｜This blog addresses the following questions｜

• What is AI software? AI software landscape
• What is a software accelerator or AI development?
• How to implement AI software to speed up AI development?

｜Who need this blog｜

• If you are looking for some tools to reduce the team’s workload and increase productivity; or 
• You are looking for some tools to build and deploy deep learning models easily; or 
• You want to optimize the workflow of building an AI project and to better manage the models of your team.

What is AI software? 

Artificial intelligence (AI) software generally refers to a platform or service that provides users the functionalities to implement AI-based on different requirements, in a less complicated way. It integrates with machine learning or deep learning algorithms into users’ everyday functionality, automating tasks or providing predictive analytics. 

We could classify AI software into two broad categories: for final application and for development. Some AI software is designed with purely no-code features so that staff without strong AI backgrounds or programming skills are able to apply AI and solve problems. For instance, many MarTech platforms provide drag-and-drop features for marketers analyzing and optimizing the performance of Ads. The other type of AI software is specifically designed for developing AI projects. This type of AI software aims at reducing the complexity of AI development, making your lives easier. The functionalities of AI software for developers usually include data annotation tools, GPU resources allocation tools, model training accelerators, etc. For instance, developers use machine learning operation tools (MLOps) to track the details of each AI model experience and tune the hyperparameters better. 

The landscape of AI software for developers 2022

There are tons of AI software for developers on the market, and most of the AI software supports a mixture of functionalities. In the following sections, this blog will introduce you to that AI software based on its key features. The order of lifting accords to the general workflow of an AI project.

Data annotation tools  (Learn more about Data Annotation Tools For Computer Vision)

1. Roboflow: After uploading your data to the platforms, Roboflow supports dozens of annotation formats and automates part of the labeling process. Its DataOps  functionality makes re-train models with new training data as you collect it.
2. Img Lab: Img Lab is a web-based open-source software for online annotation.
3. CVAT: CVAT is an web-based and open source data annotation tool, providing various functions for labeling data for computer vision.

Model training & deployment platform

1. Viso.ai: Viso.ai is a no-code AI platform to build computer vision applications. It provides drag and drop features that reduce the time spent on programming. 
2. IBM WATSON: IBM Watson supports AI development from data collection, model building to model deployment. IBM’s Cloud Pak for Data allows users to view all the models on one single platform.
3. Amazon SageMaker: Amazon SageMaker provides users a platform to prepare, build, train, and deploy machine learning models quickly with pre-built functionalities. For instance, common use cases are available on the platform and users can deploy those models easily. 
4. Microsoft Azure AI Platform: Azure AI platform allows you to enhance your project in a variety of ways, from better application creation, data analysis, or machine learning capabilities.
5. Google Cloud AI platform: The Google Cloud AI Platform offers pre-configured Virtual Machines (VMs) for creating deep learning applications. You can provision this VM quickly on the Google Cloud, and the Deep Learning VM image contains popular AI frameworks. You can launch Google Compute Engine instances where TensorFlow, PyTorch, scikit-learn and other popular AI frameworks are already installed. Find out more about the Google Deep Learning VM here.

Model maintenance and monitoring (ModelOps) tools 

1. ModelOp: ModelOps is one of the leading ModelOp’s platforms that offers end-to-end governance of model-driven initiatives.
2. Dataron: : Datatron’s Model Catalog provides the model transparency and lineage throughout the AI model’s operationalization, monitoring, and governance lifecycle. When models are first registered, Datatron’s Model Catalog will automatically capture the properties, metadata, and more and populate the detail throughout the system. Users are able to keep track of their models across different teams.
3. Neptune.ai: Neptune.ai provides users a plug-in to monitor the relevance parameters on their platform. 

AI software or plug-in for accelerating AI development.

1. Algorithmia 
2. Deci.ai
3. Run.ai
4. Latent.ai
5. hAIsten AI

It is clear that Among all types of software, AI accelerators may be the most confusing category. Below are some more explanations. 

What is software for AI acceleration? 

This type of AI software focuses on speeding up the AI development process from various aspects. Usually these tools are integrable with the current development process. 

In history, AI acceleration mostly refers to hardware techniques that optimize the computing performance of processor units. Nevertheless, recently there are new AI products that speed up AI development from a software perspective. According to The Software for AI Optimization Summit 2021, those AI software “reduces training length, inference time, energy consumption and memory usage of AI models.” 

There are several aspects to optimize the operative performance of AI development. From the architecture, compiler, and runtime level to model processing level, to the whole workflow level. As a result, data scientists and AI engineers are able to keep productive and efficient and ensure high quality of model performance and accuracy, while the cost is the same or even less. 

How to implement AI software to speed up AI development?

Who may need AI software? In terms of your role:

1. You are a deep learning professional who wants to work super efficiently (which means you don't want to waste your life on waiting a long time for computing when training a model, or you don’t want to deal with SDK issues when deployment). 
2. You are an expert of a specific discipline. You want to solve a specific problem in your field through deep learning. However, you are not so familiar with deep learning, and without hard coding skills to deal with the environmental configuration and optimization. 

In terms of your organization’s size and level:

1. You are a team building a new deep learning product or service. 
2. You provide technical consulting service to your customers, your capacity is limited to the number of data scientists. 
3. You are a freelancer or contractor working for enterprises. 

These types of services are usually integrable with your current AI development process. Common ways to use the service includes:

1. API: Connect to the AI soccelerators’s API and apply the service.  
2. Plugin: Insert a plugin. 
3. Platform: Upload your model on the platform.