Full Stack Deep Learning in AWS (Cohort 11)

Mar 29, 10:00AM PDT(17:00 GMT) | Mon,Wed
Student Reviews
Many deep learning course cover theoretical techniques of algorithms and modeling. In this course, we will train you to become a Full Stack Deep Learning Engineer, capable of not just training models but also deploying and managing them in production for business value.
You will learn deep learning primarly through building 8 production grade services, step by step. You will learn how to build production AI in AWS, how to integrate it with an application, and how to manage it through its lifecycle.
You will also build from scratch a custom project (where you will gather image data, train and tune a production grade neural network, build a prediction service, and connect that prediction service to create your own image processing AI application).

Students who take this course will be able to:

  • Identify and frame business use cases that can be solved by deep learning
  • Choose the right techniques, tools, frameworks to the business use cases
  • Build production AI on AWS, and manage through their lifecycle.
  • Hands on implementation of end to end AI for each use cases
    • Session 1: Mar 29, 10am-11:30am PDT ((US Pacific Time Zone, Daylight Saving Time, GMT-7)
    • Session 2: Mar 31, 10am-11:30am PDT
    • Session 3: Apr 5, 10am-11:30am PDT
    • Session 4: Apr 7, 10am-11:30am PDT
    • Session 5: Apr 12, 10am-11:30am PDT
    • Session 6: Apr 14, 10am-11:30am PDT
    • Session 7: Apr 19, 10am-11:30am PDT
    • Session 8: Apr 21, 10am-11:30am PDT

    • 4 weeks / 8 sessions / 12 hours
    • 8 lectures / 4 hands-on projects
    • Capstone project, Github portfolio
    • Live Sessions (with zoom), Real time interaction
    • Slack support in and after class

    Check the Syllabus tab for full course content.

    Developers, data scientists, students.

  • Basic familiarity with deep learning
  • Bring an AWS account (you can sign up one for free at AWS). Attendees can use their own AWS account to run the hands on labs and have all artifacts (models, datasets) in their account for further use.
  • No coding prerequisite. We will provide self paced exercises where attendees, if interested, can code in Python, Java or Javascript.
  • Difficulty Level
    Est. time spend per week: 3 hours live class (required) + 3 hours homework (required) + 3 hours projects (bonus, optional).

    Full refund upon request before the first session ends (Mar 29th, 2021 12PM PDT). 5% transaction fee is not refundable.

    If missed live sessions, you can watch recordings any time, along with interactive learning tools, slides, course notes
    Students have life time access to course materials

  • Earn Certificate of Completion
  • Financial aid is available for application, contact for details
  • Cohort 10: Jan 18 ~ Feb 10, 2021
  • Cohort 9: Nov 3 ~ Nov 26, 2020
  • Cohort 8: Sep 15 ~ Oct 8, 2020
  • Cohort 7: Jul 27 ~ Aug 19, 2020
  • Cohort 6: Jun 15 ~ Jul 8, 2020
  • Cohort 5: May 11 ~ Jun 3, 2020
  • Cohort 4: Mar 17 ~ Apr 9, 2020
  • Cohort 3: Jan 31, 2020
  • Cohort 2: Jan 28 ~ Feb 20, 2020
  • Cohort 1: Oct 30 ~ Nov 22, 2019
  • This course includes 8 hands on workshops, and each workshop will cover:
  • How AI works for images/Time series, and an overview of Deep Learning
  • How to build a production Deep Learning AI (Bringing Images/Time series into AWS, Model Training, Model Validation, Endpoints, Gateway/Lambda Integration, Application Integration).
  • One or more AWS tools. Across the 8 sessions we will cover AWS Sagemaker, AWS Sagemaker BuiltIn Algorithms, AWS Sagemaker with with Transfer Learning for Neural Networks, How to select and use GPU instances in AWS, AWS Sagemaker Endpoint, AWS Lambda, AWS API Gateway, AWS Roles and Authentication, AWS Cloudwatch, AWS S3, Python based application integration and testing, Production AI Best Practices. Microservice design pattern for AI deployment. We will also cover Navigator, a life cycle overlay tool that makes AWS tools easier to configure and use.
  • You will build and test Image Classifiers and your custom project.

  • You will learn how to use these Production AI Cloud Tools:
  • How to configure and use S3 for your data
  • How to bring your data into AWS Sagemaker for Images and Deep Learning
  • How to configure and use AWS Sagemaker. Deep dive on built in AWS Sagemaker built in support for CNN/ResNet and Transfer Learning. How to hyper-parameter tune Sagemaker algorithms for Deep Learning.
  • How to configure and use GPU based AWS compute instances for Deep Learning.
  • Configuring and using a Sagemaker Endpoint.
  • Connecting a Sagemaker Endpoint to a public URL via AWS Gateway and Lambda.
  • Integrating REST microservices with applications. Using Python for API testing with images.
  • Cloud AWS best practices. Cloudwatch for logs, managing endpoints.
  • Navigator for ease of use. How to use Navigator and AWS together.

  • AI Algorithms, Algorithm Internals and Deep Learning Technical Concepts
  • How to build and use production grade DL. How to select an algorithm for a use case, train, deploy and use it in production, and measure how well it is doing.
  • Powerful general purpose algorithms - CNN for images and Transfer Learning for acceleration, and how they work internally and how to hyper-parameter tune them for best performance.
  • Metrics and practices for algorithmic evaluation.
  • An intro to advanced aspects of Production AI - live monitoring and diagnosis, model versioning, retraining and others.
  • Your custom project:

  • In Sessions 1 and 2 we will give you an overview of how you will develop your custom project, and some project ideas. You should ideally select your project topic by session 3
  • Over the remaining sessions, we will devote a fraction of each session to custom project work. You will also be able to interact with the instructors and your fellow students over Slack to discuss any aspect of your project as you need to.
  • We will provide guidance as you (a) gather data for your project and prepare it for use in AWS, (b) train a Deep Learning algorithm for your project (c) Build a prediction microservice for your project and (d) integrate your prediction service into a Python program to create your custom application. We will provide hosting facilities where you can host your completed project, connect it to a github repo, and showcase it to future employers or peers. If you choose to, you can also continue to work on your project in your AWS account and improve it further after the workshop ends. All project resources will be available.
  • Module 1: How to build and run a production DL in the cloud - Image Classification
    In this first session, we will show the steps needed to build and run a production DL in the cloud. We will illustrate these steps with Image Classification. In the code lab, the attendees will build and test their own A using provided sample data and learn how to configure S3 in their AWS account.
    What attendees will learn:
    • Overview of the production AI lifecycle and all of its steps. How to go from data to running production AI
    • Description of cloud services that can be used for each stage
    • Overview of an image classification problem and how to build an AI for it with AWS with Image Classification Algorithms
    • Lab 1: Get your AWS account started and use S3. We will demo an end to end lifecycle for images. If time permits, students will start on Sagemaker.

    Module 2: AWS Sagemaker for Images and Deep Learning
    This session builds upon the first. We cover the basics of Deep Learning and continue our code lab of building an Image Classifier in AWS. We will use AWS Built In algorithms for Deep Learning. In the code lab, attendees will configure AWS Deep Learning Convolutional Neural Network Algorithms and select GPU based AWS Compute Instances for Training.
    What the attendees will learn:
    • Use Case: Image Classification. We will also share project ideas.
    • Algorithms/Concepts: Deep Learning. Images processing for AWS Sagemaker.
    • Production AI: Data storage in S3, Image processing for Training.
    • Lab 2: We will configure AWS Sagemaker for DL training. All models and artifacts will be in the attendee’s AWS account for their further use.

    Module 3: Hyper Parameter Tuning Deep Learning in AWS Sagemaker
    In this session, we show how to hyper parameter tune DL algorithms trained in the cloud. We will cover Hyper Parameters for Convolutional Neural Networks in CNN. Attendees will also configure a ResNet for their Image Classification. In code lab, we will continue the AWS DL lifecycle and complete the Sagemaker training and hyper parameter configuration. If time permits, we will move on to Sagemaker Endpoints. Students will also decide on their projects and share their project idea online in a shared document.
    What the attendees will learn:
    • Use Case: Image classification. Students will also choose their custom project idea./li>
    • Algorithms/Concepts: Hyper Parameter Tuning for Deep Learning in AWS.
    • Production AI: How to retrain a production DL AI with new information and how to iteratively deploy increasingly accurate models.
    • Lab 3: Hyper Parameter tuning for DL in Sagemaker. If time permits, we will get started on Sagemaker Endpoints.

    Module 4: Endpoints in Sagemaker and AWS Lambda, Going from Model to Prediction Service
    In this session, we will continue our development of the Production DL Lifecycle. You will learn how to take your trained DL model from previous sessions and create a working production grade microservice for Predictions. You will also make progress on your projects, and create your first custom dataset.
    What the attendees will learn:
    • Use Case: Images Classification, Creating a Prediction Service
    • Algorithms/Concepts: How to build a Prediction Service for Deep Learning Predictions. How to assess Your prediction service
    • Production AI: How to configure and EndPoint and create a publicly accessible URL.
    • Lab 4: Sagemaker Endpoints, AWS Lambda and AWS API Gateway. Students will also create and upload their first dataset for their custom project.

    Module 5: Time-series forecasting and feature engineering
    In this session, we will show you how to think about time-series forecasting and the basic concepts of regression. We also show you how to convert a dataset into a format acceptable by SageMaker. We will also cover the basic concepts of Recurrent Neural Networks..
    What the attendees will learn:
    • Use Case: Time-series forecasting: You will be provided with 2 use cases of electricity consumption data.
    • Algorithms/Concepts: Recurrent neural networks and basics of regression.
    • Lab 5: Students will convert the two publicly available datasets into a format that can be consumed by an AWS forecasting algorithm

    Module 6: DeepAR Algorithm: Training and hyper-parameters
    In this session, we cover the basics concepts of an LSTM and the parameters of a DeepAR algorithm in this context. Code lab will build on the previous session, where the you will configure a time-series forecasting training job and evaluate its training performance
    What the attendees will learn:
    • Use Case: Time-series data. Configuring a DeepAR algorithm, iterative tuning of the hyper-parameters
    • Algorithms/Concepts: DeepAR hyper-parameters
    • Production AI: Your custom project
    • Lab 6: Training job with DeepAR.

    Module 7: Time-series Forecasting: Deployment and evaluation
    In this session, we will build on the previous session by deploying this service into production and evaluate its performance using regression metrics. Students will also learn the details of how a DeepAR algorithm works and what context to use it in. Students will also interact with the deployed service using a python snippet..
    What the attendees will learn:
    • Use Case: Time series dataset. AWS lambda code for it
    • Algorithms/Concepts: Metrics to evaluate the forecasting model.
    • Production AI: AWS Lambda, API Gateway and Python scripts
    • Lab 7: Continue on Custom Project. Build your project’s prediction service and integrate it with a Python Application.

    Module 8: Advanced topics in Production Cloud AI, Complete and showcase your project.
    We will provide an overview of advanced topics that attendees can explore beyond this webinar series. These include Model Versioning, interactions between application versioning and model versioning, diagnostics of production AI in the presence of data changes, A/B testing, and others. In the code lab, students will complete their custom project and create a github and project repository to showcase their project. They can also create a project video if they choose to and put it in a hosting facility that we will provide..
    What attendees will learn:
    • Best practices for Production AI (model versioning, model integrity, retraining cycles, microservice API management and versioning). How to manage production application changes and production AI upgrades in concert.
    • Best practices for Cloud AI (debugging, instances, your cloud bill :-)).
    • Lab 8: Finish your custom project

    Sindhu Ghanta
    Head of Machine Learning in Pyxeda. She was a Post-Doctoral Fellow with BIDMC and the Department of Pathology, Harvard Medical School, where she was involved in detection and classification of features from histopathological (breast cancer) images. She worked as a research scientist with Parallel Machines on monitoring the health of machine learning algorithms in production and has many publications on ML innovations.

    Nisha Talagala
    Founder of Pyxeda AI. Previously, Nisha co-founded ParallelM which pioneered the MLOps practice of managing machine learning in production.

    Yamuna Dulanjani
    Senior Machine Learning Engineer at Pyxeda AI

    • Start Date: Mar 29, 10:00PDT | Mon,Wed
    • Venue: Online (zoom)
    • Fee:
      $399 $399 USD
    • Status: Course Ended
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