by Contributed | Nov 17, 2020 | Azure, Microsoft, Technology
This article is contributed. See the original author and article here.
This is a series of Azure applied workshops where participants will start with the basics of cloud, getting started on Azure, and will then move on to learn about Azure’s Data and Machine Learning tools. This training series is intended for a broad audience including students, researchers, faculty, post-docs, staff and anyone interested in getting started and learning Azure.
This series will get you started on Azure and provide hands on experience managing data and leveraging Azure’s data science and machine learning tools.
The Azure workshop series will focus on the following topics:
1. Getting Started with Azure – Part 1 [1 of 19]
2. Getting Started with Azure – Part 2 [2 of 19]
3. Getting Started with Azure – Part 3 [3 of 19]
4. Getting Started with Azure – Part 4 [4 of 19]
5. Getting Started with Azure – Part 5 [5 of 19]
6. Exercise – Setting up first Subscription [6 of 19]
7. Exercise – Preparing for Machine Learning [7 of 19]
8. Exercise – Preparing for Working with Data [8 of 19]
9. What is Machine Learning? [9 of 19]
10. Azure ML Tools [10 of 19]
11. Exercise – Build a ML Model with Azure [11 of 19]
12. Exercise – Deploy and Consume a ML Model [12 of 19]
13. Using Automated ML [13 of 19]
14. Exercise – Deploy your ML Model on Azure [14 of 19]
15. Benefits Working with Data [15 of 19]
16. Databases [16 of 19]
17. Unstructured Data [17 of 19]
18. Exercise: How to Transform Data [18 of 19]
19. Tools and Integrations [19 of 19]
Additional resources and links:
by Contributed | Nov 17, 2020 | Azure, Microsoft, Technology
This article is contributed. See the original author and article here.
Azure Data Explorer offers ingestion (data loading) from Event Hubs, a big-data streaming platform and event ingestion service. Event Hubs can process millions of events per second in near real-time.
The “1-click” tool now intelligently suggests the relevant table mapping definitions according to the Event Hub source, in just 4 clicks.
Click 1:
Click on “Ingest new data” in Kusto Web Explorer (KWE) –> Reference to Event Hub
Click 2:
Connect the Event Hub as a source for continuous event ingestion to a table (new/existing)
Click 3:
The 1-click tool fetches Event Hub events and infers its schema per the event structure. The user must only verify the inferred schema, and make any necessary adjustments.
Click 4:
Click on the “Start Ingestion” button to begin inferring the incoming events in either streaming or batching mode, per your needs.
Next time you need to connect Event Hub with Azure Data Explorer, use 1-click for an easy and accurate definition end to end ingestion definition from table to query.
** All actions are revertible by the service (Tools)
read more
by Contributed | Nov 17, 2020 | Azure, Microsoft, Technology
This article is contributed. See the original author and article here.
Azure Data Studio – a cross-platform client tool with hybrid and poly cloud capabilities – now brings KQL experiences for modern data professionals. In this episode with Julie Koesmarno, she will show KQL magic in Notebooks and the native KQL experiences – all in Azure Data Studio. You’ll also learn more about the use cases for KQL experiences in Azure Data Studio and our roadmap.
Watch on Data Exposed
Resources:
View/share our latest episodes on Channel 9 and YouTube!
by Contributed | Nov 17, 2020 | Azure, Microsoft, Technology
This article is contributed. See the original author and article here.
Follow the same steps mentioned here Publish a bot to Azure – Bot Composer | Microsoft Docs However to publish Azure Bot Framework Composer Bot to already existing Resource Group, follow below steps.
You can find the provisioning steps in the readme.md file which is automatically created when you create a new Bot in Bot Composer.
As of now, by default, provisioning script creates a new Resource Group appending environment value to it and does not deploy the services to an existing Resource Group.
Find value
const resourceGroupName = ${name}-${environment};
and change it to
const resourceGroupName = ${name};
in the file provisionComposer.js and save. With this simple change, it will not append the given Environment value to Resource Group name.
Why this will work?
In provisionComposer.js file, the code
const createResourceGroup = async (client, location, resourceGroupName) => { logger({ status: BotProjectDeployLoggerType.PROVISION_INFO, message: "> Creating resource group ...", }); const param = { location: location, }; return await client.resourceGroups.createOrUpdate(resourceGroupName, param); };
uses function client.resourceGroups.createOrUpdate – this function just checks if the RG exists or not, it’s present, it’ll use the same RG, if not, it’ll create a new RG and also if you provide a new location it’ll update the location to existing RG.
by Contributed | Nov 17, 2020 | Azure, Microsoft, Technology
This article is contributed. See the original author and article here.
Organizations are leveraging artificial intelligence (AI) and machine learning (ML) to derive insight and value from their data and to improve the accuracy of forecasts and predictions. In rapidly changing environments, Azure Databricks enables organizations to spot new trends, respond to unexpected challenges and predict new opportunities. Data teams are using Delta Lake to accelerate ETL pipelines and MLflow to establish a consistent ML lifecycle.
Solving the complexity of ML frameworks, libraries and packages
Customers frequently struggle to manage all of the libraries and frameworks for machine learning on a single laptop or workstation. There are so many libraries and frameworks to keep in sync (H2O, PyTorch, scikit-learn, MLlib). In addition, you often need to bring in other Python packages, such as Pandas, Matplotlib, numpy and many others. Mixing and matching versions and dependencies between these libraries can be incredibly challenging.
Figure 1. Databricks Runtime for ML enables ready-to-use clusters with built-in ML Frameworks
With Azure Databricks, these frameworks and libraries are packaged so that you can select the versions you need as a single dropdown. We call this the Databricks Runtime. Within this runtime, we also have a specialized runtime for machine learning which we call the Databricks Runtime for Machine Learning (ML Runtime). All these packages are pre-configured and installed so you don’t have to worry about how to combine them all together. Azure Databricks updates these every 6-8 weeks, so you can simply choose a version and get started right away.
Establishing a consistent ML lifecycle with MLflow
The goal of machine learning is to optimize a metric such as forecast accuracy. Machine learning algorithms are run on training data to produce models. These models can be used to make predictions as new data arrive. The quality of each model depends on the input data and tuning parameters. Creating an accurate model is an iterative process of experiments with various libraries, algorithms, data sets and models. The MLflow open source project started about two years ago to manage each phase of the model management lifecycle, from input through hyperparameter tuning. MLflow recently joined the Linux Foundation. Community support has been tremendous, with 250 contributors, including large companies. In June, MLflow surpassed 2.5 million monthly downloads.
Diagram: MLflow unifies data scientists and data engineers
Ease of infrastructure management
Data scientists want to focus on their models, not infrastructure. You don’t have to manage dependencies and versions. It scales to meet your needs. As your data science team begins to process bigger data sets, you don’t have to do capacity planning or requisition/acquire more hardware. With Azure Databricks, it’s easy to onboard new team members and grant them access to the data, tools, frameworks, libraries and clusters they need.
Alignment Healthcare
Alignment Healthcare, a rapidly growing Medicare insurance provider, serves one of the most at-risk groups of the COVID-19 crisis—seniors. While many health plans rely on outdated information and siloed data systems, Alignment processes a wide variety and large volume of near real-time data into a unified architecture to build a revolutionary digital patient ID and comprehensive patient profile by leveraging Azure Databricks. This architecture powers more than 100 AI models designed to effectively manage the health of large populations, engage consumers, and identify vulnerable individuals needing personalized attention—with a goal of improving members’ well-being and saving lives.
Building your first machine learning model with Azure Databricks
by Contributed | Nov 17, 2020 | Azure, Microsoft, Technology
This article is contributed. See the original author and article here.
Today, Azure is proud to take the next step toward our commitment to enabling customers to harness the power of AI (Artificial Intelligence) at scale. For AI, the bar for innovation has never been higher with hardware requirements for training models far outpacing Moore’s Law. Technology leaders across industries are discovering new ways to apply the power of machine learning, accelerated analytics and AI to make sense of unstructured data. The natural language models of today are exponentially larger than the largest models of four short years ago.
OpenAI’s GPT-3 model, for instance, has three orders of magnitude more parameters than the ResNet-50 image classification model that was at the forefront of AI in the mid-2010s. These kinds of demanding workloads required the development of a new class of system within Microsoft Azure, from the ground-up using the latest hardware innovations.
The Azure team has built on our experience virtualizing the latest GPU technology, and building the public cloud industry’s leading InfiniBand-enabled HPC virtual machines to offer something totally new for AI in the cloud. Each deployment of an ND A100 v4 cluster rivals the largest AI supercomputers in the industry in terms of raw scale and advanced technology. These VMs enjoy the same unprecedented 1.6 Tb/s of total dedicated InfiniBand bandwidth per VM, plus AMD Rome-powered compute cores behind every NVIDIA A100 GPU as used by the most powerful dedicated on-premise HPC systems. Azure adds massive scale, elasticity, and versatility of deployment, as expected by Microsoft’s customers and internal AI engineering teams.
This unparalleled scale and capability of interconnect in a cloud offering, with each GPU directly paired with a high-throughput low-latency InfiniBand interface, offers our customers a unique dimension of scaling on demand without managing their own datacenters.
Today, at SC20, we’re announcing the public preview of the ND A100 v4 VM family, available from one virtual machine to world-class supercomputer scale, with each individual VM featuring:
- Eight of the latest NVIDIA A100 Tensor Core GPUs with 40 GB of HBM2 memory, offering a typical per-GPU performance improvement of 1.7x – 3.2x compared to V100 GPUs- or up to 20x by layering features like new mixed-precision modes, sparsity, and MIG- for significantly lower total cost of training with improved time-to-solution
- VM system-level GPU interconnected based on NVLINK 3.0 + NVswitch
- One 200 Gigabit InfiniBand HDR link per GPU with full NCCL2 support and GPUDirect RDMA for 1.6 Tb/s per virtual machine
- 40 Gb/s front-end Azure networking
- 6.4 TB of local NVMe storage
- InfiniBand-connected job sizes in the thousands of GPUs, featuring any-to-any and all-to-all communication without requiring topology aware scheduling
- 96 physical AMD Rome vCPU cores with 900 GB of DDR4 RAM
- PCIe Gen 4 for the fastest possible connections between GPU, network and host CPUs- up to twice the I/O performance of PCIe Gen 3-based platforms
Like other Azure GPU virtual machines, ND A100 v4 is also available with Azure Machine Learning (AML) service for interactive AI development, distributed training, batch inferencing, and automation with ML Ops. Customers can choose to deploy through AML or traditional VM Scale Sets, and soon many other Azure-native deployment options such as Azure Kubernetes Service. With all of these, optimized configuration of the systems and InfiniBand backend network is taken care of automatically.
Azure Machine Learning provides a tuned virtual machine (pre-installed with the required drivers and libraries) and container-based environments optimized for the ND A100 v4 family. Sample recipes and Jupyter Notebooks help users get started quickly with multiple frameworks including PyTorch, TensorFlow, and training state of the art models like BERT. With Azure Machine Learning, customers have access to the same tools and capabilities in Azure as our AI engineering teams.
Accelerate your innovation and unlock your AI potential with the ND A100 v4.
Preview sign-up is open. Request access now.
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