360DT

So many Python libraries exist that offer powerful and efficient foundations for supporting your data science work and machine learning model development.

While the list may seem overwhelming, there are certain libraries you should focus your time on, as they are some of the most commonly used today. They are:

- Pandas
- NumPy
- Scikit-learn
- Gradio
- TensorFlow
- Keras
- SciPy
- StatsModels
- Plotly
- Seaborn

Hello, connections!

The pandemic has impacted our lives in a positive as well as in a negative way and surprisingly people have started to adapt to the situation.
But there are still some things that need to be figured out in order to smoothen the journey ahead.

We would like you to kindly participate in a short 5 minutes research study that we are conducting to gather information to know about your pain points while opting for a course online.
https://lnkd.in/gXmYjtM

We can guarantee it wouldn't take much of your time.

Waiting for your response.
Thanks in advance.

Survey by 360DT

Business profitability and sustainability are powerful reasons to invest in infrastructure efficiency, but it is easy to feel lost about how to actually reduce costs. A foundation of robust and actionable data is essential for a successful efficiency program.

Here are some suggestions from our experience that ensured the Airbnb CUR was robust and accurate:
- Design with downstream use cases in mind.
- Build for retroactive adjustments.
- Study the options for obtaining raw data.

Developing a trustworthy and interpretable cost data foundation set Airbnb up for long-term success in cloud cost management. But data alone is not enough to achieve cost savings. Leadership commitment to savings goals, effective program management, contract management, and technical excellence across Airbnb made the success of the program possible.

Read the whole article to know how Airbnb made it successful.

Achieving Insights and Savings with Cost Data The path to cloud efficiency begins with a cost data foundation

Oracle on Wednesday announced that it's making GoldenGate, its popular real-time data fabric, available as a cloud-native service that's highly automated and fully managed.

The intent is to bring the service to a wider market -- both by making it more affordable and more accessible for non-technical users.

Oracle's GoldenGate technology is a set of platform components that have helped organizations with data replication for more than 10 years. Oracle already has thousands of GoldenGate customers, including more than 80 percent of the Fortune 100.

In its new form, customers don't have to worry about extracting data, transforming it to fit database cloud services, or loading it into database cloud services.

Moreover, the service also takes care of analyzing the data in real-time as it's being replicated, and it delivers continuous business continuity while data is being replicated.

All told, it simplifies the process of moving data between different database types, vendors, and products.

Know more about the GoldenGate.

Oracle makes GoldenGate a cloud-native, fully-managed service | ZDNet The general purpose real-time data fabric platform is designed to deliver data integration for both operational databases and analytic data stores

The shift to cloud-native environments away from traditional data center infrastructures continues unabated, but security and complexity challenges remain a struggle for DevOps teams.
These were two of the main takeaways in a survey and analyst report published by the IT consultancy firm Flexera.

The survey, based on the responses of 750 IT professionals, revealed that 92% of the organizations represented in the survey have a multi-cloud strategy, 80% have a hybrid cloud strategy and only a minority (45%) have integrated data between cloud environments.

The top challenges, according to the survey results, are:
- security (81%)
- managing cloud spending (79%)
- governance, lack of resources/expertise, and compliance (75%)
- comprehending cost implications of software licenses (55%).

Read the whole blog. Click on the link below.
https://lnkd.in/eAnBXx6

Solving Cloud-Native Challenges in the Rush to the Cloud - DevOps.com The shift to cloud-native environments continues unabated, but security and complexity challenges remain a struggle for DevOps teams.

Researchers in Iran have used a deep neural network (DNN) to extract the foetal electrocardiogram (ECG) from a single abdominal ECG channel. Their method, described in Physiological Measurement, may improve foetal monitoring in the future.

The challenge? It’s difficult to isolate the foetal ECG signal from abdominal ECG because the latter contains signals from both the foetus (“foetal ECG”) and the mother (“maternal ECG”), as well as from sources of interference, such as muscle contractions. This task becomes even more demanding toward the end of pregnancy when the amplitude of the foetal ECG signal is comparable to that of the maternal ECG.

Limitations of their method include a potential overreliance on the training dataset, especially when the foetal ECG signal is weak, and the potential for errors to be propagated to the second sub-network from the first.

https://lnkd.in/gq7qUbX

Deep learning teases apart abdominal ECG signals – Physics World A deep neural network can accurately extract the foetal signal from the abdominal electrocardiogram of the mother

Google has worked with the team over Carnegie Mellon University's Create Lab to bring timelapse videos to Google Earth, which the tech giant says is the biggest update the company has made to the platform since 2017.

According to Rebecca Moore, Earth Engine and Outreach director of Google Earth, Timelapse has been developed using 24 million satellite images from the past 37 years from 1984 to 2020 before being compiled into an interactive 4D experience.

To play its part in helping the environment, Google made a commitment last September to operate with carbon-free energy across its businesses worldwide by 2030.

https://lnkd.in/gXb_dz3

Google introduces Timelapse to Google Earth | ZDNet Google says Google Earth is getting its biggest update since 2017.

Raspberry Pi 4 owners can now train their own custom models using Edge Impulse's cloud-based development platform for machine learning on edge devices.

Raspberry Pi is a capable little machine, but if you're interested in developing your own embedded machine-learning applications, training custom models on the platform has historically been tricky due to the Pi's limited processing power.

https://lnkd.in/dV4N3-m

Machine learning on Raspberry Pi just took a big step forward Raspberry Pi 4 owners can now train their own custom models using Edge Impulse's cloud-based development platform for machine learning on edge devices.

VMware has addressed a critical vulnerability in the VMware Carbon Black Cloud Workload appliance that could allow attackers to bypass authentication after exploiting vulnerable servers.

VMware Carbon Black Cloud Workload is a Linux data center security software designed to protect workloads running in virtualized environments.

It also bundles endpoint protection capabilities, including endpoint detection and response (EDR), next-gen antivirus, and real-time threat hunting.

This security vulnerability impacts VMware Carbon Black Cloud Workload appliance version 1.0.1 and earlier.

Know more.
https://lnkd.in/dvXPSai

VMware fixes authentication bypass in data center security software VMware has addressed a critical vulnerability in the VMware Carbon Black Cloud Workload appliance that could allow attackers to bypass authentication after exploiting vulnerable servers.

Trifacta Launches Industry First Data Engineering Cloud

Trifacta recently announced the release of Trifacta Data Engineering Cloud, a major expansion of its visual data preparation platform.

The product is headlined by multi-cloud support with solutions that are optimized for each platform. Users can choose between ETL or ELT, or an optimal combination of the two based on cost.

Trifacta Data Engineering Cloud also touts universal connectivity to more than 180 enterprise data sources both on-prem and in the cloud. Trifacta’s cloud integrates with all toolchains as well.

In a media statement about the news, Trifacta CEO Adam Wilson said “Accelerating data preparation and democratizing ETL for these users and their cloud data warehousing projects requires an enterprise-grade data engineering platform that is open, intelligent, and self-service. We’ve created the Trifacta Data Engineering Cloud to meet these needs.”

https://lnkd.in/gqtz6EJ

Trifacta Launches Industry First Data Engineering Cloud Trifacta recently announced the release of Trifacta Data Engineering Cloud, a major expansion of its visual data preparation platform. The product is headlined by multi-cloud support with solutions…

Agility is about releasing value, but in some circles, a misunderstanding of a ‘people-centric focus can unintentionally undermine that principle. Well-meaning leaders new to are sometimes coached to put all their efforts into creature comforts for their people whilst forgetting the primary purpose of the firm – to relentlessly deliver value to their customers and clients.

Agile frameworks are all about getting things done, releasing value, early and often, getting that delivery out the door and into the hands of our customers.
Everything about is about eliminating and overcoming any friction or challenge that prevents delivery from happening.

But Agile frameworks are not an excuse to take on unrealistic commitments after all the Agile Manifesto has a basic principle of patterns of work.
Agile teams, therefore, need to be organized and structured for teams to deliver maximum , , , and .

www.linkedin.com

Next-generation ADAS and autonomous driving (AD) systems, when deployed to market, will require accurate and high-speed recognition, judgment, and operation.

Convolutional neural networks (CNNs) require large amounts of computation for pattern recognition. As the number of sensors installed increases, higher CNN performance is required. However, as power consumption increases in proportion to performance, a heavy and expensive water-cooling system is needed.

It is required to achieve both high deep learning performance and low power consumption that enables a lightweight and cost-effective air-cooling system. Achieving a CNN performance of 60TOPS with an efficiency of 10TOPS/W per one LSI device is the optimal target from a practical point of view.


Read the full article.

High Reliable and Performance Deep Learning Accelerator for ADAS and Autonomous Driving Systems - ELE Times autonomous driving (AD) systems, when deployed to market, will require accurate and highspeed recognition, judgment, and operation.