Businesses these days revolves around machine learning and AI. This emphasis helps enabling them to obtain or sustain competition in this every growing business world. Many companies are integrating AI in their products to provide the best to the customers and stand out from the crowd.
A walk down the memory lane reminds us that the main Driving forces that brought life to AI are,
- Vast amount of Data
- Algorithm – research and knowledge sharing
- Hardware – High Processing machines
Well, now is the juncture to pop the most prominent question “How do you apply AI in your product?” The first and foremost thing that you need to check is the problem that you are trying to solve and make sure that you collect a enough data to second your solution to the problem. The required amount of data varies based on the complexity of the problem. In this information era, you come across a lot of vendors who can provide you the sufficient data that you require. The second most important aspect of AI transformation is finding a suitable algorithm. A lot of research has been done in various streams, and the ball is in your court and you are responsible for choosing the most suitable one for you. With respect to the hardware availability, various cloud platforms like AWS, Azure, and Google Cloud Platform, provides GPU and TPU resources in a cost-effective way.
In this blog, we will talk about the procedure to use AWS services in the process of AI modelling. AWS helps in most of the modelling aspects which include creating a data lake, data visualization, data transformation, training, and serving trained models.
AWS provided ML services can be categorized as follows,
- Application services
- Platform services
- Framework and infrastructure.
- Application services:
AWS pre-trained AI services are ready-to-use services that are available in your applications. These models are continuously trained and completely maintained by AWS. This service is specifically used for image and video analysis, recommendation, translation, and chatbot. Best of all, AI Services on AWS don’t require machine learning experience.
a) Vision services: Amazon Recognition makes it simpler to add an image and analysis any video that is uploaded to your Vision services tend to identify objects, people, text, scenes, and activities as well as detect any inappropriate content. For instance, you are building an intelligent news application which gives you the opportunity to use celebrity recognition and make information collection faster. In addition, recommendation service to feed users which they like.
b) Language services and speech: AWS offers a range of Natual Language Processing (NLP) services which allows you to add intelligence to the product. Most of the customer service tasks can be automated. Understanding the sentiment of the user enhances a faster and improved decision making. Here is a list of the various services under the language services:
- Polly – Turn text into lifelike speech using deep learning
- Amazon Translate
- Transcribe – high-quality speech-to-text capabilities to your applications and workflows
- Amazon Comprehend – helps discover insights and relationships in a large document.
- Amazon Lex – conversational agent to improve customer services
- Amazon Texttract – Automatically extract text and data from the scanned document
- Platform services
ML services help both developers and data scientists to quickly and easily build, train, and deploy ml models at any scale. Sagemaker is one of the most used services in this category.
The algorithm is chosen either from AWS marketplace or pre-built algos. The Pre-process and training is done by using notebook instances. Here you can schedule training jobs and spend vast amounts of time on hyperparameter tuning. This makes it a whole lot easier for data scientists.
- Sagemaker ground truth
Amazon SageMaker Ground Truth helps you build highly accurate training datasets for machine learning. All labelling ventures are handled either with automated labelling or human labellers.
- Framework and Compute:
Most of all popular frameworks are available to experiment and customize algorithms on your use case. The framework of your choice can be used either in Sagemaker notebook instance or in deep learning AMI. Installation and configuration of the machine are already done, so you can start building your business logic.
To conclude, the basic understanding of AWS ML services can help you integrate application services without the necessity to showcase prior knowledge in machine learning. The upcoming blogs would be more centred on building, training, and deploying AI models.