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- Cloud computing is one of the most important and needed skills. Conventional physical servers are now giving way to more scalable, cost-effective, and secure environments such as the Google Cloud Platform (GCP). Startups to multinationals are embracing GCP to store, compute, network, and analyze data.
- Google Cloud Training Ahmedabad is a professional program which introduces one in-depth with how to design, run, and deploy apps on Google Cloud. It teaches both basic and advanced aspects like Compute Engine, Kubernetes Engine, BigQuery, Cloud Storage, IAM security, networking, and DevOps automation.
- In contrast to theoretical training, Google Cloud Training in Ahmedabad focuses on practical application. Students have immediate hands-on access to the Google Cloud console, labs, and actual business projects. Participants at the conclusion of this course will possess the skills and confidence to attempt GCP certifications and function as cloud professionals in leading industries.
Google Cloud Course in Ahmedabad
- It is designed to provide students with both technical knowledge and actual-practical skills necessary in the cloud sector. Google Cloud Training in Ahmedabad is designed such that students not only study but are also able to implement their learning in real-life situations.
Develop a solid foundation of Google Cloud architecture.
Practical application of Compute, Storage, and Networking services.
Acquire advanced tools like BigQuery, Dataflow, and TensorFlow.
Host applications on GCP with live monitoring.
Apply IAM policies for safe access and encryption.
Acquire skill through industry-level projects.
- The Google Cloud Classes in Ahmedabad is curated with a blend of theory and practice. Its feature-rich learning allows students to learn, practice, and secure placement opportunities with ease.
- Course Features:
Live online and offline classes by trainers.
Immediate practice on the Google Cloud Console.
Access to lab facilities 24/7 for limitless learning.
Real-life case studies and project-based training.
Mock tests for Google Cloud certifications.
Placement assistance, resume writing, and interview sessions.
- The Google Cloud Classes in Ahmedabad is available to a large student population and working professionals alike. Even individuals who are new to cloud technology can enroll.
- Who Is Eligible to Take This Course:
Engineering and IT students (B.Tech, BCA, MCA, etc.)
Fresh graduates searching for IT/cloud jobs.
Software developers making a switch to cloud projects.
Network engineers and system administrators.
Data engineers, database administrators, data analysts.
- Ahmedabad is quickly emerging as the IT hub of Gujarat with finance, healthcare, manufacturing, start-ups, and e-commerce companies embracing cloud technology. Demand for Google Cloud professionals is likely to increase further in the years to come.
- Successful completion of this Google Cloud Course in Ahmedabad enables candidates to work in opportunities not just in Ahmedabad-based companies but also in international IT companies.
- Career Scope Highlights:
Greater requirement for GCP certified professionals in Ahmedabad.
Certifications are valued by employers across the globe.
Provides scope in AI, Big Data, and Machine Learning.
Good freelancing & consultancy scope for migrations and hybrid solutions.
- The Google Cloud Course in Ahmedabad is designed on a coherent scheme of modules. The intention is to begin from the fundamental level and then proceed step by step to advanced levels to facilitate easy learning for everyone.
- Course Modules:
Introduction to Cloud and GCP: Fundamentals, virtualization, cloud service models, and GCP configuration.
Compute Services: App Engine, Compute Engine, Kubernetes Engine.
Networking: VPC (Virtual Private Cloud), Load Balancing, Cloud Interconnect.
Storage Services: Cloud Storage, Filestore, Persistent Disks.
Databases: Cloud SQL, Spanner, Bigtable, and Firestore.
Security and IAM: IAM policies, identity roles, firewalls, encryption methods.
Data and AI: BigQuery, TensorFlow, ML APIs, Pub/Sub, Dataflow.
DevOps and Automation: CI/CD pipelines, Cloud Build, Cloud Functions.
Monitoring and Debugging: Using Stackdriver for logging, monitoring, and alerts.
Capstone Project: Complete implementation of an end-to-end cloud solution using all modules.
- Another feature of this Google Cloud Course in Ahmedabad is its emphasis on official GCP certifications. These certifications hold currency all over the world and weigh heavily on resumes.
- Students can attempt the following after taking the course:
- GCP Training in Ahmedabad offers study material, simulated exams, and guide assistance from the trainers so that candidates feel sure to clear these certifications after the course.
Associate Cloud Engineer.
Professional Cloud Architect.
Professional Data Engineer.
Professional Cloud Security Engineer.
Professional DevOps Engineer.
- Once this GCP training course in Ahmedabad is completed, the entry-level candidates are offered good starting packages, provided they are certified professionals.
Freshers generally get 4.5 to 6 LPA.
With the certification, packages go up to 8 to 10 LPA.
Having experience of 2 to 3 years, salaries go up to 12 to 15 LPA.
Freelancing experts get money on a project-by-project basis and tend to earn more than in salaried designations.
- Cloud computing is a domain where growth is far quicker than other IT positions. Professionals can grow both in Indian and global markets with industry skills and GCP certifications.
- Career Growth Path:
Begin your cloud career as Cloud Support Engineer.
Graduate to positions such as Cloud Engineer / Data Engineer.
Promote to Cloud Architect / DevOps Engineer.
Secure high-level positions in MNCs worldwide.
Specialize deeper in Big Data, AI/ML, and Google Cloud advanced services.
- Demand for Google Cloud Training Courses in Ahmedabad has grown rapidly over the last few years with increasing IT adoption among startups and established firms. Rising digitalization among industries like finance, retail, healthcare, and education has fueled this trend.
- Reasons for Popularity:
Increased job opportunities for GCP professionals within Ahmedabad's IT economy.
Affordable course price in relation to larger metro cities.
Placement by IT firms and start-ups in Gujarat.
Practical training with project-based hands-on practice.
- After the completion of this GCP Classes in Ahmedabad students are eligible for various cloud specializations' jobs based on interest and certifications.
- Job Roles:
- Responsibilities:
Cloud Engineer.
Data Engineer.
DevOps Engineer.
Cloud Security Engineer.
Cloud Application Developer.
Deploying and managing scalable applications on GCP services.
Creating secure cloud networks and VPCs.
Implementing IAM roles, encryption, and policies for security.
Implementing workflows in Cloud Functions, CI/CD pipelines.
Monitoring and optimizing systems with Stackdriver.
- Upon completion of these GCP Classes in Ahmedabad, learners can get placed in various industries in Ahmedabad as well as other locations. Cloud roles are no longer the purview of IT alone nearly every industry is recruiting cloud experts.
- Industries that hire Cloud Specialists:
IT and Software Services.
Banking and Financial Services.
E-commerce and Retail.
Healthcare and Pharmaceuticals.
AI, Big Data, and ML-based Startups.
Telecom and Networking.
- Getting enrolled with a good institute is what matters most for career success. Our Google Cloud Training in Ahmedabad is framed to offer deep technical learning with career-centric assistance such as placements and certifications.
- Why Our Course is the Best Choice:
Experts who are working on real-time GCP with certifications are the trainers.
Strong emphasis on experiential practical learning.
Resume, certification exam, and interview guidance.
Low course fees with payment facilities.
Convenient batches: weekdays, weekends, and fast-track.
Lifetime access to study material and recorded classes.
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- Setting up cloud projects and accounts. Activities include
Creating projects
Assigning users to predefined IAM roles within a project
Managing users in Cloud Identity (manually and automated)
Enabling APIs within projects
Provisioning one or more Stackdriver workspaces
- Managing billing configuration. Activities include:
Creating one or more billing accounts
Linking projects to a billing account
Establishing billing budgets and alerts
Setting up billing exports to estimate daily/monthly charges
- Installing and configuring the command line interface (CLI), specifically the Cloud SDK (e.g., setting the default project)
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- Planning and estimating GCP product use using the Pricing Calculator
- Planning and configuring compute resources. Considerations include:
Selecting appropriate compute choices for a given workload (e.g., Compute Engine, Google Kubernetes Engine, App Engine, Cloud Run, Cloud Functions)
Using preemptible VMs and custom machine types as appropriate
- Planning and configuring data storage options. Considerations include:
Product choice (e.g., Cloud SQL, BigQuery, Cloud Spanner, Cloud Bigtable)
Choosing storage options (e.g., Standard, Nearline, Coldline, Archive)
- Planning and configuring network resources. Tasks include:
Differentiating load balancing options
Identifying resource locations in a network for availability
Configuring Cloud DNS
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- Deploying and implementing Compute Engine resources. Tasks include:
Launching a compute instance using Cloud Console and Cloud SDK (gcloud) (e.g., assign disks, availability policy, SSH keys)
Creating an autoscaled managed instance group using an instance template
Generating/uploading a custom SSH key for instances
Configuring a VM for Stackdriver monitoring and logging
Assessing compute quotas and requesting increases
Installing the Stackdriver Agent for monitoring and logging
- Deploying and implementing Google Kubernetes Engine resources. Tasks include:
Deploying a Google Kubernetes Engine cluster
Deploying a container application to Google Kubernetes Engine using pods
Configuring Google Kubernetes Engine application monitoring and logging
- Deploying and implementing App Engine, Cloud Run, and Cloud Functions resources. Tasks include, where applicable:
Deploying an application, updating scaling configuration, versions, and traffic splitting
Deploying an application that receives Google Cloud events (e.g., Cloud Pub/Sub events, Cloud Storage object change notification events)
- Deploying and implementing data solutions. Tasks include:
Initializing data systems with products (e.g., Cloud SQL, Cloud Datastore, BigQuery, Cloud Spanner, Cloud Pub/Sub, Cloud Bigtable, Cloud Dataproc, Cloud Dataflow, Cloud Storage)
Loading data (e.g., command line upload, API transfer, import/export, load data from Cloud Storage, streaming data to Cloud Pub/Sub)
- Deploying and implementing networking resources. Tasks include:
Creating a VPC with subnets (e.g., custom-mode VPC, shared VPC)
Launching a Compute Engine instance with custom network configuration (e.g., internal-only IP address, Google private access, static external and private IP address, network tags)
Creating ingress and egress firewall rules for a VPC (e.g., IP subnets, tags, service accounts)
Creating a VPN between a Google VPC and an external network using Cloud VPN
Creating a load balancer to distribute application network traffic to an application (e.g., Global HTTP(S) load balancer, Global SSL Proxy load balancer, Global TCP Proxy load balancer, regional network load balancer, regional internal load balancer)
- Deploying a solution using Cloud Marketplace. Tasks include:
Browsing Cloud Marketplace catalog and viewing solution details
Deploying a Cloud Marketplace solution
- Deploying application infrastructure using Cloud Deployment Manager. Tasks include:
Developing Deployment Manager templates
Launching a Deployment Manager template
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- Managing Compute Engine resources. Tasks include:
Managing a single VM instance (e.g., start, stop, edit configuration, or delete an instance)
SSH/RDP to the instance
Attaching a GPU to a new instance and installing CUDA libraries
Viewing current running VM inventory (instance IDs, details)
Working with snapshots (e.g., create a snapshot from a VM, view snapshots, delete a snapshot)
Working with images (e.g., create an image from a VM or a snapshot, view images, delete an image)
Working with instance groups (e.g., set autoscaling parameters, assign instance template, create an instance template, remove instance group)
Working with management interfaces (e.g., Cloud Console, Cloud Shell, GCloud SDK)
- Managing Google Kubernetes Engine resources. Tasks include:
Viewing current running cluster inventory (nodes, pods, services)
Working with node pools (e.g., add, edit, or remove a node pool)
Working with pods (e.g., add, edit, or remove pods)
Working with services (e.g., add, edit, or remove a service)
Working with stateful applications (e.g. persistent volumes, stateful sets)
Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK)
- Managing App Engine and Cloud Run resources. Tasks include:
Adjusting application traffic splitting parameters
Setting scaling parameters for autoscaling instances
Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK)
- Managing storage and database solutions. Tasks include:
Moving objects between Cloud Storage buckets
Converting Cloud Storage buckets between storage classes
Setting object life cycle management policies for Cloud Storage buckets
Executing queries to retrieve data from data instances (e.g., Cloud SQL, BigQuery, Cloud Spanner, Cloud Datastore, Cloud Bigtable)
Estimating costs of a BigQuery query
Backing up and restoring data instances (e.g., Cloud SQL, Cloud Datastore)
Reviewing job status in Cloud Dataproc, Cloud Dataflow, or BigQuery
Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK)
- Managing networking resources. Tasks include:
Adding a subnet to an existing VPC
Expanding a subnet to have more IP addresses
Reserving static external or internal IP addresses
Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK)
- Monitoring and logging. Tasks include:
Creating Stackdriver alerts based on resource metrics
Configuring log sinks to export logs to external systems (e.g., onpremises or BigQuery)
Viewing specific log message details in Stackdriver
Using cloud diagnostics to research an application issue (e.g., viewing Cloud Trace data, using Cloud Debug to view an application point-intime)
Viewing Google Cloud Platform status
Working with management interfaces (e.g., Cloud Console, Cloud Shell, Cloud SDK)
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- Managing identity and access management (IAM). Tasks include:
Viewing IAM role assignments
Assigning IAM roles to accounts or Google Groups
Defining custom IAM roles
- Managing service accounts. Tasks include:
Managing service accounts with limited privileges
Assigning a service account to VM instances
Granting access to a service account in another project
- Viewing audit logs for project and managed services.
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Google Professional Cloud Architect Training Program
30k
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Class
- 2 Live Project
- Self-Paced/ Classroom
- Certification Pass Guaranteed
- Designing a solution infrastructure that meets business requirements. Considerations include:
Business use cases and product strategy
Cost optimization
Supporting the application design
Integration with external systems
Movement of data
Design decision trade-offs
Build, buy, or modify
Success measurements (e.g., key performance indicators [KPI], return on investment [ROI], metrics)
Compliance and observability
- Designing a solution infrastructure that meets technical requirements. Considerations include:
High availability and failover design
Elasticity of cloud resources
Scalability to meet growth requirements
Performance and latency
- Designing network, storage, and compute resources. Considerations include:
Integration with on-premises/multi-cloud environments
Cloud-native networking (VPC, peering, firewalls, container networking)
Choosing data processing technologies
Choosing appropriate storage types (e.g., object, file, RDBMS, NoSQL, New SQL)
Choosing compute resources (e.g., pre-emptible, custom machine type, specialized workload)
Mapping compute needs to platform products
- Creating a migration plan (i.e., documents and architectural diagrams). Considerations include:
Integrating solution with existing systems
Migrating systems and data to support the solution
Licensing mapping
Network planning
Testing and proof of concept
Dependency management planning
- Envisioning future solution improvements. Considerations include:
Cloud and technology improvements
Business needs evolution
Evangelism and advocacy
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- Configuring network topologies. Considerations include:
Extending to on-premises (hybrid networking)
Extending to a multi-cloud environment that may include GCP to GCP communication
Security and data protection
- Configuring individual storage systems. Considerations include:
Data storage allocation
Data processing/compute provisioning
Security and access management
Network configuration for data transfer and latency
Data retention and data life cycle management
Data growth management
- Configuring compute systems. Considerations include:
Compute system provisioning
Compute volatility configuration (preemptible vs. standard)
Network configuration for compute nodes
- Infrastructure provisioning technology configuration (e.g. Chef/Puppet/Ansible/Terraform/Deployment Manager)
- Container orchestration with Kubernetes
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- Designing for security. Considerations include:
Identity and access management (IAM)
Resource hierarchy (organizations, folders, projects)
Data security (key management, encryption)
Penetration testing
Separation of duties (SoD)
Security controls (e.g., auditing, VPC Service Controls, organization policy)
Managing customer-managed encryption keys with Cloud KMS
- Designing for compliance. Considerations include:
Legislation (e.g., health record privacy, children’s privacy, data privacy, and ownership)
Commercial (e.g., sensitive data such as credit card information handling, personally identifiable information [PII])
Industry certifications (e.g., SOC 2)
Audits (including logs)
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- Analyzing and defining technical processes. Considerations include:
Software development life cycle plan (SDLC)
Continuous integration / continuous deployment
Troubleshooting / post mortem analysis culture
Testing and validation
Service catalogue and provisioning
Business continuity and disaster recovery
- Analyzing and defining business processes. Considerations include:
Stakeholder management (e.g. influencing and facilitation)
Change management
Team assessment / skills readiness
Decision-making process
Customer success management
Cost optimization / resource optimization (capex / opex)
- Developing procedures to ensure resilience of solution in production (e.g., chaos engineering)
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- Advising development/operation team(s) to ensure successful deployment of the solution. Considerations include:
Application development
API best practices
Testing frameworks (load/unit/integration)
Data and system migration tooling
- Interacting with Google Cloud using GCP SDK (gcloud, gsutil, and bq). Considerations include:
Local installation
Google Cloud Shell
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Google Professional Cloud DevOps Engineer Training Program
30k
LearnersWeekend/WeekdayLive
Class
- 2 Live Project
- Self-Paced/ Classroom
- Certification Pass Guaranteed
- Balance change, velocity, and reliability of the service
Discover SLIs (availability, latency, etc.)
Define SLOs and understand SLAs
Agree to consequences of not meeting the error budget
Construct feedback loops to decide what to build next
Toil automation
- Manage service life cycle
Manage a service (e.g., introduce a new service, deploy it, maintain and retire it)
Plan for capacity (e.g., quotas and limits management)
- Ensure healthy communication and collaboration for operations
Prevent burnout (e.g., set up automation processes to prevent burnout)
Foster a learning culture
Foster a culture of blamelessness
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- Design CI/CD pipelines
Immutable artifacts with Container Registry
Artifacts repositories with Container Registry
Deployment strategies with Cloud Build, Spinnaker
Deployment to hybrid and multi-cloud environments with Anthos, Spinnaker, Kubernetes
Artifacts versioning strategy with Cloud Build, Container Registry
CI/CD pipeline triggers with Cloud Source Repositories, Cloud Build GitHub App, Cloud Pub/Sub
Testing a new version with Spinnaker
Configure deployment processes (e.g., approval flows
- Implement CI/CD pipelines
CI with Cloud Build
CD with Cloud Build
Open source tooling (e.g. Jenkins, Spinnaker, Git Lab, Concourse)
Auditing and tracing of deployments (e.g., CSR, Cloud Build, Cloud Audit Logs)
- Manage configuration and secrets
Secure storage methods
Secret rotation and configuration changes
- Manage infrastructure as code
Terraform / Cloud Deployment Manager
Infrastructure code versioning
Make infrastructure changes safer
Immutable architecture
- Deploy CI/CD tooling
Centralized tools vs. multiple tools (single vs multi-tenant)
Security of CI/CD tooling
- Manage different development environments (e.g., staging, production, etc.):
Decide on the number of environments and their purpose
Create environments dynamically per feature branch with GKE, Cloud Deployment Manager
Local development environments with Docker, Cloud Code, Scaffold
- Secure the deployment pipeline:
Vulnerability analysis with Container Registry
Binary Authorization
IAM policies per environment
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- Manage application logs
Collecting logs from Compute Engine, GKE with Stackdriver Logging, Fluentd
Collecting third-party and structured logs with Stackdriver Logging, Fluentd
Sending application logs directly to Stackdriver API with Stackdriver Logging
- Manage application metrics with Stackdriver Monitoring
Collecting metrics from Compute Engine
Collecting GKE/Kubernetes metrics
Use metric explorer for ad hoc metric analysis
- Manage Stackdriver Monitoring platform
Creating a monitoring dashboard
Filtering and sharing dashboards
Configure third-party alerting in Stackdriver Monitoring (i.e., Pager Duty, Slack, etc.)
Define alerting policies based on SLIs with Stackdriver Monitoring
Automate alerting policy definition with Cloud DM or Terraform
Implementing SLO monitoring and alerting with Stackdriver Monitoring
Understand Stackdriver Monitoring integrations (e.g., Grafana, BigQuery)
Using SIEM tools to analyze audit/flow logs (e.g., Splunk, Data dog)
Design Stackdriver Workspace strategy
- Manage Stack Driver Logging platform
Enabling data access logs (e.g., Cloud Audit Logs)
Enabling VPC flow logs
Viewing logs in the GCP Console
Using basic vs. advanced logging filters
Implementing logs-based metrics
Understanding the logging exclusion vs. logging export
Selecting the options for logging export
Implementing a project-level / org-level export
Viewing export logs in Cloud Storage and BigQuery
Sending logs to an external logging platform
- Implement logging and monitoring access controls:
Set ACL to restrict access to audit logs with IAM, Stack driver Logging
Set ACL to restrict export configuration with IAM, Stack driver Logging
Set ACL to allow metric writing for custom metrics with IAM, Stack driver Monitoring
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- Identify service performance issues
Evaluate and understand user impact (Stackdriver Service Monitoring for App Engine, Istio)
Utilize Stackdriver to identify cloud resource utilization
Utilize Stackdriver Trace/Profiler to profile performance characteristics
Interpret service mesh telemetry
Troubleshoot issues with the image/OS
Troubleshoot network issues (e.g., VPC flow logs, firewall logs, latency, view network details)
- Debug application code:
Application instrumentation
Stackdriver Debugger
Stackdriver Logging
Stackdriver Trace
Debugging distributed applications
App Engine local development server
Stackdriver Error Reporting
Stackdriver Profiler
- Optimize resource utilization:
Identify resource costs
Identify resource utilization levels
Develop plan to optimize areas of greatest cost or lowest utilization
Manage pre-emptible VMs
Work with committed-use discounts
TCO considerations
Consider network pricing
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- Coordinate roles and implement communication channels during a service incident:
Define roles (incident commander, communication lead, operations lead)
Handle requests for impact assessment
Provide regular status updates, internal and external
Record major changes in incident state (When mitigated When all clear etc.)
Establish communications channels (email, IRC, Hangouts, Slack, phone, etc.)
Scaling response team and delegation
Avoid exhaustion / burnout
Rotate / hand over roles
Manage stakeholder relationships
- Investigate incident symptoms impacting users
Identify probable causes of service failure
Evaluate symptoms against probable causes; rank probability of cause based on observed behavior
Perform investigation to isolate most likely actual cause
Identify alternatives to mitigate issue
- Mitigate incident impact on users:
Roll back release
Drain / redirect traffic
Turn off experiment
Add capacity
- Resolve issues (e.g., Cloud Build, Jenkins):
Code change / fix bug
Verify fix
Declare all-clear
- Document issue in a post-mortem:
Document root causes
Create and prioritize action items
Communicate post-mortem to stakeholders
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Google Professional Data Cloud Engineer Training Program
30k
LearnersWeekend/WeekdayLive
Class
- 2 Live Project
- Self-Paced/ Classroom
- Certification Pass Guaranteed
- Data processing Fundamentals
Data Processing Concepts
Data Processing Pipelines
- Data Storage Fundamentals
About GCP
Data Storage in GCP
Working with Data
Cloud Storage
Data Transfer Services
Cloud Fire Store
Cloud Spanner
Cloud Memory Store
Different Memory options
- Selecting the best memory storage
Compare storage options
Mapping storage systems to business requirements
Data modeling
Trade-offs involving latency, throughput, transactions
Distributed systems
Schema design
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- Data publishing and visualization
- Online (interactive) vs. batch predictions
- Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
- Big Data Ecosystem
MapReduce
Hadoop & HDFS
Apache Pig
Apache Spark
Apache Kafka
- Real-time Messaging with Pub/Sub
Pub/sub basics
pub/Sub Terminologies
Advanced Pub/Sub Concepts
Working with Pub/Sub
- Cloud Data Flow Pipelining
Introduction to Data flow
Pipeline Lifecycle
Dataflow pipeline concepts
Advanced Dataflow concepts
Dataflow security and access
Using Dataflow
- Cloud Dataproc
Dataproc Basics
Working with Dataproc
Advanced Dataproc
- NoSQL Data with Cloud Big Table
Big Table Concepts
Big Table Architecture
Big Table Data Model
Big Table Schema Design
Big Table Advanced Concepts
- Data Analytics using BigQuery
BigQuery Basics
Using BigQuery
Partitioning and Clustering
Best Practices
Securing BigQuery
BigQuery Monitoring and Logging
Machine Learning with BigQuery ML
Working with BigQuery
Advanced BigQuery Concepts
- Data Exploration with Cloud Datalab
Datalab Concepts
Working with Datalab
- Visualization with Cloud Data Studio
Reporting & Business intelligence
Data Distribution
Introduction to Cloud Data Studio
Charts and Filters
- Job automation and orchestration (e.g., Cloud Composer)
Orchestration with Cloud Composer
Cloud Composer Overview
Cloud Composer Architecture
Working with Cloud Composer
Advanced Cloud Composer Concepts
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- Steps for Designing
Choice of infrastructure
System availability and fault tolerance
Use of distributed systems
Capacity planning
Hybrid cloud and edge computing
Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
At least once, in-order, and exactly once, etc., event processing
- Migrating data warehousing and data processing
Awareness of current state and how to migrate a design to a future state
Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
Validating a migration
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- Building and operationalizing Storage Solutions
Cloud Managed Services
Effectives Use of Managed Services
Storage Cost and performance
Lifecycle Management of Data
- Building and operationalizing Pipelines
Data cleansing
Batch and streaming
Transformation
Data acquisition and import
Integrating with new data sources
- Building and operationalizing processing infrastructure
Provisioning resources
Monitoring pipelines
Adjusting pipelines
Testing and quality control
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- Introduction to Machine Learning
Machine Learning Introduction
Machine Learning Basics
Machine Learning Types and Models
Overfitting
Hyperparameters
Feature Engineering
- Machine Learning with TesnorFlow
Deep Learning with TensorFlow
Introduction to Artificial Neural Networks
Neural Network Architectures
Building a Neural Network
- Leveraging pre-built ML models as a service. Considerations include:
ML APIs (e.g., Vision API, Speech API)
Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
Conversational experiences (e.g., Dialogflow)
- Deploying an ML pipeline
Ingesting appropriate data
Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
Continuous evaluation
- Choosing the appropriate training and serving infrastructure
Distributed vs. single machine
Use of edge compute
Hardware accelerators (e.g., GPU, TPU)
- Measuring, monitoring, and troubleshooting machine learning models
Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
Impact of dependencies of machine learning models
Common sources of error (e.g., assumptions about data)
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+16473706470
+91-971 152 6942
Interested in Course?
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