Google Cloud Platform Training

Google Training Courses

Get Google Cloud Platform (GCP) certification, exams, and training from certified professionals. Build a solid and comprehensive understanding of the services and technologies available on Google Cloud to enhance your career and ensure you pass your certification on your first attempt.

Check out our Official Google Training Courses

Why Get Google Cloud Certified?

Google Cloud Platform is one of the top three global cloud providers and with the growth of a hybrid approach to cloud service providers GCP is certain to remain in the mix of solutions used by companies across the globe.

The shortage of cloud computing-related skills across the world is even more acute with respect to GCP;  resulting in lucrative opportunities for those with the required skills and experience.

Google Cloud Platform provides a plethora of services that can be leveraged in a myriad of IT careers from scalable infrastructure to artificial intelligence and machine learning and data science.  Get GCP certified and turbo boost your career.

Google GSuite and Google Chrome Enterprise

GCP is built for the hybrid cloud allowing easy integration with other cloud providers and on-premises solutions that don't lock you into a single provider. 

But two of Google's most compelling services are G Suite and Google Chrome Enterprise. These are compelling solutions for businesses small or large with the shift to remote work and accessible-from-anywhere services.

Take advantage of the huge opportunities that exist to migrate customers to GSuite and Google Chrome enterprise but getting GSuite and Google Chrome Enterprise certified.

GCP Fundamentals Core Infrastructure Course

Googles  GCP Fundamentals Core Infrastructure course covers the fundamentals of Google Cloud Platform (GCP) and is a great starting point for anyone looking to skill up on GCP.

Title: GCP Fundamentals - Core Infrastructure
Duration: 1 Days
Price: R5,000 (ex vat)

Module 1: Introducing Google Cloud Platform

  • Explain the advantages of Google Cloud Platform
  • Define the components of Google's network infrastructure, including Points of presence, data centers, regions, and zones
  • Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS)

Module 2: Getting Started with Google Cloud Platform

  • Identify the purpose of projects on Google Cloud Platform
  • Understand the purpose of and use cases for Identity and Access Management
  • List the methods of interacting with Google Cloud Platform
  • Lab: Getting Started with Google Cloud Platform

Module 3: Virtual Machines and Networks in the Cloud

  • Identify the purpose of and use cases for Google Compute Engine
  • Understand the various Google Cloud Platform networking and operational tools and services
  • Lab: Compute Engine

Module 4: Storage in the Cloud

  • Understand the purpose of and use cases for Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable, and Google Cloud Datastore
  • Learn how to choose between the various storage options on Google Cloud Platform
  • Lab: Cloud Storage and Cloud SQL

Module 5: Containers in the Cloud

  • Define the concept of a container and identify uses for containers
  • Identify the purpose of and use cases for Google Kubernetes Engine and Kubernetes
  • Lab: Kubernetes Engine

Module 6: Applications in the Cloud

  • Understand the purpose of and use cases for Google App Engine
  • Contrast the App Engine Standard environment with the App Engine Flexible environment
  • Understand the purpose of and use cases for Google Cloud Endpoints
  • Lab: App Engine

Module 7: Developing, Deploying, and Monitoring in the Cloud

  • Understand options for software developers to host their source code
  • Understand the purpose of template-based creation and management of resources
  • Understand the purpose of integrated monitoring, alerting, and debugging
  • Lab: Deployment Manager and Stackdriver

Module 8: Big Data and Machine Learning in the Cloud

  • Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms
  • Lab: BigQuery

Google Cloud Training Partners

 

Jumping Bean is an official Google Cloud training partner. All our instructors are official Google certified instructors. With years of hands-on training and in-field experience our trainers have a depth of knowledge and insight that will provide you with the understanding and knowledge  you need to master Google Cloud Platform.

Looking for a Google Cloud Course? Let Us Know!

Are you looking for a specific Google training  course but can't find it? Let us know and we will be happy to offer the course.  Call us on +2711-7818014 or use the contact form below.

Training That Suits You!

We offer online or classroom based training, part-time or full-time; what ever suits you we can accommodate.

Testimonials

What our alumni have to say about our training

Google Cloud Engineer Associate Training Courses

Google Cloud Engineer Associate Training

The recommended courses for Google Cloud Engineer Associate certification are:

After completing the courses above one is ready to write the Google Associate Cloud Engineer certification. For the Google Cloud Architect Professional certification one should also attend the "Architecting with Google Cloud: Design & Process"

Course Outlines

Title: Architecting with Google Compute Engine
Duration: 3 Days
Price: R15,000 (ex vat)

 

Module 1
Introduction to GCP

  • List the different ways of interacting with GCP
  • Use the GCP Console and Cloud Shell
  • Create Cloud Storage buckets
  • Use the GCP Marketplace to deploy solutions

Module 2
Virtual Networks

  • List the VPC objects in GCP
  • Differentiate between the different types of VPC networks
  • Implement VPC networks and firewall rules
  • Design a maintenance server

Module 3
Virtual Machines

  • Recall the CPU and memory options for virtual machines
  • Describe the disk options for virtual machines
  • Explain VM pricing and discounts
  • Use Compute Engine to create and customize VM instances

Module 4
Cloud IAM

  • Describe the Cloud IAM resource hierarchy
  • Explain the different types of IAM roles
  • Recall the different types of IAM members
  • Implement access control for resources using Cloud IAM

Module 5
Storage and Database Services

  • Differentiate between Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Firestore and Cloud Bigtable
  • Choose a data storage service based on your requirements Implement data storage services
  • Implement data storage services

Module 6
Resource Management

  • Describe the cloud resource manager hierarchy
  • Recognize how quotas protect GCP customers
  • Use labels to organize resources
  • Explain the behavior of budget alerts in GCP
  • Examine billing data with BigQuery

Module 7
Resource Monitoring

  • Describe the Stackdriver services for monitoring, logging, error reporting, tracing, and debugging
  • Create charts, alerts, and uptime checks for resources with Stackdriver Monitoring
  • Use Stackdriver Debugger to identify and fix errors

Module 8
Interconnecting Networks

  • Recall the GCP interconnect and peering services available to connect your infrastructure to GCP
  • Determine which GCP interconnect or peering service to use in specific circumstances
  • Create and configure VPN gateways
  • Recall when to use Shared VPC and when to use VPC Network Peering
  • Describe Cloud DNS

Module 9
Load Balancing and Autoscaling

  • Recall the various load balancing services
  • Determine which GCP load balancer to use in specific circumstances
  • Describe autoscaling behavior
  • Configure load balancers and autoscaling

Module 10
Infrastructure Automation

  • Automate the deployment of GCP services using Deployment Manager or Terraform
  • Outline the GCP Marketplace

Module 11
Managed Services

  • Describe the managed services for data processing in GCP

Title: Architecting with Google Kubernetes Engine
Duration: 3 Days
Price: R17,000 (ex vat)

​​​​​​

Module 1

  • Introduction to Google Cloud Platform
  • Use the Google Cloud Platform Console
  • Use Cloud Shell
  • Create GCP projects
  • Understand the differences among GCP compute platforms
  • Cloud Resource Manager, Quotas, Billing

Module 2

  • Launching Workloads in Kubernetes Engine
  • Understand the architecture of Kubernetes: pods, namespaces
  • Understand the components of Kubernetes
  • Create Docker containers using Google Container Builder
  • Store container images in Google Container Registry
  • Create a Kubernetes Engine cluster
  • Install software using Helm charts

Module 3

  • Debugging, Monitoring, Logging, Error Reporting
  • Introspect Kubernetes containers
  • View pod logs
  • Troubleshoot common Kubernetes problems
  • Use Stackdriver Kubernetes Monitoring
  • Use Prometheus monitoring with Stackdriver

Module 4

  • Scheduling and Autoscaling Workloads in Kubernetes Engine
  • Apply labels
  • Create and manage Deployments
  • Perform rolling upgrades and rollbacks of Deployments
  • Define Services
  • Expose Services with LoadBalancers and NodePorts
  • Run cron jobs
  • Control pod execution with taints and tolerations
  • Configure Kubernetes Engine clusters for autoscaling

Module 5

  • Kubernetes and Google Cloud VPC Networking Fundamentals
  • Understand the Kubernetes networking model
  • Understand how Kubernetes networking differs from Docker networking
  • Understand how Kubernetes networking differs from Compute Engine networking
  • Understand VPC networks and subnets
  • Understand load balancer types
  • Use Kubernetes DNS

Module 6

  • Persistent Data and Storage
  • Use Secrets to isolate security credentials
  • Use ConfigMaps to isolate configuration artifacts
  • Push out and roll back updates to Secrets and ConfigMaps
  • Configure Persistent Storage Volumes for Kubernetes Pods
  • Use StatefulSets to ensure that claims on persistent storage volumes persist across restarts

Module 7

  • Access Control and Security in Kubernetes and Kubernetes Engine
  • Understand Kubernetes authentication and authorization
  • Define Kubernetes RBAC roles and role bindings for accessing resources in namespaces
  • Define Kubernetes RBAC cluster roles and cluster role bindings for accessing cluster-scoped resources
  • Define Kubernetes pod security policies to only allow pods with specific security-related attributes to run
  • Define Kubernetes network policies to allow and block traffic to pods Understand the structure of GCP IAM
  • Define IAM roles and policies for Kubernetes Engine cluster administration
  • Decide between building one larger cluster and many smaller clusters

Module 8

  • Using GCP Managed Storage Services from Kubernetes Applications
  • Understand pros and cons for using a managed storage service versus self-managed containerized storage
  • Understand use cases for Cloud Storage, and use Cloud Storage from within a Kubernetes application
  • Understand use cases for Cloud SQL and Cloud Spanner and use them from within a Kubernetes application
  • Understand use cases for Cloud Datastore, and use Cloud Datastore from within a Kubernetes application
  • Understand use cases for Bigtable, and use Bigtable from within a Kubernetes application

Google Cloud Architect Professional Training

Google Solutions Architect Professional Training

The recommended course outline for the Google Cloud Architect Professional certification is to take the following courses:

To complete the Google Cloud Architect Professional certification an additional course to those required for the Google Cloud Engineer Associate certification. This is the "Architecting with Google Cloud: Design & Process" course.

Course Outline

Title: Architecting with Google Cloud: Design & Process
Duration: 2 Days
Price: R10,000 (ex vat)


Module 1: Defining the Service

  • Describe users in terms of roles and personas
  • Write qualitative requirements with user stories
  • Write quantitative requirements using key performance indicators (KPIs)
  • Evaluate KPIs using SLOs and SLIs
  • Determine the quality of application requirements using SMART criteria


Module 2: Microservice Design and Architecture

  • Decompose monolithic applications into microservices
  • Recognise appropriate microservice boundaries
  • Architect stateful and stateless services to optimise scalability and reliability
  • Implement services using 12-factor best practices
  • Build loosely coupled services by implementing a well-designed REST architecture
  • Design consistent, standard RESTful service APIs


Module 3: DevOps Automation

  • Automate service deployment using CI/CD pipelines
  • Leverage Cloud Source Repositories for source and version control
  • Automate builds with Cloud Build and build triggers
  • Manage container images with Google Container Registry
  • Create infrastructure with code using Deployment Manager and Terraform


Module 4: Choosing Storage Solutions

  • Choose the appropriate Google Cloud data storage service based on use case, durability, availability, scalability and cost
  • Store binary data with Cloud Storage
  • Store relational data using Cloud SQL and Spanner
  • Store NoSQL data using Firestore and Cloud Bigtable
  • Cache data for fast access using Memorystore
  • Build a data warehouse using BigQuery

Module 5: Google Cloud and Hybrid Network Architecture

  • Design VPC networks to optimise for cost, security, and performance
  • Configure global and regional load balancers to provide access to services
  • Leverage Cloud CDN to provide lower latency and decrease network egress
  • Evaluate network architecture using the Cloud Network Intelligence Center
  • Create hybrid networks between Google Cloud and on-premises data centers using Cloud Interconnect
 Module 6: Deploying Applications to Google Cloud
  • Choose the appropriate Google Cloud deployment service for your applications
  • Configure scalable, resilient infrastructure using Instance Templates and Groups
  • Orchestrate microservice deployments using Kubernetes and GKE
  • Leverage App Engine for a completely automated platform as a service (PaaS)
  • Create serverless applications using Cloud Functions

Module 7: Designing Reliable Systems

  • Design services to meet requirements for availability, durability, and scalability
  • Implement fault-tolerant systems by avoiding single points of failure, correlated failures, and cascading failures
  • Avoid overload failures with the circuit breaker and truncated exponential backoff design patterns
  • Design resilient data storage with lazy deletion
  • Analyse disaster scenarios and plan for disaster recovery using cost/risk analysis

Module 8: Security

  • Design secure systems using best practices like separation of concerns, the principle of least privilege, and regular audits
  • Leverage Cloud Security Command Center to help identify vulnerabilities
  • Simplify cloud governance using organisational policies and folders
  • Secure people using IAM roles, Identity-Aware Proxy, and Identity Platform
  • Manage the access and authorisation of resources by machines and processes using service accounts
  • Secure networks with private IPs, firewalls, and Private Google Access
  • Secure networks with private IPs, firewalls, and Private Google Access

Module 9: Maintenance and Monitoring

  • Manage new service versions using rolling updates, blue/green deployments, and canary releases
  • Forecast, monitor, and optimise service cost using the Google Cloud pricing calculator and billing reports and by analysing billing data
  • Observe whether your services are meeting their SLOs using Cloud Monitoring and Dashboards
  • Use Uptime Checks to determine service availability
  • Respond to service outages using Cloud Monitoring Alerts and processes using service accounts

Google Cloud Dataproc

Leveraging Unstructured Data with Cloud Dataproc on Google Cloud Platform

This course is part of the Google Data Engineer specialisation courses. In this course, you will learn to create and manage computing clusters to run Hadoop, Spark, Pig and/or Hive jobs on Google Cloud Platform. Learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning.

You will also learn how to access various cloud storage options from their compute clusters and integrate Google's machine learning capabilities into their analytics programs. It is 5 days of jam-packed theory, demos, and practicals that will arm you with all you need to know to master the Big data on Google Cloud Platform

Course Outline

Title: Leveraging Unstructured Data with Cloud Dataproc on Google Cloud Platform
Duration: 4 Days
Price:  R25,000 (ex vat)

Module 1 -Google Cloud Dataproc Overview

  • Creating and managing clusters.
  • Leveraging custom machine types and preemptible worker nodes.
  • Scaling and deleting Clusters.
  • Lab: Creating Hadoop Clusters with Google Cloud Dataproc.

Module 2 Running Dataproc Jobs

  • Running Pig and Hive jobs.
  • Separation of storage and compute.
  • Lab: Running Hadoop and Spark Jobs with Dataproc.
  • Lab: Submit and monitor jobs.

Module 3 Integrating Dataproc with Google Cloud Platform

  • Customize cluster with initialization actions.
  • BigQuery Support.
  • Lab: Leveraging Google Cloud Platform Services.

Module 4 Making Sense of Unstructured Data with Google’s Machine Learning APIs

  • Google’s Machine Learning APIs.
  • Common ML Use Cases.
  • Invoking ML APIs.
  • Lab: Adding Machine Learning Capabilities to Big Data Analysis.

Module 5 Serverless data analysis with BigQuery

  • What is BigQuery.
  • Queries and Functions.
  • Lab: Writing queries in BigQuery.
  • Loading data into BigQuery.
  • Exporting data from BigQuery.
  • Lab: Loading and exporting data.
  • Nested and repeated fields.
  • Querying multiple tables.
  • Lab: Complex queries.
  • Performance and pricing.

Module 6 Serverless, autoscaling data pipelines with Dataflow

  • The Beam programming model.
  • Data pipelines in Beam Python.
  • Data pipelines in Beam Java.
  • Lab: Writing a Dataflow pipeline.
  • Scalable Big Data processing using Beam.
  • Lab: MapReduce in Dataflow.
  • Incorporating additional data.
  • Lab: Side inputs.
  • Handling stream data.
  • GCP Reference architecture.

Serverless Machine Learning with TensorFlow on Google Cloud Platform:


Module 7 Getting started with Machine Learning

  • What is machine learning (ML).
  • Effective ML: concepts, types.
  • ML datasets: generalization.
  • Lab: Explore and create ML datasets.

Module 8 Building ML models with Tensorflow 

  • Getting started with TensorFlow.
  • Lab: Using tf.learn.
  • TensorFlow graphs and loops + lab.
  • Lab: Using low-level TensorFlow + early stopping.
  • Monitoring ML training.
  • Lab: Charts and graphs of TensorFlow training.

Module 9 Scaling ML models with CloudML

  • Why Cloud ML?
  • Packaging up a TensorFlow model.
  • End-to-end training.
  • Lab: Run a ML model locally and on cloud.

Module 10 Feature Engineering

  • Creating good features.
  • Transforming inputs.
  • Synthetic features.
  • Preprocessing with Cloud ML.
  • Lab: Feature engineering.

Building Resilient Streaming Systems on Google Cloud Platform:

Module 11 Architecture of streaming analytics pipelines

  • Stream data processing: Challenges.
  • Handling variable data volumes.
  • Dealing with unordered/late data.
  • Lab: Designing streaming pipeline.

Module 12 Ingesting Variable Volumes

  • What is Cloud Pub/Sub?
  • How it works: Topics and Subscriptions.
  • Lab: Simulator.

Module 13 Implementing streaming pipelines

  • Challenges in stream processing.
  • Handle late data: watermarks, triggers, accumulation.
  • Lab: Stream data processing pipeline for live traffic data.

Module 14 Streaming analytics and dashboards

  • Streaming analytics: from data to decisions.
  • Querying streaming data with BigQuery.
  • What is Google Data Studio?
  • Lab: build a real-time dashboard to visualize processed data.

Module 15  High throughput and low-latency with Bigtable

  • What is Cloud Spanner?
  • Designing Bigtable schema.
  • Ingesting into Bigtable.
  • Lab: streaming into Bigtable.

Google Data Engineering Training Course

Google Data Engineer Training

The Google Data Engineer Professional certification is a sought-after qualification that validates a holder's skills in architecting, maintaining, and tuning big data pipelines and processes. The recommended course for the Google Data Engineer Profesional certification are:

It is recommended that candidates who want to obtain this certification have the Google Cloud Architect Associate certification or have the requisite skills and knowledge required for holders of the certificate. It is not a mandatory requirement.

Course Outlines

Title: Google Cloud Big Data & Machine Learning Fundamentals
Duration: 1 Day
Price:  R8,000 (ex vat)

Module 1: Introducing Google Cloud Platform

  • Google Platform Fundamentals Overview.
  • Google Cloud Platform Big Data Products.
  • Lab: Sign up for Google Cloud Platform.

Module 2: Compute and Storage Fundamentals

  • CPUs on demand (Compute Engine).
  • A global file system (Cloud Storage).
  • Cloud Shell.
  • Lab: Set up an Ingest-Transform-Publish data processing pipeline.

Module 3: Data Analytics on the Cloud

  • 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 Spark on Dataproc.

Module 4: Scaling Data Analysis

  • Fast random access.
  • Datalab.
  • BigQuery.
  • Lab: Build a Machine Learning Dataset.

Module 5: Machine Learning

  • Machine Learning with TensorFlow.
  • Lab: Carry out ML with TensorFlow.
  • Pre-built models for common needs.
  • Lab: Employ ML APIs.

Module 6: Data Processing Architectures

  • Message-oriented architectures with Pub/Sub.
  • Creating pipelines with Dataflow.
  • Reference architecture for real-time and batch data processing.

Module 7: Summary

  • Why GCP?.
  • Where to go from here.
  • Additional Resources.

Title: Data Engineering on Google Cloud
Duration: 4 Day
Price:  R28,000 (ex vat)

Module 1: Introduction to Data Engineering

  • Explore the role of a data engineer.
  • Analyze data engineering challenges.
  • Intro to BigQuery.
  • Data Lakes and Data Warehouses.
  • Demo: Federated Queries with BigQuery.
  • Transactional Databases vs Data Warehouses.
  • Website Demo: Finding PII in your dataset with DLP API.
  • Partner effectively with other data teams.
  • Manage data access and governance.
  • Build production-ready pipelines.
  • Review GCP customer case study.
  • Lab: Analyzing Data with BigQuery.

Module 2: Building a Data Lake

  • Introduction to Data Lakes.
  • Data Storage and ETL options on GCP.
  • Building a Data Lake using Cloud Storage.
  • Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions.
  • Securing Cloud Storage.
  • Storing All Sorts of Data Types.
  • Video Demo: Running federated queries on Parquet and ORC files in BigQuery.
  • Cloud SQL as a relational Data Lake.
  • Lab: Loading Taxi Data into Cloud SQL.

Module 3: Building a Data Warehouse

  • The modern data warehouse.
  • Intro to BigQuery.
  • Demo: Query TB+ of data in seconds.
  • Getting Started.
  • Loading Data.
  • Video Demo: Querying Cloud SQL from BigQuery.
  • Lab: Loading Data into BigQuery.
  • Exploring Schemas.
  • Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA.
  • Schema Design.
  • Nested and Repeated Fields.
  • Demo: Nested and repeated fields in BigQuery.
  • Lab: Working with JSON and Array data in BigQuery.
  • Optimizing with Partitioning and Clustering.
  • Demo: Partitioned and Clustered Tables in BigQuery.
  • Preview: Transforming Batch and Streaming Data.

Module 4: Introduction to Building Batch Data Pipelines,

  • EL, ELT, ETL.
  • Quality considerations.
  • How to carry out operations in BigQuery.
  • Demo: ELT to improve data quality in BigQuery.
  • Shortcomings.
  • ETL to solve data quality issues.

Module 5: Executing Spark on Cloud Dataproc

  • The Hadoop ecosystem.
  • Running Hadoop on Cloud Dataproc.
  • GCS instead of HDFS.
  • Optimizing Dataproc.
  • Lab: Running Apache Spark jobs on Cloud Dataproc.

Module 6: Serverless Data Processing with Cloud Dataflow

  • Cloud Dataflow.
  • Why customers value Dataflow.
  • Dataflow Pipelines.
  • Lab: A Simple Dataflow Pipeline (Python/Java).
  • Lab: MapReduce in Dataflow (Python/Java).
  • Lab: Side Inputs (Python/Java).
  • Dataflow Templates.
  • Dataflow SQL.

Module 7: Manage Data Pipelines with Cloud Data Fusion and Cloud Composer

  • Building Batch Data Pipelines visually with Cloud Data Fusion.
  • Components.
  • UI Overview.
  • Building a Pipeline.
  • Exploring Data using Wrangler.
  • Lab: Building and executing a pipeline graph in Cloud Data Fusion.
  • Orchestrating work between GCP services with Cloud Composer.
  • Apache Airflow Environment.
  • DAGs and Operators.
  • Workflow Scheduling.
  • Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery.
  • Monitoring and Logging.
  • Lab: An Introduction to Cloud Composer.

Module 8: Introduction to Processing Streaming Data

  • Processing Streaming Data.

Module 9: Serverless Messaging with Cloud Pub/Sub

  • Cloud Pub/Sub.
  • Lab: Publish Streaming Data into Pub/Sub.

Module 10: Cloud Dataflow Streaming Features

  • Cloud Dataflow Streaming Features.
  • Lab: Streaming Data Pipelines.

Module 11: High-Throughput BigQuery and Bigtable Streaming Features

  • BigQuery Streaming Features.
  • Lab: Streaming Analytics and Dashboards.
  • Cloud Bigtable.
  • Lab: Streaming Data Pipelines into Bigtable.

Module 12: Advanced BigQuery Functionality and Performance

  • Analytic Window Functions.
  • Using With Clauses.
  • GIS Functions.
  • Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz.
  • Performance Considerations.
  • Lab: Optimizing your BigQuery Queries for Performance.
  • Optional Lab: Creating Date-Partitioned Tables in BigQuery.

Module 13: Introduction to Analytics and AI

What is AI?.
From Ad-hoc Data Analysis to Data-Driven Decisions.
Options for ML models on GCP.

Module 14: Prebuilt ML model APIs for Unstructured Data

  • Unstructured Data is Hard.
  • ML APIs for Enriching Data.
  • Lab: Using the Natural Language API to Classify Unstructured Text.

Module 15: Big Data Analytics with Cloud AI Platform Notebooks

  • What's a Notebook.
  • BigQuery Magic and Ties to Pandas.
  • Lab: BigQuery in Jupyter Labs on AI Platform.

Module 16: Production ML Pipelines with Kubeflow

  • Ways to do ML on GCP.
  • Kubeflow.
  • AI Hub.
  • Lab: Running AI models on Kubeflow.

Module 17: Custom Model building with SQL in BigQuery ML

  • BigQuery ML for Quick Model Building.
  • Demo: Train a model with BigQuery ML to predict NYC taxi fares.
  • Supported Models.
  • Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML.
  • Lab Option 2: Movie Recommendations in BigQuery ML.

Module 18: Custom Model building with Cloud AutoML

  • Why Auto ML?
  • Auto ML Vision.
  • Auto ML NLP.
  • Auto ML Tables.

Contact Us Anchor Tag

Contact Form

Jumping Bean Contact Form!