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By Raghav K.

Almost every IT organization is making a shift to embrace DevOps. The opportunity of a considerable speed increase to the software development lifecycle and greater business agility is hard to pass. Streamlined and accelerated interactions between development and operations are simple when defined but a more complex structure when implemented.


Machine learning is the study of computer algorithms that can be automatically improved through experience. Machine learning uses big data or past experience to optimize the performance standards of computer programs.

What Is Machine Learning?

Machine Learning (ML) in simple terms can be defined as the science of getting computers to act and learn without explicit programming to perform those actions. It has become quite popular in recent years, however, the term itself was coined in 1959 by Arthur Samuel who defined Machine Learning as ‘the field of study that gives computers the ability to learn without being explicitly taught’.

A more recent and formal definition of Machine Learning was created by Tom Mitchell and describes it as a well-defined learning problem — ‘A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.’ …


As cliché as it may sound, 2020 has indeed been a challenging year for all of us, perhaps the most difficult year yet. Looking back, we’ve done well to overcome unprecedented challenges through innovative ideas, including maintaining our position as the third largest global provider for IaaS for the third consecutive year, hosting our very first fully-digital Apsara Conference, and the wide adoption of cloud native technologies for this year’s Double 11. We’ve weathered the storm of 2020, and we can only expect the best for 2021.

In this blog, we’d like to share some of our biggest achievements and innovations this year amid the “new normal”. We’d also like to take this opportunity to thank all of you for your incessant support in this eventful year. …


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Still struggling with DDoS attacks? Contact us today to get FREE emergency support!

Still struggling with DDoS attacks? Contact us today to get FREE emergency support!

As 2020 draws to a close, it’s critical to note how the global cybersecurity landscape dramatically changed this year amid the COVID-19 outbreak. Cybersecurity threats dominated headlines in 2020 as the global business world witnessed record-breaking cyber attacks across online platforms and services. The cybersecurity threats such as Distributed Denial-of-Service (DDoS) are on the rise across industries disrupting businesses of all sizes worldwide. According to the 2020 Threat Intelligence Report by NetScout, more than 4.83 million DDoS attacks occurred in the first half of 2020. …


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Step up the digitalization of your business with Alibaba Cloud 2020 Double 11 Big Sale! Get new user coupons and explore over 16 free trials, 30+ bestselling products, and 6+ solutions for all your needs!

By Alibaba Cloud Serverless

The COVID-19 pandemic has reshaped the digital lifestyle. With today’s aggressive digital transformation and increased efficiency, Serverless is poised to become the new computing paradigm for the cloud, freeing developers from heavy manual resource management and performance optimization and sparking a new revolution in cloud productivity.

However, Serverless is often tricky to implement. Migrating legacy projects to Serverless while ensuring business continuity during the migration process, providing comprehensive development tools and effective debugging and diagnostic tools under the Serverless architecture, and leveraging Serverless for better cost savings are all constant challenges. This is especially true when it comes to the large-scale implementation of Serverless in core scenarios. As a result, enterprises need best practices for large-scale application of Serverless in core scenarios. …


Step up the digitalization of your business with Alibaba Cloud 2020 Double 11 Big Sale! Get new user coupons and explore over 16 free trials, 30+ bestselling products, and 6+ solutions for all your needs!

By Hologres, with Zhang Zhaoliang (Shiheng), Senior Technical Expert of the Alibaba Search Division

Background

The real-time data warehouses of the Alibaba Search and Recommendation Data Warehouse Platform support multiple e-commerce businesses, such as Taobao (Alibaba Group), Taobao Special Edition (Taobao C2M), and Eleme. Real-time data warehouses also support data applications, such as real-time dashboards, real-time reports, real-time algorithm training, and real-time A/B test dashboards.

Value of Data

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We believe that data is the brainpower of the Alibaba Search and Recommendation System. This power is reflected in many areas, such as algorithm iteration, product operations, and decision-making. Therefore, it is important to understand how data flows in search and recommendation business scenarios. First, information is collected. When you use the search and recommendation feature of Taobao Mobile, tracking information on the server is triggered. Second, the collected information is processed by using offline and real-time extract, transform, load (ETL), and then loaded into the product engine. Third, we build an analysis system based on the engine to facilitate analysis and decision-making for algorithms and products. Fourth, after each decision is made, new content is produced and you see the business forms generated by the algorithm model. This results in a new round of data collection, processing, loading, and analysis. In this way, data is used to form a complete business process, in which each phase is very important. …


Step up the digitalization of your business with Alibaba Cloud 2020 Double 11 Big Sale! Get new user coupons and explore over 16 free trials, 30+ bestselling products, and 6+ solutions for all your needs!

By Alibaba Developer

Tmall has broken two records during the 2020 Double 11 Global Shopping Festival for consumption in GMV (US$74.1 billion) and peak orders per second (583,000). Alibaba Cloud has once again handled the world’s largest traffic peaks without any major issues. How did our technology support the entire event, providing a smooth experience for nearly one billion shoppers around the world?

Recently, Alibaba held the Technical Communication Meeting for Double 11. During the meeting, Ding Yu, Researcher of Alibaba Cloud and the Head of the Cloud-Native Application Platform of Alibaba Cloud, said, “This year, Alibaba Cloud has achieved major technical breakthroughs in the comprehensive cloud-native of the core system to implement major improvements in resource efficiency, R&D efficiency, and delivery efficiency. The resource cost of every 10,000 transactions has been reduced by 80% in four years. The R&D and O&M efficiency have been increased by more than 10% on average, and the delivery efficiency of scale applications has been improved by 100%. This means that Alibaba has successfully completed a comprehensive deployment of cloud-native technology during the 2020 Double 11 Global Shopping Festival.”


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Step up the digitalization of your business with Alibaba Cloud 2020 Double 11 Big Sale! Get new user coupons and explore over 16 free trials, 30+ bestselling products, and 6+ solutions for all your needs!

At the stroke of midnight on this year’s Singles’ Day, online shoppers in China and across Asia started placing orders on Alibaba’s e-commerce sites that saw peak orders hit a whopping 583,000 per second.

This was 1,400 times the peak volume of the inaugural event 12 years ago on 11 November 2009. To cope with the record number of orders, which amounted to $74.1bn …


In order to reduce the cost of computing power for front-end engineers using Pipcook, we supported the use of Pipcook training models on Google Colab in August.

Google Colab is a Juypter Notebook service provided by Google, which can use free GPU/TPU resources.

Let’s start the Step by Step of this article!

Create a new Google Colab on Google Drive. Opening the link will create a new *.ipynb file on your Google Drive.

Next is environment preparation. Just execute the following code in the code block in Notebook:

!wget -P /tmp https://nodejs.org/dist/v12.18.1/node-v12.18.1-linux-x64.tar.xz
!rm -rf /usr/local/lib/nodejs
!mkdir -p /usr/local/lib/nodejs
!tar -xJf /tmp/node-v12.18.1-linux-x64.tar.xz -C /usr/local/lib/nodejs
!sh -c 'echo "export PATH=/usr/local/lib/nodejs/node-v12.18.1-linux-x64/bin:\$PATH" >> /etc/profile'
!rm -f /usr/bin/node
!rm -f /usr/bin/npm
!ln -s /usr/local/lib/nodejs/node-v12.18.1-linux-x64/bin/node /usr/bin/node
!ln -s /usr/local/lib/nodejs/node-v12.18.1-linux-x64/bin/npm /usr/bin/npm
!npm config delete registry
import os
PATH_ENV = os.environ['PATH'] …


In the data era, business data is the core asset for enterprises. Customers in various industries are constantly seeking for more powerful and fine-grained database backup and recovery capabilities to decrease commercial risks caused by data loss and business logic errors. For example, in the gaming industry, a large number of customers demand for the “game correction” capability to deal with the risk of misoperations or failures.

In 2020, an employee of a listed company accidentally deleted the company’s business-critical database. As a result, the market value of the company was severely affected. Conventional scheduled or manual backups of data are not ideal solutions for the preceding incidents because data backup and “Black Swan” incidents (abnormal incidents that are hard to predict) happen unpredictably. To prevent the incidents above, the ideal solution is to use second-level granularity for backing up data. …

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