Bigdata on Google Cloud Platform and Machine Learning Fundamentals 2017-11-09T14:24:55+00:00

Bigdata on Google Cloud Platform

Bigdata on Google Cloud Platform & Machine Learning Fundamentals

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Bigdata on Google Cloud Platform and Machine Learning Fundamentals (CPB100)

Bigdata on Google Cloud Platform and Machine Learning Fundamentals (CPB100)

Objective of this Bigdata on Google Cloud Platform & Machine Learning Fundamentals:-

The objective of this course is to provide an introduction to Google Cloud Platform Big Data & Machine Learning Fundamentals.  This course focuses on fundamental theory. You will receive a full training and Google Cloud Platform Big Data & Machine Learning Fundamentals. This course guarantees you that you will receive all tools end theory needed from experts in the field.

This course takes students through the fundamentals, giving them a solid foundation that they can build upon, then moves on to more advanced knowledge, teaching them how they can apply Google Cloud Platform Big Data & Machine Learning Fundamentals in practical situations.

TRAINING METHODOLOGY

In Class: $4,999
Locations: NEW YORK CITY, D.C, BAY AREA.
Next Session: 25th Nov 2017

Online: $2,999
Next Session: On Demand

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Home / All courses /  Cloud Technologies /Google Cloud Platform Big Data & Machine Learning Fundamentals (CPB100)

Google Cloud Platform Big Data & Machine Learning Fundamentals (CPB100)

Instructor: John Doe, Lamar George

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Bigdata on Google Cloud Platform and Machine Learning Fundamentals (CPB100)

Bigdata on Google Cloud Platform and Machine Learning Fundamentals (CPB100)

Nowadays, every business is planning or executing a major digital transformation involving new business models, new technologies, and new processes. Indeed, there are several essential ingredients for a successful digital transformation. Data is one of them, which is a critical foundation for every successful digital transformation.

In the world of data, we are experiencing not just the usual level of change (which is very important). We are in the midst of a generational market disruption that we call Data 3.0(which is very important). The sheer amount of data available for decision-making can overwhelm the human mind (and it is very important in these days). Not only is it hard to keep up with the data, we can’t even keep up with the questions we should be asking (specially for the IT department). Which is why, in addition to expertise and processes around data, we need Machine learning google cloud platform.

Even the simplest questions are hard to answer for the IT deparment.

When it comes to building the data foundation for digital transformation, there are many simple questions that most companies struggle to answer (for the IT deparment).

The growth of Cloud and Big Data platforms has made these questions even harder to answer(for the IT deparment). These platforms are providing powerful data processing capabilities to virtually every employee at relatively low cost(which is very important for many companies). And employees are embracing these capabilities rapidly(in these days).

This is where Machine learning google cloud platform comes in. It’s not the pursuit of a perfect answer (which is very important to answer). The best the IT deparment can find today, further refined with new data tomorrow. Think search engine queries and Amazon’s “people who bought that also bought this” recommendations (which are very important for Amazon). Are those outputs ever necessarily “perfect”? No, but they’re good enough to drive the business forward, which is the point (and it is needed).

Our approach of Machine learning google cloud platform, to connecting data at a machine scale with something of a human “intuition,” is CLAIRE™. The CLAIRE engine delivers unified metadata intelligence across all of your data stores (which is very important). The key is to use Machine learning google cloud platform to metadata. Metadata describes the structure, attributes, logical and physical locations, relationships, lineage, profile, and quality of the underlying data, which is very useful in machine learning. The underlying data can be on premise in traditional data stores or big data platforms, or in cloud-based applications or data repositories (Machine learning google cloud platform). Using machine learning and metadata (Machine learning google cloud platform), it is found the signal in the noise more quickly, and start answering the questions it was discussed above. We make the CLAIRE technology available to customers through the Enterprise Information Catalog, which is very useful.

Using Machine learning google cloud platform to your business data

First, developers must get started within 1-2 business areas. Many of the customers start implementing their Enterprise Information Catalog in the context of a data-driven initiative like GDPR or predictive analytics or cloud migration (which is so useful). Other customers do it in the context of an efficiency or operational simplification initiative (which is so useful too).

Once the IT team learns how to use this Machine learning google cloud platform, it will spread very quickly. While enabling a company to build a data foundation for digital transformation, it will also enable you to establish control over your data in this world of Data 3.0, which is very important.

Chinese tech and gaming giant Tencent Holdings Ltd. is ready to invest heavily in emerging technologies, such as Bigdata on Google Cloud Platform, founder Pony Ma said in a speech to Tsinghua University’s School of Economics and Management.

Pony Ma feels that he is ready to invest in several basic fields such as AI, cloud computing, and Big Data (Bigdata on Google Cloud Platform).

All future technologies (like Bigdata on Google Cloud Platform) will be inseparable from these three areas.

Ma also believes that with deep algorithms, artificial intelligence can play a major role in medicine, which is very important. Ma’s company recently released Tencent Miying, which is an AI technology that reads medical images to help doctors screen cancers and detect and treat diseases early and accurately.

Artificial intelligence (AI) can also be applied to finance, robotics and other fields as well as every aspect of commerce and daily life.

Tencent is looking for opportunities in business segments where AI and information technologies can be used (such as Bigdata on Google Cloud Platform)

“It’s like electricity,” the Tencent founder said. “It’s impossible for you to build your own power plant at home and not use the public power grid. That’s completely unfeasible.”

Ma said that (in the future) all firms will use AI technology to handle Big Data in the cloud (Bigdata on Google Cloud Platform). Anything relating to cloud and artificial intelligence will involve Big Data (Bigdata on Google Cloud Platform). This is, without doubt, the future direction (Bigdata on Google Cloud Platform).

Many products and investment directions such as Bigdata on Google Cloud Platform is something that he is looking at.

Bigdata on Google Cloud Platform is the technology that every company wants to have for having more benefits. This Bigdata on Google Cloud Platform technology (Bigdata and Google Cloud) is something that will change the IT technology. Ma and his company are using about these technologies and they expect to have big advances in the future.

Google has today announced its Cloud Job Discovery (Machine learning google cloud platform) service has now entered a beta stage of deployment with Google confirming that Cloud Job Discover (Machine learning google cloud platform) is now available to staffing agencies and applicant tracking systems, in addition to the career sites and job boards that already had access to the service. Another feature that comes with the beta stage is that Cloud Job Discovery (Machine learning google cloud platform) now supports more than 100 language variations. Machine learning google cloud platform will likely be of particular importance to its development for crossing international job market borders.

Cloud Job Discover (Machine learning google cloud platform) is unlikely to be something that the average consumer is aware of, although it is something they will likely make use of in the future when looking for a new job. This was formerly known as the Cloud Jobs API (Machine learning google cloud platform) and is actually part of the underlying technology that powers ‘Google for Jobs’. An initiative launched by Google back in June to better help job seekers and job vacancies find each other. Cloud Job Discover (Machine learning google cloud platform) is able to achieve this through the use of machine learning which not only looks to translate (parse) job vacancy information from one language to another with ease, but just as easily translate (parse) the same information to be more relevant (and more likely to be found) by those within the same industry. Google is using Machine learning google cloud platform to change the way in which jobs are filled. Google has been embarking on various projects lately which look to improve the employment landscape for all those involved.

Speaking of which and as part of today’s announcement on the beta status, Google also explained that since the launch of Cloud Job Discovery (Machine learning google cloud platform) back in November of 2016, it has made its way to more than 3000 job properties. While also drawing on sentiments on how the service has improved the job recruiting process for companies like Johnson & Johnson, who in turn use Jibe (an early access Cloud Job Discovery customer) to find suitable candidates.

Machine learning platforms from vendors like Amazon, Google, IBM, Microsoft, and others can automate business processes on a previously impossible scale and free up employees for more creative, intensive work. This Machine learning google cloud platform also requires a lot more commitment and, sometimes, coaxing than parking an Amazon Echo on a kitchen table or tapping a button to have Google saving the photos on your phone.

Machine learning google cloud platform let companies perform some of the same tasks AI tackles in consumer settings, just in larger numbers and with money on the line. For example, the Machine learning google cloud platform opened for business last year provides image-recognition services—not too different from what Google Photos does for your phone’s pictures. Machine learning google cloud platform allows Airbus to correct satellite imagery to distinguish between snow and clouds.

Machine learning google cloud platform can also take on tasks that individual users would rarely bother with, but for which companies might pay a great deal. Consider the job of grading the visibility of sponsorship signs and banners in a sports event.

SAP’s Leonardo Machine Learning Foundation can do that a faster. They are able to have a look at every frame, we’re able to have a look at every pixel of HD or 4K video, and we’re able to process it faster than real time.

Customer service represents another obvious application of machine learning google cloud platform’s ability to parse human input. For example: Siri, but at scale and self-improving.

Deloitte Consulting helped an unnamed financial-services firm deploy an AI-based  system (Machine learning google cloud platform) that handles some 27,000 customer queries an hour in over a dozen languages.

Deloitte said that it’s smarter than a chatbot and it’s like a learning set that’s actually doing back-end analytics.

A strong case can be made for companies to outsource machine learning google cloud platform’s services to companies that specialize in them, but not just one company.

There’s definitely some risk in building big stuff based on a single Machine learning google cloud platform, but that’s still far preferable for most companies to trying to build their own Machine learning google cloud platform’s capabilities.

For example, Box first signed up with Google to use its Machine learning google cloud platform to automate image recognition. More recently, though, it announced plans to bring Microsoft’s Azure Machine Learning Platform onboard for other, not yet specified AI services, which means that they prefer to use Microsft Azure machine learning platform than Machine learning google cloud platform.

Nowadays, there are billions of files in Box company, and many of those files are image files. Box looked at what problem they could solve first with Machine learning, it made sense to start with providing an image recognition service through our partnership with Google Cloud, which means that they want to use Machine learning google cloud platform.

Google’s image-recognition services (Machine learning google cloud platform) are free in the current private beta, but won’t be when they ship later this year. Box will reveal pricing closer to then.

Google is seeing retail customers using image recognition (Machine learning google cloud platform) in Box to optimize digital asset management of product photos, a major media company is using this technology to automatically tag massive amounts of inbound photos from freelance photographers around the globe, and a global real estate firm is leveraging optical character recognition in Box to digitize workflows for paper-based leases and agreements.

In some scenarios, it makes more sense to keep AI in-house (Machine learning google cloud platform). PayPal, for instance, opted to build the fraud-detection AI (Machine learning google cloud platform) it rolled out in 2013.

Machine learning google cloud platform could be used as a generative model for images, it was time to really solidify the end-to-end system, the main goal of this project.

There are some steps to do it:

Preprocessing: a directory of images is converted into TFRecords and split into evaluation and test datasets. These are stored in the user’s Cloud Storage bucket (Machine learning google cloud platform).

Training Job: The TensorFlow code is packaged and uploaded to the user’s cloud (Machine learning google cloud platform). The model is trained using Machine learning google cloud platform (GPUs/CPUs/RAM specified in config file) with the checkpoints and final SavedModel being saved to GCS.

Create and Deploy Model: a model is created and the SavedModel code is then deployed onto the (Machine learning google cloud platform).

Prediction (Generation) Jobs: the prediction API is used to access the trained model hosted on Machine learning google cloud platform. For the second mode, an input image is supplied, with an embedding acting as output. In this Machine learning google cloud platform’s project was used an App Engine project to provide a sample interface for the user to generate images from two trained models.

To get the tool up and running on Machine learning google cloud platform, first the Cloud environment has to be set up. A Machine learning google cloud platform project has to be set up on the projects page, billing has to be setup, and then the Machine learning google cloud platform and Compute engine APIs have to be enabled. To use the command line interface, the Cloud SDK must be installed. Follow these instructions to set up the cloud environment.

The user can begin running training jobs. Using a script that allows the user to specify an image directory and then takes care of preprocessing the images and starting the training job on Machine learning google cloud platform. Other flags allow the user to further tune their training/preprocessing tasks such as center-cropping the images or which port to start their TensorBoard instance (TensorBoard: the greatest way to monitor any TensorFlow training).

Another script it was created to allow users to create and deploy their models they created from running training jobs on Machine learning google cloud platform. Once on Machine learning google cloud platform, getting generated images or image embeddings is one API call away.

There is a beta Google Cloud Dataprep (Machine learning google cloud platform), an intelligent, fully-managed cloud service (built in collaboration with Trifacta) that visually explores, cleans and prepares structured and unstructured data for analysis or training machine-learning models.

Cloud Dataprep features include a visual experience that makes data preparation intuitive and approachable for analysts who want to modify or enrich their datasets directly, without needing to rely on data engineers (Machine learning google cloud platform). Cloud Dataprep (Machine learning google cloud platform) runs on serverless infrastructure that handles scalability, performance, availability and security.

Cloud Dataprep (which is like a Machine learning google cloud platform) also has intelligence built-in for understanding and automatically operationalizing your particular usage patterns, making data preparation even faster and less prone to user error. The overall result (of using Machine learning google cloud platform) is more productive, efficient and powerful data analytics pipelines, leading to faster time-to-insight.

Merkle(Bigdata on Google Cloud Platform) is used to analyze new datasets for better customer relationships. A performance marketing agency specializing in data-based marketing solutions, Merkle Inc. (Bigdata on Google Cloud Platform) helps its clients maximize their most profitable customer relationships through a framework it calls Connected CRM. Merkle (Bigdata on Google Cloud Platform) relies on Google Cloud Datastore and Google BigQuery, with Cloud Dataprep bringing new data into BigQuery for analysis.

For Merkle(Bigdata on Google Cloud Platform), Cloud Dataprep offers a better solution for rapid data ingestion than other tools and techniques.

Cloud Dataprep(Bigdata on Google Cloud Platform) allows us to quickly view and understand new datasets, and its flexibility supports our data transformation needs. The GUI (of this Bigdata on Google Cloud Platform) is nicely designed, so the learning curve is minimal. Our initial data preparation work is now completed in minutes, not hours or days.

Venture Development Center (Bigdata on Google Cloud Platform) is used to deliver Big Data strategies with GCP, Cloud Dataprep and BigQuery (Bigdata on Google Cloud Platform).

Venture Development Center (VDC) LLC is an advisory services company that helps its clients define, identify and implement big data use cases that can lead to business transformation and data monetization, which is like a Bigdata on Google Cloud Platform.

Cloud Dataprep(Bigdata on Google Cloud Platform) and BigQuery(Bigdata on Google Cloud Platform) are key ingredients in its platform for delivering those services.

VDC needed a platform (Bigdata on Google Cloud Platform) that was versatile, easy to utilize and provided a migration path as our needs for data review, evaluation, hygiene, interlinking and analysis advanced.

VDC immediately knew that Google Cloud Platform, with Cloud Dataprep(Bigdata on Google Cloud Platform) and BigQuery (Bigdata on Google Cloud Platform), were exactly what we were looking for.

Cloud Dataprep(Bigdata on Google Cloud Platform) allows us to accomplish this quickly and adeptly.

Cloud Dataprep(Bigdata on Google Cloud Platform) integrates with other GCP services (e.g., Cloud Storage, Google BigQuery, Cloud Dataflow, Cloud Machine Learning Engine)

The world of connected devices has enormous potential, but it is clear that people need distributed edge cloud(using Bigdata on Google Cloud Platform) to do it. Developers (using Bigdata on Google Cloud Platform) can leverage edge cloud to write their applications and accelerate the growth of IoT.

Using Bigdata on Google Cloud Platform and Internet of Things (IoT), our devices constantly connect and communicate mostly through a central cloud and have transformed many aspects of our daily lives (using Bigdata on Google Cloud Platform). IoT (using Bigdata on Google Cloud Platform) is undoubtedly a transformative technology and that impacts most industries, and the way businesses and consumers behave. Some projections state that there will be more than 50 billion connected devices (using Bigdata on Google Cloud Platform) and as many as 22 billion IoT devices operating by 2021.

A disadvantage is that there is not enough bandwidth to cope with the explosion of the data at the edge of the network today. 5G will not address the problem as it is targeting an order of magnitude increase in capacity and will not provide the multiple orders of magnitude in additional network capacity required. For example, a single autonomous vehicle (using Bigdata on Google Cloud Platform) will generate more data than the capacity of an entire 5G base station.

With IoT(using Bigdata on Google Cloud Platform), everything from our home appliances to potentially our clothing will become nodes on the internet. Beyond the data explosion problem (Bigdata on Google Cloud Platform), this will undoubtedly create challenges to our privacy and personal freedom. Connectivity results (of using Bigdata on Google Cloud Platform) in potential exposure: more of our personal data and our activities will be visible. Bigdata on Google Cloud Platform will increase the value of our personal data, and advents in big data analytics will enable “cloud companies” (using Bigdata on Google Cloud Platform) to derive significantly more value from our personal data. In other words, using Bigdata on Google Cloud Platform will potentially be even more disenfranchised unless we take control of our personal data.

Bigdata on Google Cloud Platform can enable any digital device to become a cloud server. Using Bigdata on Google Cloud Platform, the data can be processed at the periphery of the network on originating devices (edge nodes) or other devices in proximity, taking away the absolute control from centralized nodes and extending it to the edge (using Bigdata on Google Cloud Platform). This will not only restore control to the producer and owners of the data, but will also save our networks from choking due to the massive explosion of data.

Bigdata on Google Cloud Platform is starting a new era of decentralized computing and a wide range of new applications across all industries and verticals. When we turn any device to an edge cloud server (using Bigdata on Google Cloud Platform) we solve many challenges of central cloud computing (Bigdata on Google Cloud Platform) such asbandwidth, latency, date centre efficiency and cost. Bigdata on Google Cloud Platform restores greater control and power back to the producers and owners of the data.

Amazon Echo and Google Home illustrate the challenge of central cloud computing (Bigdata on Google Cloud Platform) and the trend by central cloud providers to build new edge devices to offload some central cloud processing to the edge (Bigdata on Google Cloud Platform). It is needed to turn any computing device into a cloud server (using Bigdata on Google Cloud Platform). This is the most effective way to scale and turn the vision of IoT from hype to reality (using Bigdata on Google Cloud Platform).

Using Bigdata on Google Cloud Platform, we allow devices and applications to form ad hoc clusters that can collaborate and bootstrap resources as needed (using Bigdata on Google Cloud Platform). Bigdata on Google Cloud Platform is an effective way to extend central cloud capabilities to the edge to minimize the cost and the burden on bandwidth and latency to support billion of devices (using Bigdata on Google Cloud Platform). It will minimize signaling and bearer bandwidth. It will enable peer-to-peer connectivity and micro service discovery, optimizing inter-node access time to address stringent latency requirements.

The world of connected devices has enormous potential, but it is clear that we need distributed edge cloud to do it (using Bigdata on Google Cloud Platform). Developers (which are using Bigdata on Google Cloud Platform) can leverage edge cloud to write their applications and accelerate the growth of IoT and turn it from hype to reality (using Bigdata on Google Cloud Platform). The question is: are developers (which are using Bigdata on Google Cloud Platform) are given the tools to design and create new applications.

Thanks to Moore’s law in computing and bandwidth which has enabled massive computing power and great connectivity at the edge nodes, the time has come to extend the cloud to the edge (using Bigdata on Google Cloud Platform).  Bigdata on Google Cloud Platform will have even a more transformative impact than central cloud computing. Bigdata on Google Cloud Platform is not a replacement for central cloud computing. In fact, it will transform central cloud into a massively more powerful entity. This is not a vision for the future and can be done today. Bigdata on Google Cloud Platform will accelerate the growth of IoT and will be the impetus for the next evolution in computing

Bigdataguys has organized courses to help developers (or any person that wants to know more about Google Cloud Platform Big Data & Machine Learning) gain a greater understanding of Google Cloud Platform Big Data & Machine Learning. This course gives you excellent opportunities in the job market. These classes aim to bring students up to speed on Google Cloud Platform Big Data & Machine Learning.

This course of Bigdataguys offers qualify courses in data science to know more about Google Cloud Platform Big Data & Machine Learning. The best way to learn about Google Cloud Platform Big Data & Machine Learning is to take a course with us. This course covers the basic theory and practical examples.

Average salaries for Google Data Engineer: $131952.

CURRICULUM

  • Google Platform Fundamentals Overview.
  • Google Cloud Platform Data Products and Technology.
  • Usage scenarios.
  • Lab: Sign up for Google Cloud Platform.
  • CPUs on demand (Compute Engine).
  • A global file system (Cloud Storage).
  • CloudShell.
  • Lab: Set up an Ingest-Transform-Publish data processing pipeline.
  • Stepping-stones to the cloud.
  • Cloud SQL: your SQL database on the cloud.
  • Lab: Importing data into CloudSQL and running queries.
  • Spark on Dataproc.
  • Lab: Machine Learning Recommendations with SparkML.
  • Fast random access.
  • Datalab.
  • BigQuery.
  • Lab: Build machine learning dataset.
  • Machine Learning with TensorFlow.
  • Lab: Train and use a neural network.
  • Fully built models for common needs.
  • Lab: Employ ML APIs
  • Message-oriented architectures with Pub/Sub.
  • Creating pipelines with Dataflow.
  • Reference architecture for real-time and batch data processing.
  • Why GCP?
  • Where to go from here
  • Additional Resources

Online: $2999
Next Batch: On Demand

In Class: $4999
Locations: New York City, D.C., Bay Area
Next Batch: starts from  25th Nov 2017

COURSE HIGHLIGHTS

Skill level: Intermediate
Language: English
Certificate: No
Assessments: Self
Prerequisites: Basic Python programming

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FAQ'S

Advanced Course like Bigdata on Google Cloud Platform duration largely depends on trainee requirements, it is always recommended to consult one of our advisors for specific course duration.

We record each LIVE class session you undergo through and we will share the recordings of each session/class.

If you have any queries you can contact our 24/7 dedicated support to raise a ticket. We provide you email support and solution to your queries. If the query is not resolved by email we can arrange for a one-on-one session with our trainers.

You will work on real world projects wherein you can apply your knowledge and skills that you acquired through our training. We have multiple projects that thoroughly test your skills and knowledge of various aspect and components making you perfectly industry-ready.

Our Trainers will provide the Environment/Server Access to the students and we ensure practical real-time experience and training by providing all the utilities required for the in-depth understanding of the course.

Yes. All the training sessions are LIVE Online Streaming using either through WebEx or GoToMeeting, thus promoting one-on-one trainer student Interaction.

The Bigdata on Google Cloud Platform by BigdataGuys will not only increase your CV potential but will offer you a global exposure with enormous growth potential.

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Pros
"AI Training and Placement in 12 weeks" I am C/C++ developer with 3 + years of programming experience. I completed deep learning tensorflow instructor lead online course. This is live instructor lead interactive training of 8 students. The instructor is PhD, interactive,very helpful and knowledgeable. His training approach is very detail oriented and focused on how to apply deep learning in real time. After training was completed, their consulting partners helped me get project placement within 4 weeks with healthcare startup in NYC. I would highly recommend this online boot camp if you are looking to jumpstart your career in AI and have programming background which is mandatory this course. I appreciate the instructors and Bigdataguys's team for helping me upgrade my career into AI and wish them success in their future initiatives.
Cons
I wish they had branches in Texas too and more frequent batches.
Lab Exercises94
Projects88.5
Trainer Quality98
Promptness88.5

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BLOGS

INSTRUCTORS

John Doe
Learning Scientist & Master Trainer 
John Doe has been a professional educator
for the past 20 years. He’s taught, tutored,
and coached over 1000 students, and he
holds degrees in Physics and Literature
from Northwestern University. He has
spent the last 4 years studying how
people learn to code and develop applications.

Lamar George
Learning Scientist & Master Trainer 
He has been a professional educator for
the past 20 years. He’s taught, tutored,
and coached over 1000 students, and
he holds degrees in Physics and Literature
from Northwestern University. He has
spentthe last 4 years studying how
people learn to code and develop applications.

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Training | Workshops | Paid Consulting | Bootcamps
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1250 Connecticut Ave, Suite 200,Washington, D.C,20036,
Telephone No.202-897-1944
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Description
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