Artificial Intelligence (AI) is revolutionizing Industries
AI is the technology that has been used rampantly to enhance and automate numerous activities and has enabled people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision-making optimally. However, the one thing that makes AI stand out from the rest is how integrated it is with our day-to-day lives. It's a close simulation of human intelligence which consists of processes such as learning/training, reasoning, and self-correction.
Sorting emails into different tabs such as primary/social/updates, chatbots used for marketing and clarifying basic queries of customers, the customized posts that pop on our social media feeds, autonomous vehicles, humanoid robots, and what not. All of these are few of the major examples where AI is predominantly used.
One thing to be noted is that AI is a broad umbrella concept that includes two major sub-fields: Machine Learning (ML) and Deep Learning (DL). ML is commonly used along with AI but it is a subset of AI. ML refers to an AI system that can self-learn based on the algorithm. DL is a type of machine learning based on artificial neural networks in which multiple layers of processing are used to extract progressively higher-level features from data.
Digging a little deep with Deep Learning
Very often, DL applications follow a life cycle to ensure a structured workflow and to smoothen out the computation. The major steps involved are as follows:
- Use initial data to create the dataset and model.
- Test the model.
- Refine the dataset based on the model test results.
- Create a new test model, and compare it to the previous models.
- When you’re satisfied with the results, create a final production model from the most recent dataset.
- You repeat steps 2–4 as often as necessary until you end up with an accurate model.
The deep learning cycle is iterative, even after a model is in production. After a model is in production, you continue to find ways to improve it.
Training VS Inference
Training: Training refers to the process of creating a machine-learning algorithm. Training involves the use of a deep-learning framework (e.g., TensorFlow) and training datasets. IoT data provides a source of training data that data scientists and engineers can use to train machine learning models for a variety of use cases, from failure detection to consumer intelligence.
Inference: Inference refers to the process of using a trained machine learning algorithm to make a prediction. IoT data can be used as the input to a trained machine learning model, enabling predictions that can guide decision logic on the device, at the edge gateway, or elsewhere in the IoT system.
Challenges in Deep Learning
Just like how every coin has two sides, DL also has a few bottlenecks. Challenges mainly include the possibility of gaps in performance while integrating models with various platforms, a diverse set of requirements for different use cases, etc.
OpenVINO
OpenVINO toolkit, the savior to the above-mentioned challenges, is a free toolkit facilitating the optimization of a deep learning model from a framework and deployment using an inference engine onto Intel hardware. It stands for Open Visual Inferencing and Neural Network Optimization.
Use cases include surveillance cameras, retail store cameras, crossroad to automate smart cities, and factories. It can be easily incorporated into DL-based models for detection, classification, segmentation, and more. It's also available for Linux, Windows, etc. OpenVINO is based on DL inference and not on training.
This toolkit contains the following components:
- Model Optimizer
- Inference engine
- Runtime environment
- Bitstreams
It is simple and easy to use, involves the approach of "code once and deploy on many different platforms", works on all intel platforms seamlessly, and gives the performance required on the cloud, fog, and edge computing platforms.
Why OpenVINO you ask? Well, it helps ensure safe, smart, and efficient industry applications, improves patient care and medical diagnosis, curates personalized and convenient exposures, and enhances public safety in general.
The developer journey of OpenVINO includes three phases: Build, Optimize, and Deploy. The first phase of the building focuses on more than 280+ pre-trained models which can be utilized for varied applications. Following this stage is the step of optimization which includes a model optimizer. It is a Python-based tool to import trained models and convert them to Intermediate Representation. It optimizes performance or space with conservative topology transformations and has hardware-agnostic optimizations. The last phase of deployment involves the integration of the model across several platforms with guaranteed accuracy and consistency throughout.
With this, we have come to the end of this blog which gives you a brief overview of OpenVINO Toolkit and it's applications. Do check out the below video series for your further reference and get hands on practice by creating your own Intel DevCloud Account!
Watch the full Video Series here:
Intel OpenVINO Video Series