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Advanced Machine Learning with TensorFlow on Google Cloud Platform

Name: Advanced Machine Learning with TensorFlow on Google Cloud Platform
Availability: InStock
Rating: 4.6 (5878 reviews)

This course will give you hands-on experience optimizing, deploying, and scaling a variety of production ML models. You’ll learn how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text, along with recommendation systems.

4.6 out of 5 rating
Last updated : 23/07/2026
English

Available as Instructor Led Training, Live Online & In Person at your Offices or Ours.

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Download Overview

Duration:

40.00 hours

32.5 CPD hours

Overview:

Description:

Course Outline:

Module 1: Machine Learning on Google Cloud Platform

- Effective ML
- Fully Managed ML
Module 2: Explore the Data

- Exploring the Dataset
- BigQuery
- BigQuery and AI Platform Notebooks
Module 3: Creating the Dataset

- Creating a Dataset
Module 4: Build the Model

- Build the Model
Module 5: Operationalize the Model

- Operationalizing the Model
- Cloud AI Platform
- Train and Deploy with Cloud AI Platform
- BigQuery ML
- Deploying and Predicting with Cloud AI Platform
Module 6: Architecting Production ML Systems

- The Components of an ML System
- The Components of an ML System: Data Analysis and Validation
- The Components of an ML System: Data Transformation + Trainer
- The Components of an ML System: Tuner + Model Evaluation and Validation
- The Components of an ML System: Serving
- The Components of an ML System: Orchestration + Workflow
- The Components of an ML System: Integrated Frontend + Storage
- Training Design Decisions
- Serving Design Decisions
- Designing from Scratch
Module 7: Ingesting Data for Cloud-Based Analytics and ML

- Data On-Premises
- Large Datasets
- Data on Other Clouds
- Existing Databases
Module 8: Designing Adaptable ML Systems

- Adapting to Data
- Changing Distributions
- Right and Wrong Decisions
- System Failure
- Mitigating Training-Serving Skew Through Design
- Debugging a Production Model
Module 9: Designing High-Performance ML Systems

- Training
- Predictions
- Why Distributed Training?
- Distributed Training Architectures
- Faster Input Pipelines
- Native TensorFlow Operations
- TensorFlow Records
- Parallel Pipelines
- Data Parallelism with All Reduce
- Parameter Server Approach
- Inference
Module 10: Hybrid ML Systems

- Machine Learning on Hybrid Cloud
- KubeFlow
- Embedded Models
- TensorFlow Lite
- Optimizing for Mobile
Module 11: Welcome to Image Understanding with TensorFlow on GCP

- Images as Visual Data
- Structured vs. Unstructured Data
Module 12: Linear and DNN Models

- Linear Models
- DNN Models Review
- Review: What is Dropout?
Module 13: Convolutional Neural Networks (CNNs)

- Understanding Convolutions
- CNN Model Parameters
- Working with Pooling Layers
- Implementing CNNs with TensorFlow
Module 14: Dealing with Data Scarcity

- The Data Scarcity Problem
- Data Augmentation
- Transfer Learning
- No Data, No Problem
Module 15: Going Deeper Faster

- Batch Normalization
- Residual Networks
- Accelerators (CPU vs GPU, TPU)
- TPU Estimator
- Neural Architecture Search
Module 16: Pre-built ML Models for Image Classification

- Pre-Built ML Models
- Cloud Vision API
- AutoML Vision
- AutoML Architecture
Module 17: Working with Sequences

- Sequence Data and Models
- From Sequences to Inputs
- Modeling Sequences with Linear Models
- Modeling Sequences with DNNs
- Modeling Sequences with CNNs
- The Variable-Length problem
Module 18: Recurrent Neural Networks

- Introducing Recurrent Neural Networks
- How RNNs Represent the Past
- The Limits of What RNNs Can Represent
- The Vanishing Gradient Problem
Module 19: Dealing with Longer Sequences

- LSTMs and GRUs
- RNNs in TensorFlow
- Deep RNNs
- Improving our Loss Function
- Working with Real Data
Module 20: Text Classification

- Working with Text
- Text Classification
- Selecting a Model
- Python vs Native TensorFlow
Module 21: Reusable Embeddings

- Historical Methods of Making Word Embeddings
- Modern Methods of Making Word Embeddings
- Introducing TensorFlow Hub
- Using TensorFlow Hub Within an Estimator
Module 22: Recurrent Neural NetworksEncoder-Decoder Models

- Introducing Encoder-Decoder Networks
- Attention Networks
- Training Encoder-Decoder Models with TensorFlow
- Introducing Tensor2Tensor
- AutoML Translation
- Dialogflow
Module 23: Recommendation Systems Overview

- Types of Recommendation Systems
- Content-Based or Collaborative
- Recommendation System Pitfalls
Module 24: Content-Based Recommendation Systems

- Content-Based Recommendation Systems
- Similarity Measures
- Building a User Vector
- Making Recommendations Using a User Vector
- Making Recommendations for Many Users
- Using Neural Networks for Content-Based Recommendation Systems
Module 25: Collaborative Filtering Recommendation Systems

- Types of User Feedback Data
- Embedding Users and Items
- Factorization Approaches
- The ALS Algorithm
- Preparing Input Data for ALS
- Creating Sparse Tensors For Efficient WALS Input
- Instantiating a WALS Estimator: From Input to Estimator
- Instantiating a WAL Estimator: Decoding TFRecords
- Instantiating a WALS Estimator: Recovering Keys
- Instantiating a WALS Estimator: Training and Prediction
- Issues with Collaborative Filtering
- Cold Starts
Module 26: Neural Networks for Recommendation Systems

- Hybrid Recommendation System
- Context-Aware Recommendation Systems
- Context-Aware Algorithms
- Contextual Postfiltering
- Modeling Using Context-Aware Algorithms
Module 27: Building an End-to-End Recommendation System

- Architecture Overview
- Cloud Composer Overview
- Cloud Composer: DAGs
- Cloud Composer: Operators for ML9
- Cloud Composer: Scheduling
- Cloud Composer: Triggering Workflows with Cloud Functions
- Cloud Composer: Monitoring and Logging

Available Delivery Options for Advanced Machine Learning with TensorFlow on Google Cloud Platform

Live Instructor Led Classroom Online (Live Online)
Traditional Instructor Led Classroom training (TILT/ILT)
Delivery at your offices in United Kingdom
Private dedicated course for your staff

How many CPD hours does Advanced Machine Learning with TensorFlow on Google Cloud Platform provide?

The 5 day Advanced Machine Learning with TensorFlow on Google Cloud Platform course gives you up to 32.5 CPD hours/structured learning hours.

Who is the correct audience for Advanced Machine Learning with TensorFlow on Google Cloud Platform?

- Data Engineers and programmers interested in learning how to apply machine learning in practice
- Anyone interested in learning how to leverage machine learning in their enterprise

Do you provide training for Advanced Machine Learning with TensorFlow on Google Cloud Platform?

Yes, we provide corporate training, dedicated training and closed classes for Advanced Machine Learning with TensorFlow on Google Cloud Platform. This can take place anywhere in United Kingdom including London, Manchester, Birmingham, Edinburgh or live online.

What is the duration of Advanced Machine Learning with TensorFlow on Google Cloud Platform?

The Advanced Machine Learning with TensorFlow on Google Cloud Platform training takes place over 5 day(s), with each day lasting approximately 40.00 hours including breaks.

What other terms do people search for?

Popular related searches include: Advanced Machine Learning with TensorFlow on Google Cloud Platform, AI & Data Science, MLTF

Why are Nexus Human the best provider for Advanced Machine Learning with TensorFlow on Google Cloud Platform?

Nexus Human are recognised as one of the best training companies. Our trainers have won awards including Small Firms Best Trainer, national training partner of the year for Ireland, and global top 30 instructor awards in 2012, 2019 and 2021.

Is there a discount code for Advanced Machine Learning with TensorFlow on Google Cloud Platform?

Yes, the discount code PENPAL5 is currently available for Advanced Machine Learning with TensorFlow on Google Cloud Platform training. Only one discount code can be used per booking.

DURATION 1 Day

No scheduled classes available at this time.

Training Insurance Included!

When you organise training, we understand that there is a risk that some people may fall ill, become unavailable.

To mitigate the risk we include training insurance for each delegate enrolled on our public schedule, they are welcome to sit on the same Public class within 6 months at no charge, if the case arises.