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- An overall assessment of the hyperconverged infrastructure space including trends for datacenter transformation
- Real-world considerations for implementing hyperconverged infrastructure
- An evaluation of strengths and cautions for 12 key hyperconvergence vendors
Azure Machine Learning Studio provides an interactive canvas that allows you to develop, run, test, and iterate an experiment representing a predictive analysis model. There are a wide variety of modules available that can:
- Input data into your experiment
- Manipulate the data
- Train a model using machine learning algorithms
- Score the model
- Evaluate the results
- Output final values
Once you’re satisfied with your experiment, you can deploy it as a Classic Azure Machine Learning Web service or a New Azure Machine Learning Web service so that users can send it new data and receive back results.
In this article, we give an overview of the mechanics of how your Machine Learning model progresses from a development experiment to an operationalized Web service.
While Azure Machine Learning Studio is designed to help you develop and deploy a predictive analysis model, it’s possible to use Studio to develop an experiment that doesn’t include a predictive analysis model. For example, an experiment might just input data, manipulate it, and then output the results. Just like a predictive analysis experiment, you can deploy this non-predictive experiment as a Web service, but it’s a simpler process because the experiment isn’t training or scoring a machine learning model. While it’s not the typical to use Studio in this way, we’ll include it in the discussion so that we can give a complete explanation of how Studio works.
Developing and deploying a predictive Web service
Here are the stages that a typical solution follows as you develop and deploy it using Machine Learning Studio:
The training experiment
The training experiment is the initial phase of developing your Web service in Machine Learning Studio. The purpose of the training experiment is to give you a place to develop, test, iterate, and eventually train a machine learning model. You can even train multiple models simultaneously as you look for the best solution, but once you’re done experimenting you’ll select a single trained model and eliminate the rest from the experiment. For an example of developing a predictive analysis experiment, see Develop a predictive analytics solution for credit risk assessment in Azure Machine Learning.
The predictive experiment
Once you have a trained model in your training experiment, click Set Up Web Service and select Predictive Web Service in Machine Learning Studio to initiate the process of converting your training experiment to a predictive experiment. The purpose of the predictive experiment is to use your trained model to score new data, with the goal of eventually becoming operationalized as an Azure Web service.
This conversion is done for you through the following steps:
- Convert the set of modules used for training into a single module and save it as a trained model
- Eliminate any extraneous modules not related to scoring
- Add input and output ports that the eventual Web service will use
There may be more changes you want to make to get your predictive experiment ready to deploy as a Web service. For example, if you want the Web service to output only a subset of results, you can add a filtering module before the output port.
In this conversion process, the training experiment is not discarded. When the process is complete, you have two tabs in Studio: one for the training experiment and one for the predictive experiment. This way you can make changes to the training experiment before you deploy your Web service and rebuild the predictive experiment. Or you can save a copy of the training experiment to start another line of experimentation.
The Web service
Once you’re satisfied that your predictive experiment is ready, you can deploy your service as either a Classic Web service or a New Web service based on Azure Resource Manager. To operationalize your model by deploying it as a Classic Machine Learning Web service, click Deploy Web Service and select Deploy Web Service [Classic]. To deploy as New Machine Learning Web service, click Deploy Web Service and select Deploy Web Service [New]. Users can now send data to your model using the Web service REST API and receive back the results. For more information, see How to consume an Azure Machine Learning Web service.
The non-typical case: creating a non-predictive Web service
If your experiment does not train a predictive analysis model, then you don’t need to create both a training experiment and a scoring experiment – there’s just one experiment, and you can deploy it as a Web service. Machine Learning Studio detects whether your experiment contains a predictive model by analyzing the modules you’ve used.
After you’ve iterated on your experiment and are satisfied with it:
- Click Set Up Web Service and select Retraining Web Service – input and output nodes are added automatically
- Click Run
- Click Deploy Web Service and select Deploy Web Service [Classic] or Deploy Web Service [New] depending on the environment to which you want to deploy.
Your Web service is now deployed, and you can access and manage it just like a predictive Web service.
Updating your Web service
Now that you’ve deployed your experiment as a Web service, what if you need to update it?
That depends on what you need to update:
You want to change the input or output, or you want to modify how the Web service manipulates data
If you’re not changing the model, but are just changing how the Web service handles data, you can edit the predictive experiment and then click Deploy Web Service and select Deploy Web Service [Classic] or Deploy Web Service [New] again. The Web service is stopped, the updated predictive experiment is deployed, and the Web service is restarted.
Here’s an example: Suppose your predictive experiment returns the entire row of input data with the predicted result. You may decide that you want the Web service to just return the result. So you can add a Project Columns module in the predictive experiment, right before the output port, to exclude columns other than the result. When you click Deploy Web Service and select Deploy Web Service [Classic] or Deploy Web Service [New] again, the Web service is updated.
You want to retrain the model with new data
If you want to keep your machine learning model, but you would like to retrain it with new data, you have two choices:
- Retrain the model while the Web service is running – If you want to retrain your model while the predictive Web service is running, you can do this by making a couple modifications to the training experiment to make it a retraining experiment, then you can deploy it as a retraining web service. For instructions on how to do this, see Retrain Machine Learning models programmatically.
- Go back to the original training experiment and use different training data to develop your model – Your predictive experiment is linked to the Web service, but the training experiment is not directly linked in this way. If you modify the original training experiment and click Set Up Web Service, it will create a new predictive experiment which, when deployed, will create a new Web service. It doesn’t just update the original Web service.
If you need to modify the training experiment, open it and click Save As to make a copy. This will leave intact the original training experiment, predictive experiment, and Web service. You can now create a new Web service with your changes. Once you’ve deployed the new Web service you can then decide whether to stop the previous Web service or keep it running alongside the new one.
You want to train a different model
If you want to make changes to your original predictive experiment, such as selecting a different machine learning algorithm, trying a different training method, etc., then you need to follow the second procedure described above for retraining your model: open the training experiment, click Save As to make a copy, and then start down the new path of developing your model, creating the predictive experiment, and deploying the web service. This will create a new Web service unrelated to the original one – you can decide which one, or both, to keep running.
Indonesia’s Largest E-Commerce Player Leverages Nutanix AHV Hypervisor to Keep Transactions Seamless for over 2 Million Merchants
SINGAPORE–(BUSINESS WIRE)– Nutanix (NASDAQ: NTNX), a leader in enterprise cloud computing, today announced at its .NEXT on Tour conference in Singapore that Tokopedia has been using the Nutanix Enterprise Cloud Platform to provide the scalability and ease of management required for its rapid expansion in Indonesia.
Tokopedia is Indonesia’s largest e-commerce company with over 2 million merchants and over 60 million dollars in sales transacted monthly. Since the beginning of operations in 2009, Tokopedia has been expanding at a breakneck pace. To capture the opportunities available in Southeast Asia’s largest economy, Tokopedia needed an IT partner in Indonesia that could easily cope with their massive expansion.
Initially, Tokopedia’s legacy monolithic IT systems comprised various disparate systems that ran their core applications, including an online web server for their e-commerce business and various enterprise applications such as MySQL for database management. These traditional systems were increasingly complex to handle as Tokopedia’s online marketplace grew. Tokopedia needed to consolidate its IT infrastructure and find a partner that could seamlessly manage computing, storage and networking requirements while scaling in tandem with their rapid expansion. Given that they are running an e-commerce platform, it was also critical that they could work with a next-generation provider that could offer a simple yet cohesive platform with an “always-on” capability to maintain maximum uptime and efficiency in operations.
Tokopedia turned to Nutanix in July 2016 to manage their computing, storage and networking problems. The solution included Citrix’s Virtual Desktop Infrastructure (VDI) on Nutanix, various enterprise applications, including MySQL and Nutanix AHV Hypervisor to run its core application, the Tokopedia e-commerce platform. In a seamless plug-and-play migration exercise that took only three days, Tokopedia saw a host of benefits including freed up space as 25 servers were cut down to just two Nutanix servers, with five times less power consumption.
For an online marketplace of such massive scale and serving extensive numbers of customers and sellers, it is essential for Tokopedia to have a stable IT infrastructure that can be easily maintained yet highly scalable to meet the ever-changing demands of the industry. With hyperconverged infrastructure as the foundation, the Nutanix Enterprise Cloud Platform is able to provide the support to critical infrastructure to ensure that Tokopedia does not suffer from unnecessary IT hiccups. This will allow the company to focus on boosting its business instead of deploying additional manpower to maintain and troubleshoot disparate multiple systems.
“Tokopedia’s mission is to democratize commerce through technology and we want our users and merchants to have an online retail experience that’s transparent, efficient and secure,” said Infrastructure Head of Tokopedia, Ardimas Wurseto. “Nutanix has been an ideal partner as they have a solution that is perfect for a next-generation company such as ours. Its software-driven architecture makes IT operations and management seamless and simple, thus allowing our teams to focus on boosting business performance rather than on troubleshooting problems caused by complex IT systems.”
“We are thrilled to be working with Tokopedia in making new waves in the e-commerce sector,” said Matt Young, SVP and Head of Asia-Pacific and Japan, Nutanix. “It is a tremendous validation of the Nutanix Enterprise Cloud OS, which offers reliability in providing a suitable platform in a mission-critical service.”
Nutanix makes infrastructure invisible, elevating IT to focus on the applications and services that power their business. The Nutanix Enterprise Cloud Platform leverages web-scale engineering and consumer-grade design to natively converge compute, virtualization and storage into a resilient, software-defined solution with rich machine intelligence. The result is predictable performance, cloud-like infrastructure consumption, robust security, and seamless application mobility for a broad range of enterprise applications. Learn more at www.nutanix.com or follow us on Twitter @nutanix.
© 2017 Nutanix, Inc. All rights reserved. Nutanix, the Enterprise Cloud Platform, AHV and the Nutanix logo are registered trademarks or trademarks of Nutanix, Inc. in the United States and other countries. All other brand names mentioned herein are for identification purposes only and may be the trademarks of their respective holder(s).
Kami dengan sangat senang ingin memberitahukan anda bahwa kami saat ini telah memiliki beberapa training webinar terkait tentang Microsoft. Anda sebagai partner ataupun yang ingin menjadi partner akan memiliki kanal baru untuk dapat berinteraksi secara langsung secara digital. Ini adalah bagian dari komitmen kami dalam transformasi Digital yang semoga memberikan kemudahan bagi seluruh partner Microsoft yang ada di Indonesia.
Dalam training webinar ini anda akan mendapatkan sebuah kemudahan akses setiap minggu mengenai:
– Cara awal berbisnis Cloud dengan Microsoft
– Keuntungan berbisnis dengan Microsoft
– Update terbaru mengenai Cloud Produk Microsoft.
Berikut jadwal lengkapnya:
Dapatkan hadiah unik dari kami untuk anda yang mengikuti training ini dengan syarat dan ketentuan yang berlaku dibawah
How is your network doing these days? Is it able to keep up with the onslaught of mobile workers, requirements for collaborative environments, data intensive multi-media and cloud applications? Do you need to provide more network services with fewer resources every year? Are you interested in integrating your wired and wireless networks? Are you interested in a network that is simpler, more agile, and cost less to operate? If so, keep reading about how the newest member of our mobile-first solution, the Aruba 2930F Switch Series, can help solve those problems.
Designed for customers who want to create digital workplaces optimized for mobile users with integrated wired and wireless access, the 2930F provides:
- Easy deployment and management with advanced security and network management tools like Aruba ClearPass Policy Manager and Aruba AirWave.
- Convenient built-in 1GbE or 10GbE uplinks and models with up to 370 Watts of PoE+ power for 802.11ac APs, cameras, and phones
- Optimized for SDN applications with industry standard OpenFlow
- Simple deployment with Zero Touch Provisioning
- No hidden costs with license-free fully featured software and limited lifetime warranty
More about the 2930F
The Aruba 2930F Switch Series is ideal for enterprise edge, SMB and branch office networks. These basic Layer 3 access switches are easy to deploy, and can be managed with advanced security and network management tools like Aruba ClearPass Policy Manager and Aruba AirWave. A powerful Aruba ProVision ASIC delivers performance and value with support for the latest SDN apps with future proof programmability for tomorrow’s applications. The 2930F supports 10GbE uplinks, PoE+, robust QoS, RIP routing, and IPv6 – with no software licensing required.
The Aruba 2930F Switch Series provides a convenient and cost-effective access switch solution providing quick set up with Zero Touch Deployment and built-in 10GbE uplinks. The robust basic Layer 3 feature set includes a limited lifetime warranty for the original owner.
So what does the F stand for?
The F in 2930F stands for Fixed. Fixed uplinks. Fixed power. It supplements the successful Aruba 2920 Switch Series by providing a solution for customers who do not need the flexibility that the 2920’s modular power, modular uplinks, and backplane stacking provide. Here is an overview of the Aruba campus switch portfolio:
By : gillespie
Dalam mengelola bisnis sudah pasti akan ada banyak sekali permasalahan atau kendala yang dihadapi, salah satunya mengenai pelanggan. Apabila Anda merupakan pebisnis baru, mungkin mengalami kesulitan dalam hal mendapatkan pelanggan baru. Padahal segala upaya telah dijalankan dalam meraup keuntungan dari berbagai peluang usaha. Tunggu dulu, pernahkah Anda kembali mengecek dan introspeksi tentang cara kerja di dalam perusahaan? Bisa jadi terjadi kesalahan dalam kinerjanya atau bahkan produk yang ditawarkan.
Bukan Hanya Produk yang Bagus
Semua pebisnis profesional juga sudah tau benar jika para pelanggan tidak hanya menilai produk yang bagus. Mereka juga menilai bagaimana sebuah perusahaan mampu bekerja dan mengelolanya dengan baik atau tidak. Contohnya saja dalam hal promosi, jika strategi yang Anda gunakan salah atau kurang menarik maka sulit sekali mendapatkan pelanggan baru.
Dalam hal ini masalahnya sangat kompleks karena mendapatkan banyak konsumen menjadi masalah seluruh bagian perusahaan, bukan pada bagian promosi saja. Anda perlu memperhatikan berbagai macam hal mulai dari yang kecil sampai yang besar. Nah, di bawah ini berbagai tips untuk membantu Anda mendapatkan pelanggan baru:
- Evaluasi Ulang
Hal pertama yang wajib Anda lakukan agar bisa menarik pelanggan baru lebih banyak adalah evaluasi ulang. Ini penting sekali untuk melihat ulang kesalahan apa saja yang membuat bisnis Anda gagal.
- Ajak Meeting Klien dan Rekan Bisnis
Jangan lupakan untuk komunikasi dan konsultasi dengan klien serta para rekan bisnis Anda. Cara ini sangat membantu sekali untuk menemukan solusi tepat akan masalah yang dialami perusahaan. Agar meeting berjalan lancar maka Anda bisa gunakan berbagai macam fitur pada Microsoft 365 agar lebih mudah dan cepat. Misalnya Outlook Calendar untuk membuat agenda baru dan bisa langsung dibagikan pada peserta meeting. Kemudian ada Skype dimana sangat membantu sekali jika Anda tiba-tiba tidak bisa menghadiri rapat karena urusan penting. Anda bisa melakukan meeting kapanpun dimanapun serta bertatap muka secara langsung dengan Skype. Bahkan fitur ini bisa dibuka pada Word sehingga semakin memudahkan Anda.
- Menyusun Rencana Baru
Jika masalah sudah ketemu maka Anda harus menyusun rencana bisnis yang baru. Fitur Microsoft Office 365 yang dapat digunakan misalnya Word, Power Point, Exel, Share Point Online dan sebagainya. Anda juga bisa mencatat hal-hal penting pada fitur One Note dan membagikannya pada rekan bisnis secara langsung lewat email.
- Presentasi Produk Menarik
Jangan lupakan tentang presentasi yang menarik karena hal ini akan membuat pelanggan baru tertarik. Dalam Power Point di Microsoft Office 365 terdapat fitur baru yang bisa mempercantik tampilannya seperti Designer, Morf, dan Zoom.
Cloud Productivity and Voice Services for Your Mobile Workforce
The adoption of unified communication (UC) applications like Microsoft Office 365 is growing with incredible speed. Ideal for both enterprise environments and small to midsized businesses (SMBs), Office 365 can help drive business productivity, employee collaboration, and process innovations.
With Office 365 your employees can take their office communications solutions with them wherever they go. They can fully engage colleagues and customers—conduct Skype for Business meetings, share PowerPoint presentations, make voice and video calls—from home, office, or the road.
for more Information Please Download the Attachment bellow: