Google Cloud Platform Solutions – Developing Solutions With Google Cloud Platform Training Course 2017-12-27T23:50:49+00:00

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Developing Solutions With Google Cloud Platform

Google Cloud Platform Solutions - Developing Solutions With Google Cloud Platform Training Course

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Google Cloud Platform Solutions - Developing Solutions With Google Cloud Platform Training Course

Google Cloud Platform Solutions – Developing Solutions With Google Cloud Platform Training Course

Objective of Google cloud platform solutions

The objective of this course is to provide an introduction to Google cloud platform solutions Fundamentals.  This course focuses on fundamental theory. You will receive a full training and Google cloud platform solutions 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 solutions in practical situations.

TRAINING METHODOLOGY

In Class: $2,999
Locations: NEW YORK CITY, D.C, BAY AREA.
Next Session: 25th Nov 2017

Online: $1,499
Next Session: On Demand

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Google Cloud Platform Solutions

Instructor: John Doe, Lamar George

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Google Cloud Platform Solutions

Google Cloud Platform Solutions

Google Cloud Platform

Google Cloud Platform (used for Google cloud platform solutions) is a suite of cloud computing services that is used to run on the same infrastructure that Google uses internally (for its end-user products). Google could  provide a series of modular cloud services including computing, data storage, data analytics and machine learning.

The popular products of Google Cloud Platform (used for Google cloud platform solutions):

  1. Google Compute Engine – IaaS providing virtual machines.
  2. Google App Engine – PaaS for application hosting.
  3. Bigtable – IaaS massively scalable NoSQL database.
  4. BigQuery – SaaS large scale database analytics.
  5. Google Cloud Functions –FaaS providing serverless functions to be triggered by cloud events.
  6. Google Cloud Datastore – DBaaS providing a document-oriented database.
  7. Cloud Pub/Sub – a service for publishing and subscribing to data streams and messages.
  8. Google Storage – IaaS providing RESTful online file and object storage.

Quantum computing and Google cloud platform solutions are the next big technological revolution. For quantum computing, Google wants to develop solutions with google cloud platform and Microsoft is already creating a new coding language for the technology.

Intel wants that quantum computing be a reality, so Intel created a new superconducting chip using advanced material science and manufacturing techniques.

Actually, the building blocks of quantum computing (qubits) are very fragile. They can only operate at extremely low temperatures and must be packaged carefully to prevent data loss.

Actually, QuTech is simulating quantum algorithm workloads, and Intel is fabricating new “qubit test chips”. These chips will serve users well as they venture into new computing paradigms, from neuromorphic to quantum computing.

Google cloud platform solutions for Quantum computing probably will be a reality soon.

Google cloud platform solutions enable companies to move resources away from undifferentiated, costly activities. Google Cloud Platform (or Google cloud platform solutions) can identify trends in massive, noisy datasets from diverse sources, provide companies with secure and efficient IT consumption models, archive large volumes of sensitive data, and so on.

Financial Services companies must generate timely insight for customers and continuously earn their trust. Google cloud platform solutions give a superior pricing model for efficient IT spend, including world-class reliability and security. Furthermore, companies can free up resources toward adding unique value.

Convert CapEx to OpEx and take advantage of automatic sustained-use discounts with Google cloud platform solutions’ pricing model and philosophy. The benefits from cloud economics are passed continuously to customers, along with the innovations made available to you just by choosing to Go Google. Google cloud platform solutions run on the same infrastructure relied on by Google’s own trusted services (such as Search, YouTube, Maps).

Financial Services companies face more regulatory scrutiny than before and the risk of breach and data loss are top of mind for decision makers. Indeed, End-to-end security remains critical. Google cloud platform solutions has undergone rigorous compliance measures, and for nearly two decades Google has practiced integrated, pervasive security to protect its own services. Actually, Google is striving to make security a key reason to adopt public cloud.

The Google’s customers from the financial services community use Google cloud platform solutions’ product portfolio to address a broad range of solutions across high-performance and grid computing, storage & archival, business process automation, analytics & data warehousing, and machine learning.

The long-term opportunity lies in applying Google’s heritage of machine learning and analytics at web-scale to financial/market data. Google cloud platform solutions enable small, modest-sized teams to aggregate and run machine learning workloads on massive real-world data to do predictive analytics, and many domain-specific applications such as fraud & anomaly detection. Google cloud platform solutions have used: a library for machine intelligence TensorFlow and Cloud Machine Learning products (including several pre-trained models usable out-of-the-box such as Cloud Vision API, Cloud Speech API, and Google Cloud Translation API).

Actually, the volume of data being generated is increasing. Highly unstructured, raw data reduce efficiency at scale. Analytics and machine intelligence at web-scale have been researched in Google since early days. Google Cloud Platform surfaces the same analytical engines invented by Google to help enterprises and operational environment.

Google cloud platform solutions lead the industry in the ability to let the user analyze data at the scale of the entire web, with the familiarity of SQL and in a fully managed, serverless architecture. Google cloud platform solutions are able to scale automatically while the user focus only on the business.

BigQuery is Cloud Platform’s fully managed data warehouse that lets the user economically query massive volumes of data in a fast way. This is taking advantage of their pricing benefits and the scalability and security of Google’s world-class infrastructure to power your business insights.

Cloud Dataflow is a service for developing and executing a wide range of data processing patterns such as ETL, batch computation, and stream analytics.

Using this Google cloud platform solution, companies standardized on tools such as Spark, Hadoop/MapReduce, Hive, and Pig, will find a natural transition in Cloud Dataproc. Data pipelines outgrowing clusters as Dataproc are not a problem.

In this Google cloud platform solution, the user must choose from a variety of globally available storage products for your data, from managed SQL to NoSQL options, including our category-defining archival product Nearline.

In this Google cloud platform solution, an opportunity (for companies) lies in applying Google’s heritage of machine learning and analytics at web-scale to business data. Cloud Platform enables modest-sized teams to aggregate and run machine learning workloads on massive data to do predictive analytics. Google (Google cloud platform solution) has recently opened-sourced its library for machine intelligence TensorFlow and launched Cloud Machine Learning products.

In this Google cloud platform solution, Google has led the industry with innovations in data processing technologies such as MapReduce, Bigtable, and Dremel. Actually, Google cloud platform solutions is making the latest generation of its data processing tools available to everyone, including industry leading programming tools and programming models.

A Google Cloud platform solution is the mobile devices backed by scalable machine intelligence (ML) in the cloud. This requires special considerations such as serverless capabilities, a cloud-first data model capable of persisting data even (when the device is offline), low-latency access to media anywhere in the world, and real-time data synchronization across all mobile platforms. Google Cloud platform solutions have a focus on ease of use and speed (all without having to manage infrastructure).

Modern tools should handle the complexity of real-time applications. Firebase (Google cloud platform solution) is a unified app platform for iOS, Android, and the web that lets you build better mobile apps and grow your business. Without requiring any server-side management, Cloud Functions (Google cloud platform solution) lets you write single-purpose functions that respond to cloud events. Serverless solutions (Google cloud platform solution) like Firebase and Cloud Functions let mobile developers focus on what’s important.

In this Google cloud platform solution, it is very important the creation of highly responsive experiences for users backed by powerful computing resources remotely. Google cloud platform solution helps you strike this balance easily for your mobile backend, where non-interactive tasks get offloaded to Cloud Platform, resulting in improved battery life on mobile, lower bandwidth usage, and a snappy client experience on mobile.

With your data on Cloud Platform (Google cloud platform solution), unlock insights with Google’s pioneering big data analytics products. In this Google cloud platform solution, Query petabytes of data with BigQuery.

Speed and latency are very important for the user. In this Google cloud platform solution, private fiber network spans the globe with over 100 points of presence across many countries, meaning it is very low latency. In this Google cloud platform solution, the app data travels Google’s global network from our data centers to your users, anywhere in the world.

In this Google cloud platform solution, cross-platform games backed by cloud services create an unprecedented audience of connected players. In this Google cloud platform solution, best games blend client and cloud into a single platform, creating shared gaming experiences not possible before.

This Google cloud platform solution synchronizes data across mobile, console, and PC, and lets you orchestrate your game centrally from the cloud.

In this Google cloud platform solution, Google Cloud Platform goes well beyond dedicated servers and hosting for mobile, PC, and console games.

In this Google cloud platform solution, it is used a variety of globally available storage solutions for the data. This Google cloud platform solution let you synchronize data in real-time for your players around the world, including full offline support on your mobile games.

DevOps have the agility to be able to try things out quickly and move on without incurring upfront costs or facing delays.

Multiple, disposable, and recreatable environments that are accessible around the globe are a must have for application lifecycle management. Configuration management tools that suits your environment, containers, and choice of development and continuous integration tools are just a few of the things that the modern developer expects to be at their disposal of this Google cloud platform solution. This Google cloud platform solution provides a cost effective platform that addresses these requirements and more for any development and test environment.

DevOps requires numerous, disposable environments. This Google cloud platform solution is used to automatically create and tear down environments on demand with no upfront costs.

Google has officially launched its Google Cloud IoT Core service (Google cloud platform solution) into public beta. The service (Google cloud platform solution) is getting a few new features and partners to help with building out Internet of Things (IoT) solutions.

Google Cloud IoT Core (Google cloud platform solution) is a fully managed service on Google Cloud Platform (GCP) that helps businesses secure and manage their connected IoT solutions at scale. The service (Google cloud platform solution) can be used to manage IoT devices, and works in tandem with the Google Cloud IoT solution to provide data analytics.  This Google cloud platform solution can also integrate with other Google analytics services such as Google Cloud Pub/Sub, Google Cloud Dataflow, Google Cloud Bigtable, Google BigQuery, and Google Cloud Machine Learning Engine.

Cloud IoT Core (Google cloud platform solution) now maintains a logical representation of the physical IoT device, including device properties, and its last reported state.

Google Cloud IoT Core (Google cloud platform solution) is a fully managed service for managing and securing IoT devices

Google Cloud IoT solution (Google cloud platform solution) an integrate with a variety of Google analytics services to drive data insights.

Cloud computing platforms are still quietly expanding inside data centers. In a new product update, the Google Cloud Platform team announced that significantly more compute power is being made available for use.

 

Running on Intel Xeon Skylake processors, configurations of up to 96 vCPUs and 624GB of memory are now available as part of three standard configurations. The lower tier options still have the same 96 cores, but it has less memory. It may seem that such extreme configurations are unnecessary, but customers still have the option to scale even further. The absolute top of the line option from Google Cloud Platform now allows an astounding 9.75TB of memory paired with 16 of the 96 vCPU nodes.

For businesses and curious individuals, there are still custom machine types available that allow a reduced number of CPU cores and less memory to bring pricing down into more affordable territory. Older generations of CPUs can also be used to help bring costs down if compute time is not critical.

If you are still left confused as to what someone actually can do with massive quantities of compute power, you probably are not alone. Google is promoting the fact that their instances are certified for SAP HANA, a database software that stores information in memory instead of on traditional hard drives to greatly reduce the time. Faster querying of information is highly beneficial for scientific research and for analyzing massive quantities of data.

Amazon Web Services (AWS) has been used more than Google Cloud. Microsoft (Azure) and Google (Google Cloud Platform) are doing everything they can to bridge the gap.

Actually, Microsoft is leveraging a huge market. Microsoft has an incredible market with enterprises.

Nowadays, Google Cloud is leveraging the scale, the smarts and the new leadership.

Then, Google (Google Cloud) is the most experienced company in the world at running web-scale infrastructures. Furthermore, Amazon and Microsoft both run massive web properties, but Google has better smarts than them to run Google’s search engine, mapping, office productivity and other services is incredible. Google has more talent than Amazon and Microsoft.

Google has fallen down is in selling to enterprise. The selling problem of Google is that they don’t accept other approach or pattern.

VMware said that Google cloud platform solutions are an interesting development.

Actually, offerings such as Google’s Dedicated Interconnect, which is a way for organizations to connect data centers directly to Google cloud, are fascinating developments to Develop solutions with google cloud platform.

Actually, enabling customers to quickly transfer data and Google Cloud are used to Develop solutions with google cloud platform.

Google and Marketo joined forces. Google will include integration of Google tools into Marketo’s products. Marketo plans to use fully Google’s cloud before 2019. Marketo succeeded in using Google Cloud platform.

Actually, Google cloud platform solutions have become more cost-efficient, secure, and reliable. The major providers (Google Cloud, Azure, AWS) are now investing heavily in their hardware, software, and global networking infrastructure to obtain more market share. This competition (between Google Cloud, Azure, and AWS) drives costs down and requires them to constantly innovate to stay ahead.

The three cloud-computing providers we are Azure, Google Cloud, and AWS.

Google Cloud Platform is used at Kinsta. There are definitely some advantages and disadvantages to both providers.

In January 2017, RightScale conducted its sixth annual State of the Cloud Survey to analyze current cloud computing trends. In 2016, the number one cloud challenge was there wasn’t enough resources or expertise. Indeed, security was a problem too. Furthermore, the performance was a challenge when it comes to cloud computing. In 2017, all these problems were reduced.

Actually, the barrier of entry is rapidly decreasing. Indeed, a lot of managed WordPress hosting providers, such as Kinsta, are using the cloud (such as Google cloud). Large enterprises are now even investing in their own employees and engineers as Google Cloud, AWS, and Azure. Indeed, Google cloud platform solutions are developed by these engineers.

In 2017, the new customer started to adopt Azure and Google more than AWS, but AWS still is the leader because they are the first ones to really invest and shape the cloud computing industry. Google Cloud and Azure definitely have to invest more in research of this cloud technology.

It was predicted that by the end of 2018, spending on IT-as-a-Service for data centers, software and services will be $547 billion.

IDC FutureScape predicted (that by 2018) that more than 50% of IT spending will be cloud-based, reaching 60% of all IT infrastructure, and 60–70% of all software, services, and technology spending by 2020.

Wikibon is predicting that enterprise-cloud-platform spending is growing at a 16% compound annual growth (CAGR) run rate between 2016 and 2026.

Over the past five years, there has definitely been a steady growth pattern over the past five years.

It’s the developers, engineers, and sysadmins that are actually implementing the google cloud platform solutions.

Google cloud platform solutions are essentially made up of a lot of different services and solutions, which allow the user to use the same software and hardware infrastructure that Google uses for their own products (just like YouTube and Gmail). Google launched their first service, Google App Engine in a public preview in 2008.

Google Compute Engine and associated services (which allow users to launch virtual machines on demand) are used in enterprises such as Kinsta (and it is also used LXD containers to allow complete isolation and automatic scaling).

Google Compute Engine is used in companies such as HTC, Best Buy, Ubisoft, Philips, Domino’s Pizza, Leadpages, Heathrow, Coca-Cola, Evernote, Sony Music, and so on.

Amazon Web Services (AWS) is a subsidiary of Amazon.com that is used to provide cloud-computing services to businesses and individuals (since 2006).  Just like Google cloud platform solutions, Amazon Web Services (AWS) have a multitude of different services and solutions.

Actually, Google Cloud and AWS are very similar. The virtual machine that Google Cloud uses is KVM, and the virtual machine that AWS(EC2) uses is Xen. They use a different naming convention (Google Compute Engine refers to them as machine types, and Amazon EC2 refers to them as instance types).

Google Cloud allows the user to depart from the predefined configurations and customize the instance’s CPU and RAM resources to fit the workload (called custom machines).

The type of storage and disks used by a Google Cloud has a direct impact on performance, and the ability to burst capacity for short times.

Google Compute Engine offers persistent disks (block storage), and AWS EC2 offers this via their Elastic Block Store (EBS) (block storage), (which is virtual disk volume used in conjunction with cloud-based virtual machines).

Google Compute Engine offers object storage via their Google Cloud Storage service, and AWS offers object storage via their Amazon S3 service (and object storage is like distributed object storage).

Actually, Amazon EC2 and Compute Engine allow users to use disks that are locally attached to the physical machine running the instance.

Indeed, Google Cloud and AWS use different networks and partners to interconnect their data centers across the globe and deliver content via ISPs to end-users.

A big factor when it comes to comparing the two providers is network latency, which is important when it comes to businesses that serve visitors in a specific geographical location.

Indeed, Google Cloud latency is better than AWS.

Google Cloud is the first major public cloud to offer a tiered cloud network. Actually, the premium tier delivers traffic over Google’s well-provisioned, low latency, highly reliable global network. Indeed, the standard tier delivers network quality comparable to that of other major public clouds.

Google and Puppet want a better way to build and deploy applications (Google cloud platform solutions). Puppet approved modules for the infrastructure-as-code system (which means that customers can use the same tools in the cloud that they’re familiar). Indeed, Google (Google cloud platform solutions) has built tools internally to ensure that as users’ cloud services change.

Google (Google cloud platform solutions) wants to attract more enterprise customers, and its relationship with Puppet could help bring in business. Indeed, Puppet stands out from other developer tool vendors. Then, Google uses Puppet internally to automate some of its work. Puppet is still supporting other cloud platforms.

Google Compute Engine VM is used to provide more resources for memory intensive applications on its cloud platform. Indeed, the machine types have up to 96 vCPUs and 624 GB of memory.

Actually, Google became the first major cloud provider to start offering virtual machine instances using Skylake processors.  The 96-core machines (Google cloud) are available in beta in four Google Cloud Platform (GCP) regions (Central US, West US, West Europe, and East Asia).

Google (Google cloud platform solutions) takes virtual machines to a different level with nested virtualizations.

Google(Google cloud platform solutions) recently announced it has brought nested virtualization on Compute Engine virtual machine instances. This (Google cloud platform solutions and virtual machines) enables users to run virtual machine instances within a virtual machine instance.

Actually, nested virtualization is a method of running a hypervisor inside a virtual machine (Google’s virtual machine). A host-hypervisor runs on physical hardware and an outer guest virtual machine runs on the host-hypervisor.

The benefits of nested virtualization are:

  • Nested virtualization makes it easier for enterprise users to move their on-premises, virtualized workloads to the cloud without having to import and convert VM images.
  • Nested virtualization provides affordable cloud-based disaster recovery solutions, and is ideal for companies that want to setup virtual environments for technical training and certification courses.
  • Nested virtualization can run multiple hypervisors on the same host server.
  • Nested virtualization is used for experimenting with server setups, testing configurations and shedding light on software products.

Nested virtualization needs a virtual machine to run on an Intel Haswell or newer CPU. Actually, users run a typical virtual machine and install a KVM-compatible hypervisor on that instance. And, Google (Google cloud platform solutions) does not support non-KVM hypervisors like Xen, ESX and Microsoft’s Hyper-V. Actually, the features (Google cloud platform solutions) only work for Linux instances.

Actually, Microsoft added virtual nesting to Azure Stack. Furthermore, Amazon Web Services (AWS) provides the ability to run nested virtual machines using third-party tools like Oracle’s Ravello.

Actually, Google (Google cloud platform solutions) still have growth opportunities.

Google (Google cloud platform solutions) provides a significant competitive advantage in the offerings. Indeed, Google cloud platform solutions could be a huge opportunity. The Amazon’s stocks gained 150% and Microsoft’s stocks gained 50% when they started to disclose more information about their cloud businesses.

Actually, Google cloud platform solutions‘ market share will grow to 13% in 2018, while Microsoft-Azure share expands to 19% and Amazon Web Service’s market share drops to 68%.

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

This Google cloud platform solutions course of Bigdataguys offers you to know more about Google cloud platform solutions. The best way to learn about Google cloud platform solutions is to take a course with us. This course covers the basic theory and practical examples

The average salary for google cloud platform engineer ranges from approximately $107,214 per year for Solutions Engineer to $133,761 per year for Platform Engineer.

CURRICULUM

  • Identify the advantages of Google Cloud Platform for solution development
  • Identify services and tools available for solution development using Google Cloud Platform
  • Compare examples of Google Cloud Platform architectures for solution development

Lab: Google Cloud Source Repositories

  • Create a project for the course
  • Use Google Cloud Shell to develop and test an application using the App Engine SDK
  • Configure Google Cloud Source Repositories to remotely host code in Google Cloud Platform
  • Identify Cloud Endpoints features
  • Explain how to develop APIs using Cloud Endpoints
  • Compare development of Cloud Endpoints APIs using Java and Python

Lab: Google Cloud Endpoints

  • Review and edit Cloud Endpoints source code
  • Deploy an application to App Engine
  • Test a Cloud Endpoints  API in the APIs Explorer
  • Explain the use cases for App Engine Services and how to use them in structuring an application
  • Identify how to deploy and access individual App Engine services
  • Explain how to route requests to individual services

Lab: Google App Engine Services

  • Review the code for a sample application used throughout the remainder of the course
  • Deploy multiple App Engine services to a single project
  • Compare authentication and authorization
  • Identify options for securing App Engine applications
  • Explain the use cases for Application Default Credentials

Lab: User Authentication

  • Configure the OAuth consent screen and create a client ID
  • Modify Conference Central to use your client ID
  • Test Conference Central authentication
  • Modify your admin service to require administrator rights
  • Explain the use cases for App Engine versions
  • Identify how to access App Engine monitoring and logging services
  • Explain the use of resource quotas and how to troubleshoot related errors

Lab: Managing Google App Engine Applications

  • Review App Engine settings, quotas, instances, and logs
  • Update App Engine services to log to Cloud Logging
  • Deploy new versions of your default and admin services
  • Route all traffic to a new version of the default service
  • Compare storage options for App Engine Solutions Developers
  • Explain the purpose of, and use cases for, Google Cloud Storage
  • Compare Cloud SQL integration with App Engine and Compute Engine
  • Explain basic Cloud Datastore terminology and concepts, including Entity Groups

Lab: Google Cloud Datastore

  • Update an existing application to save data persistently
  • Test saving application data to Cloud Datastore
  • List and view Cloud Datastore entities in the Google Cloud Platform Console
  • Identify available query filters for Cloud Datastore
  • Compare single­property, and composite indexes in Cloud Datastore
  • Configure and optimize indexes for Cloud Datastore

Lab: Google Cloud Datastore Queries and Indexes

  • Add support for querying entities by kind and ancestor
  • Add query filters to Cloud Datastore searches
  • Update an index configuration to support composite indexes
  • Identify the steps of a Cloud Datastore write
  • Compare strong and eventual consistency in Cloud Datastore
  • Identify how to achieve strongly consistent queries
  • Identify best practices for Cloud Datastore transactions

Lab: Google Cloud Datastore Transactions

  • Add support for using Cloud Datastore transactions to an application
  • Add a Cloud Endpoint API method to view data from a different service
  • Identify Memcache types, use cases, and implementation patterns
  • Compare available scaling behaviors for application services
  • Configure application scaling for individual services

Lab: Google App Engine Performance and Optimization

  • Review Cloud Trace reports for an application
  • Configure and run a security scan for an application
  • Update an application to make use of Memcache
  • Configure and test application scaling for application services
  • Compare Push and Pull Queues
  • Explain how to schedule tasks with the Cron Service
  • Configure and securing Push and Pull Queues, as well as the Cron Service

Lab: Task Queue API

  • Add a task handler to send an email using the Mail API
  • Add a Cron Service handler and configuration to an existing application

Lab: Deleting Google Cloud Platform Projects and Resources

  • Export Google Cloud Platform data from a project
  • Delete Google Cloud Platform resources
  • Shut down a Google Cloud Platform project

Online: $2499
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 Google cloud platform solutions Training Course 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 Developing Solutions With Google cloud platform solutions Course by BigdataGuys will not only increase your CV potential but will offer you a global exposure with enormous growth potential.

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"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.
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I wish they had branches in Texas too and more frequent batches.
Lab Exercises94
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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.

Summary
Training | Bootcamp | Consulting
User Rating
5 based on 6 votes
Service Type
Training | Bootcamp | Consulting
Provider Name
BigDataGuys ,
1250 Connecticut Ave NW,Washington ,D.C-20036,
Telephone No.202-897-1944
Area
Online | NYC | D.C | Toronto
Description
Google Cloud Platform Solutions - Developing Solutions With Google Cloud Platform Training Course is a 3 day instructor-led class introduces participants to Solution Development for Google Cloud Platform.
Live Training Instructor-led
In-Class Bootcamps in NYC | D.C | Bay Area & Metro Cities
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