Self-Paced Online

Format

6 Months

Duration

12+

Projects

Intel Corporation

Certificate By

About the Course

The Intel® Distribution of OpenVINO™ toolkit helps developers achieve fast, accurate, real-world results using high-performance AI and computer vision inference deployed from the edge to the cloud.

In this course, you will learn to create high-performance, deep learning applications easily with a streamlined development workflow. You will learn about running inference tools for low precision optimizations, computer vision libraries, media processing, and pre-optimized kernels. We will also teach you how to quickly deploy your AI applications productively across combinations of host processors and accelerators, including CPUs, GPUs, and VPUs, on-prem, on-device, and in the browser or cloud using the write once deploy anywhere approach.

Throughout this course, you will learn how developers use the OpenVINO™ toolkit on multiple Intel® architectures to enable new and enhanced use cases across industries, including manufacturing, health and life sciences, retail, security, and more.

This course with OpenVINO™ toolkit is focused on developing deep learning inference applications and not model training. OpenVINO™ toolkit provides a set of pre-trained models that you can use for learning and demo purposes or for developing deep learning software.

With this certification course you will also get complimentary access to CloudxLab courses on Neural Networks and Computer Vision which will help you in gaining expertise in developing Computer Vision applications using Python*, Keras*, TensorFlow*, OpenCV* and Convolutional Neural Networks.

Program Highlights

  • Certificate by Intel Corporation

  • 6 Months of Self-Paced Learning

  • Gain practical experience by working on projects

  • Access to Intel® DevCloud for the Edge to develop, test and run your AI workloads

  • Access to online lab by CloudxLab to practice building Computer Vision applications

  • Timely Doubts Resolution

Course Certificates

What is the certificate like?

  • About Intel and OpenVINO™ toolkit

    Intel creates world-changing technology that enables global progress and enriches lives. By embedding intelligence in the cloud, network, edge, and every kind of computing device, we enable developers to unleash the potential of data to transform business and society for the better. Our legacy of democratizing technology continues by making it simpler, faster, and more cost-effective for everyone to infuse AI into their apps, everywhere that computing takes place.

    The Intel® Distribution of OpenVINO™ toolkit is a software tool that helps developers harness the full potential of AI to deploy high-performance deep learning applications on multiple processors, accelerators, and environments with a write once, deploy anywhere efficiency. Partner with us to deliver cutting-edge solutions by leveraging rapidly advancing hardware technologies with Intel® software.

  • Why Cloudxlab?

    CloudxLab is a team of developers, engineers, and educators passionate about building innovative products to make learning fun, engaging, and for life. We are a highly motivated team who build fresh and lasting learning experiences for our users. Powered by our innovation processes, we provide a gamified environment where learning is fun and constructive. From creative design to intuitive apps we create a seamless learning experience for our users. We upskill engineers in deep tech - make them employable & future-ready.

CloudxLab Advantage

hands-on lab
  • Gamified Learning Platform


  • Auto Assessments of your Work


  • No Installation Required

Mentors / Faculty

Instructor KC Rich

KC Rich

Software Program Manager, Internet of Things Group, Developer Enabling Division, Intel Corporation

Instructor Pooja Baraskar

Pooja Baraskar

Developer Programs and Evangelism Lead, Asia Pacific and Japan, Intel

Instructor Daniel Holmlund

Daniel Holmlund

Software Engineer- Internet of Things Group, Intel

Instructor Nihal Winston D’Cunha

Nihal Winston D’Cunha

Deep Learning Application Engineer, Intel

Instructor Jayakrishna Alapati

Jayakrishna Alapati

Senior Deep learning Application Engineer, Intel

Instructor Shane Ye

Shane Ye

Developer Enabler at Intel Corporation, Internet of Things Group, Developer Enabling Division

Instructor Stewart Christie

Stewart Christie

Community Manager, Global IOT Developer Program- Intel

Instructor Sandeep Giri

Sandeep Giri

Founder at CloudxLab

Past: Amazon, InMobi, D.E.Shaw

Instructor Abhinav Singh

Abhinav Singh

Co-Founder at CloudxLab

Past: Byjus

Instructor Praveen

Praveen Pavithran

Co-Founder at Yatis

Past: YourCabs, Cypress Semiconductor

Course Curriculum

Curriculum of Complimentary Courses by CloudxLab

Foundation Course
1. Programming Tools and Foundational Concepts
1. Programming Tools and Foundational Concepts
1. Linux* for Data Science
2. Getting Started with Git*
3. Python* Foundations
4. Machine Learning Prerequisites(Including Numpy*, Pandas* and Linear Algebra)
5. Getting Started with SQL
6. Analytics and Data Sciences
Computer Vision Course
1. OpenCV*
1. Introduction to OpenCV*
2. OpenCV* Basics
3. OpenCV* Basic Image Processing
4. OpenCV* Histograms
5. Blurring with OpenCV*
6. Thresholding with OpenCV*
7. Detecting moving objects in Video with OpenCV*
8. Edge Detection with OpenCV*
2. Introduction to Artificial Neural Networks
1. From Biological to Artificial Neurons
2. Implementing MLPs using Keras* with TensorFlow* Backend
3. Fine-Tuning Neural Network Hyperparameters
3. Training Deep Neural Networks
1. The Vanishing / Exploding Gradients Problems
2. Reusing Pretrained Layers
3. Faster Optimizers
4. Avoiding Overfitting Through Regularization
5. Practical Guidelines to Train Deep Neural Networks
4. Custom Models and Training with Tensorflow*
1. A Quick Tour of TensorFlow*
2. Customizing Models and Training Algorithms
3. Tensorflow* Functions and Graphs
5. Loading and Preprocessing Data with TensorFlow*
1. Introduction to the Data API
2. TFRecord Format
3. Preprocessing the Input Features
4. TF Transform
5. The TensorFlow* Datasets (TDFS) Projects
6. Convolutional Neural Networks
1. The Architecture of the Visual Cortex
2. Convolutional Layer
3. Pooling Layer
4. CNN Architectures
5. Classification with Keras*
6. Transfer Learning with Keras*
7. Object Detection
8. YOLO

Curriculum of Developing Deep Learning Application Course by Intel

1. Programming Tools and Foundational Concepts
Module 1: Introduction to AI and OpenVINO™ Toolkit
1. AI market overview and use cases of Deep Learning
2. Deep Learning development cycle - History, performance, and motivation
3. Challenges in developing and deployment of Deep Learning applications at the Edge and while working with various Hardware Platforms
4. Intel® Distribution of OpenVINO™ toolkit overview, its components and development workflow
5. Creating an AI inference in few lines of code using OpenVINO™ toolkit
6. Simplify and Develop for the Edge with Intel® AI Ecosystem and the various hardware and software offerings
Module 2: Setting up the Development environment for AI with OpenVINO™ Toolkit
1. AI prototyping with OpenVINO™ toolkit using Intel® DevCloud for the edge
2. Run sample applications and evaluate performance on various Intel® architecture with Intel® Devcloud for the edge
3. OpenVINO™ toolkit installation and setup on a local machine
4. Getting started in 5 minutes with OpenVINO™ toolkit using Jupyter* Notebooks
5. OpenVINO™ toolkit opensource version- Clone, Compile and Contribute
6. Intel® Edge Software Hub, a one-stop resource for optimized software and offerings for key edge use cases
Module 3: Expedite development of high-performance deep learning inference applications with Intel® Open Model Zoo
1. Introduction to Open Model Zoo and download a pre-trained model for learning or for developing deep learning software
2. Analyze various pre-trained models and public models and select an appropriate model based on the use case
3. Tools overview - Model downloader and Accuracy checker
4. Demos demonstrating various use cases with pre-trained and public models
Module 4: Optimization and Quantization of models for better performance
1. Introduction to Model Optimizer and understand its significance
2. Learn how model optimizer helps in streamlining the AI application development flow
3. Model conversion overview with Model Optimizer and understanding general and framework-specific conversion parameters
4. Generating Intermediate Representation files using Model Optimizer
5. Convert model with general and framework-specific conversion parameters
6. Understanding how the model optimizer works under the hood
7. Choosing the right Quantization option in Model Optimizer based on Hardware Platform and assessing the benefits and trade-offs of using different Quantization
8. Introduction to low precision optimization with Post-Training Optimization Tool and Understand the advantages of INT8 calibration
9. Learn various methods to boost model performance with Post-Training Optimization Tool
Module 5: Inference Engine & integration with deep learning applications
1. Understanding various Intel® architecture - CPU, iGPU, VPU to achieve accelerated performance
2. Introducing Intel® Movidius™ Myriad™ X VPU and getting started with Inference on Intel® Neural Compute Stick 2
3. Hardware agnostic inference with write once deploy anywhere approach
4. Creating powerful, scalable, and futureproof AI applications with a streamlined Development Workflow using Inference Engine
5. Performance improvement using SYNC and ASYNC modes of the Inference Engine
6. Efficient utilization of all the available compute resources using Heterogeneous and Multidevice Plug-ins to boost the performance
Module 6: Advanced Labs
1. Writing job file in Intel® Devcloud for the Edge and inference on multiple nodes
2. OpenVINO™ toolkit beyond vision- Speech, OCR, and NLP
3. Using the benchmark app to estimate the inference performance of your deep learning model on various devices
4. Advanced video analytics- Multiple models in one application
5. Local Project Migration with Intel® DevCloud for the Edge
6. Advanced samples and demos
Module 7: Streamline AI Application Development with Deep Learning Workbench
1. Introduction to Deep Learning Workbench and identifying the benefits
2. Learn to create optimized AI applications using a web-based graphical environment
3. Explore key concepts and features of Deep Learning Workbench
4. Getting familiar with Deep Learning Workbench workflow
5. Getting started with Deep Learning Workbench and identify the difference between running the Deep Learning Workbench on your local system and in the Intel® DevCloud for the Edge
6. Achieve the goal of optimized AI applications, whether you are new to deep learning or an advanced deep learning developer with Deep Learning Workbench
7. Develop and deploy optimized AI applications with Deep Learning Workbench
Module 8: Speed up the solution development journey with Intel® Edge Software Hub
1. Accelerate innovation for key edge computing use cases and starting with a powerful foundation for data processing, image processing, and edge AI analytics
2. Selecting the vertical-specific software package, customize the configurations to your needs, and decide on your target hardware for deployment
3. Explore the Reference Implementations to see how businesses are deploying edge intelligence in the real world
4. Downloading a Reference Implementation and Customizing it according to your requirements
5. Simplifying the edge-to-cloud workflow integration with Intel® Edge Software Hub

Projects in Developing Deep Learning Application Course by Intel

Projects in Complimentary Course by CloudxLab

Enroll Now

  1. Certification Guideline for Intel Certificate
    Complete atleast 80% of course offered by Intel along with mandatory projects
  2. Certification Guideline for CloudxLab Certificate
    Complete atleast 80% of course offered by CloudxLab along with mandatory projects
  3. Share your achievement
    Highlight your skills on your resume, LinkedIn*, Facebook* and Twitter*. Tell your friends and colleagues about it.

Pay in 2 Installments at

149*/Installment

Or Program Fee 298*

  • 6 Months of Online Self-Paced Training
  • Access to Intel® DevCloud for the Edge
  • Access to Online Lab by CloudxLab
  • Certificate from Intel and CloudxLab
Enroll Now

  • *Additional Taxes Applicable (If Any)

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Frequently Asked Questions

I'm already a machine learning engineer. How this course will help me? What makes this course unique? What I am going to learn in this course?

This course will guide you:

• To develop Deep Learning applications seamlessly.

• Optimize and improve performance with and without external accelerators

• Run high-performance inference with a “Write Once and Deploy Anywhere” approach.

• Deploy your same application across combinations of host processors, accelerators, and environments, including CPUs, GPUs, VPUs, on-premise and on-device, and in the browser or in the cloud.

• Intel® will cover advanced features and tools provided by the OpenVINO™ toolkit. You will learn how to utilize those tools to help you identify the best inference configuration for your needs.

• You will develop, test, and run your AI solution on a cluster of the latest Intel® hardware and software with Intel® DevCloud for the Edge.

• This course is focused on developing deep learning inference applications and not model training. The OpenVINO™ toolkit provides a set of pre-trained models that you can use for learning and demo purposes or for developing deep learning software.

What are the future prospects after completing this course?

Post completion of this course you will be able to develop and deploy high performance Deep Learning applications at the Edge. This course will prepare you for roles such as an IoT Developer/Engineer, Deep Learning Engineer, AI Specialist and more for companies innovating in AI.

What is the time commitment required per week?

You should expect to allocate about 3 hours/week for the duration of 3-4 months to complete this course.

How many DL applications the course will entail? Do we know what applications too we will be doing?

You will learn to create Deep Learning applications for Industrial, Retail, Smart City, and other verticals to solve a variety of tasks including emulation of human vision, automatic speech recognition, natural language processing and more using Intel® Distribution of OpenVINO™ toolkit. After completing this course, you can also try many other tutorials and reference implementations on Intel® Devcloud and Intel® Edge Software Hub. Kindly refer to the course curriculum for more information.

Do I need to install any software before starting this course?

In this course, we will go through the installation process on a local machine and you will also be provided access to Intel® DevCloud for the Edge to learn and build AI applications remotely using a web browser. You will find instructions to get your 90 days access (which you can extend further by following the process as described in the course videos) to Intel® DevCloud for the Edge.

For the complimentary courses by CloudxLab, we will provide you with 180 days of access to CloudxLab's online lab so that you do not have to install anything on your local machine.

Is there any prerequisites for this course?

No, this course is for everyone. The complimentary access to CloudxLab courses will help you in learning the required foundations to make the most out of this certificate course.

What is the certification process?

In this course, you will get two certificates - One from Intel and another one from CloudxLab

To earn the certificate from Intel, you are required to complete at least 80% of the course content along with mandatory projects. The above requirement needs to be met within 180 days of enrollment in order to be eligible for the certificate from Intel.

To earn the certificate from CloudxLab, you are required to complete at least 80% of the course content along with mandatory projects. The above requirement needs to be met within 180 days of enrollment in order to be eligible for the certificate from CloudxLab.

How long I will be able to access the course?

The course access will be for 6 months. To earn the certificate, you are required to complete the course within the deadlines.

If you are unable to complete the course within the stipulated deadlines then you can enroll in the course again to earn the certificate.

What is your refund policy?

If you are unhappy with the product for any reason, let us know within 7 days of purchasing or upgrading your account, and we'll cancel your account and issue a full refund. Please contact us at reachus@cloudxlab.com to request a refund within the stipulated time. We will be sorry to see you go though!

How can I get support?

For questions on the course offered by Intel on OpenVINO™ toolkit, the support will be provided by Intel through Community Support Forum

For complimentary courses by CloudxLab, support will be provided by the CloudxLab team through email and the CloudxLab Forum

Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries. *Trademarks: Other names and brands may be claimed as the property of others.