ONNX Runtime for training and accelerating training of Transformer models like BERT and GPT-2 are available in the blog at ONNX Runtime Training Technical Deep Dive. BERT is a language representation model that is distinguished by its capacity to effectively capture deep and subtle textual relationships in a corpus. In the original paper, the authors demonstrate that the BERT model could be easily adapted to build state-of-the-art models for a number of NLP tasks, including text classification, named entity recognition and question answering.

Trey Research Inc. delivers innovative solutions for manufacturers. They specialize in identifying and solving problems for manufacturers that can run the range from automating away mundane but time-intensive processes to delivering cutting edge approaches that provide new opportunities for their manufacturing clients. Trey Research is looking to provide the next generation experience for connected car manufacturers by enabling them to utilize AI to decide when to pro-actively reach out to the customer thru alerts delivered directly to the car's in-dash information and entertainment head unit. For their PoC, they would like to focus on two maintenance related scenarios. In the first scenario, Trey Research recently instituted new regulations defining what parts are compliant or out of compliance.

Disclaimer: The following content is not officially endorsed by Microsoft. I've been wanting to learn about investments, the stock market, machine learning, and artificial intelligence for a while now so I thought, why not combine them all together? So, today I decided to try and create my own machine learning model using Azure Machine Learning services, some free ASX historical data as my data sets and output a model that helps determine whether I should Buy, Hold, or Sell a stock listed on the Australian Stock Exchange. Since this is for experimental and learning purposes, I have to note that whatever model I am about to create and deploy should not be taken seriously (as a financial advisor, financial guidance, etc). But before I dive in any further, let's take a step back and discuss the basics... Why is everyone talking about Artificial Intelligence and Machine Learning? Machine Learning is one component of Artificial Intelligence (or AI).

I've been wanting to learn about investments, the stock market, machine learning, and artificial intelligence for a while now so I thought, why not combine them all together? So, today I decided to try and create my own machine learning model using Azure Machine Learning services, some free ASX historical data as my data sets and output a model that helps determine whether I should Buy, Hold, or Sell a stock listed on the Australian Stock Exchange. Since this is for experimental and learning purposes, I have to note that whatever model I am about to create and deploy should not be taken seriously (as a financial advisor, financial guidance, etc). But before I dive in any further, let's take a step back and discuss the basics… Machine Learning is one component of Artificial Intelligence (or AI). Artificial intelligence is essentially trying to use machines to imitate human-like behavior and intelligence. So then you may ask, "Well, is my calculator an AI system because it can calculate answers to equations much faster than I can."

Is your business ready to embrace artificial intelligence (AI)? At a recent event, Microsoft's head of AI urged business leaders to get their heads around the applications and ethics of the technology, saying that over the next decade, every company is going to become led by AI. Speaking at Australia's Future Briefing event in February 2020, Mitra Azizirad, corporate vice president of Microsoft AI, said that AI has the potential to be more of a game changer than any technological advance that has come before it; it is the next technology set to "run the world." "Software has transformed every industry; you hear it all the time – every company became a software company," Azizirad said. "But that's really changing because AI is now a totally different way to create software."

AI is fueling the next wave of transformative innovations that will change the world. With Azure AI, our goal is to empower organizations to apply AI across the spectrum of their business to engage customers, empower employees, optimize operations and transform products. These guiding principles enable us to fulfill our mission of empowering every developer and every organization to harness the potential of AI. With research centers that span the globe, from Redmond to Shanghai, we continue to achieve industry breakthroughs in areas such as vision, speech, language, advanced machine learning techniques, and specialized AI hardware. These innovations are now key components of several of our flagship products, like Office 365, Xbox, Bing and Dynamics 365.

Before I begin, let me tell you that this post is part of the Microsoft Student Partners Developer Stories initiative, and is based on the AI and ML Track. We will be exploring various Azure services - Azure Notebooks, Machine Learning Service, Container Instances and Container Registry. This post is beginner-friendly and can be used by anyone to deploy their machine learning models to Azure in a Standard format. Even high school kids are creating Machine Learning models these days, using popular machine learning frameworks like Keras, PyTorch, Caffe, etc. The model format created in one framework slightly differs with the model format created in the other.