At the turn of this decade, there is a surge of no-code AI platforms. More and more businesses are looking to leverage the power of artificial intelligence to build smarter software-based products.

在这十年之交，无代码AI平台激增。 越来越多的企业正在寻求利用人工智能的力量来构建更智能的基于软件的产品。

But execution becomes an obstacle for many. It’s a challenge for startups to find people with relevant machine learning expertise as the field is always a work in progress.

但是执行成为许多人的障碍。 对于初创公司而言，寻找具有相关机器学习专业知识的人才是一项挑战，因为该领域一直处于发展之中。

A lot of firms that invest fortunes in hiring engineers with PhDs and academic research background in machine learning fail to launch their products.

许多在招聘具有博士学位和具有机器学习学术研究背景的工程师方面投入大量资金的公司未能推出他们的产品。

This brings no-code visual drag-and-drop tools to the picture which helps fill the data scientist’s void and makes artificial intelligence less intimidating for the non-technical people.

这为图片带来了无需代码的可视化拖放工具，这有助于填补数据科学家的空白，并减少了人工智能对非技术人员的威胁。

Businesses can now generate datasets, train, and deploy models with minimal to no coding knowledge in significantly less time while staying economical.

企业现在可以在花费最少的时间内，以最少的编码知识甚至没有编码知识就可以生成数据集，训练和部署模型，同时保持经济性。

For mobile application developers, this certainly is a boon in disguise as on-device machine learning is in high demand right now. They don’t need to have a Ph.D. in machine learning and can be more creative with the data and models they wish to train.

对于移动应用程序开发人员而言，这无疑是一种变相，因为目前对设备上机器学习的需求很高。 他们不需要博士学位。 机器学习方面的知识，可以利用他们希望训练的数据和模型更具创造力。

In the next few sections, we’ll walk through some of the best no-code machine learning tools available right now. Some of these are totally free while others might charge you beyond their free trials. Nevertheless, each of them will help you to bring your AI application ideas to reality.

在接下来的几节中，我们将逐步介绍一些目前可用的最佳无代码机器学习工具。 其中一些是完全免费的，而其他一些可能会向您收取超出免费试用期的费用。 但是，它们每个都将帮助您将您的AI应用程序想法变为现实。

创建ML (Create ML)

Being an iOS developer, I had to start with Apple’s no-code drag and drop tool, CreateML. After initially launching with Xcode, today CreateML is an independent macOS application that comes with a bunch of pre-trained model templates.

作为iOS开发人员，我必须从Apple的无代码拖放工具CreateML开始。 在最初使用Xcode启动之后，今天的CreateML是一个独立的macOS应用程序，带有一堆预先训练的模型模板。

By using transfer learning lets you build your own custom models. From image classifiers to style transfers to natural language processing to recommendation systems it has almost every suite covered. All you need to do is pass the training and validation data in the required formats.

通过使用转移学习，您可以构建自己的自定义模型。 从图像分类器到样式转换，再到自然语言处理再到推荐系统，它几乎涵盖了所有套件。 您所需要做的就是以所需格式传递培训和验证数据。

Moreover, you can fine-tune the metrics and set your own iteration count before starting the training. Create ML provides realtime results on the validation data for models such as style transfer. In the end, it’ll generate a CoreML model that you can test and deploy in your iOS applications.

此外，您可以在开始训练之前微调指标并设置自己的迭代计数。 Create ML在诸如样式转换之类的模型的验证数据上提供实时结果。 最后，它将生成一个CoreML模型，您可以在iOS应用程序中对其进行测试和部署。

Google AutoML(Google AutoML)

While Apple is leading the way with Create ML, Google couldn’t afford to be left behind. There AutoML tool works much the same way as CreateML albeit on the cloud.

尽管苹果公司在Create ML方面处于领先地位，但Google却不甘落后。 尽管在云上，但AutoML工具的工作方式与CreateML几乎相同。

Google’s Cloud AutoML currently includes Vision(image classification), Natural Language, AutoML Translation, Video Intelligence, Tables in its suite of machine learning products.

Google的Cloud AutoML当前在其机器学习产品套件中包括视觉(图像分类)，自然语言，AutoML翻译，视频智能，表格。

This enables developers with limited machine learning expertise to train models specific to their use cases. AutoML on the cloud removes the need to know transfer learning or how to create a neural network by providing out of the box support for thoroughly tested deep learning models.

这使具有有限机器学习专门知识的开发人员可以训练针对其用例的模型。 通过为经过全面测试的深度学习模型提供开箱即用的支持，云上的AutoML无需了解迁移学习或如何创建神经网络。

Once the model training is finished you can test and export the model in .pb ,.tflite , CoreML etc formats.

模型训练完成后，您可以测试并导出.pb ， .tflite ，CoreML等格式的模型。

MakeML(MakeML)

MakeML is a developer tool used for creating object detection and semantic segmentation models without code.

MakeML是一个开发人员工具，用于创建无需代码的对象检测和语义分段模型。

It provides a macOS app for iOS developers to create and manage datasets(such as performing object annotations in images). Interestingly, they also have a dataset-store with some free computer vision datasets to train a neural network in just a few clicks.

它为iOS开发人员提供了macOS应用程序，用于创建和管理数据集(例如在图像中执行对象注释)。 有趣的是，他们还拥有一个数据集存储区，其中包含一些免费的计算机视觉数据集，只需单击几下即可训练神经网络。

MakeML have shown their potential in sports-based applications wherein you could do ball tracking. Also, they have an end to end tutorials for training nail and potato segmentation models which should give any non-machine learning developer a good headstart.

MakeML在基于运动的应用程序中展示了其潜力，您可以在其中进行球追踪。 此外，他们还有用于培训指甲和土豆细分模型的端到端教程，这些教程应为任何非机器学习开发人员提供良好的起点。

Using their built-in annotation tool that works in videos you can build a hawkeye detector that’s used in cricket and tennis games.

使用它们在视频中使用的内置注释工具，您可以构建用于板球和网球比赛的鹰眼检测器。

弗里茨·艾里(Fritz AI)

Fritz AI is a growing machine learning platform that helps bridge the gap between mobile developers and data scientists.

Fritz AI是一个不断发展的机器学习平台，可帮助缩小移动开发人员和数据科学家之间的鸿沟。

iOS and Android developers can quickly train and deploy models or use their pre-trained SDK which provides out of the box support for style transfer, image segmentation, pose estimation like models.

iOS和Android开发人员可以快速训练和部署模型，或者使用他们的预先训练的SDK，该SDK为样式转移，图像分割，姿势估计等模型提供现成的支持。

Their Fritz AI Studio lets you quickly turn ideas into production-ready apps by providing data annotation tools and synthetic data to generate datasets in a seamless fashion.

他们的Fritz AI Studio通过提供数据注释工具和合成数据以无缝方式生成数据集，使您能够快速将创意转变为可用于生产的应用程序。

Besides introducing support for Style Transfer before Apple, Fritz AI’s machine learning platform also provides solutions for model retraining, analytics, easy deployment, and protection from attackers.

除了在Apple之前推出对Style Transfer的支持外，Fritz AI的机器学习平台还提供了模型重新训练，分析，易于部署以及免受攻击者保护的解决方案。

跑道ML(RunwayML)

Here’s another great machine learning platform designed specifically for creators and makers. It provides a delightful visual interface to quickly train models ranging from text and image generation(GANs) to motion capture, object detection, etc without the need to write or think in code.

这是专门为创作者和制作者设计的另一个出色的机器学习平台。 它提供了令人愉悦的视觉界面，可快速训练从文本和图像生成(GAN)到运动捕捉，对象检测等各种模型，而无需编写或思考代码。

RunwayML lets you browse a range of models ranging from super-resolution images to background removal and style transfer.

通过RunwayML ，您可以浏览一系列模型，从超分辨率图像到背景去除和样式转移。

While exporting models from their application isn’t free of cost, a designer can always leverage the power of their pre-trained generative adversarial networks to synthesize new images from their prototypes.

尽管从其应用程序中导出模型并非没有成本，但设计人员始终可以利用其预先训练的生成对抗网络的功能来从其原型中合成新图像。

Their Generative Engine that synthesizes images as you type sentences is one of the highlights. You can download their application on macOS, windows or use it on the browser directly(currently in beta).

它们的生成引擎可以在您键入句子时合成图像，这是其中的亮点之一。 您可以在macOS，Windows上下载其应用程序，也可以直接在浏览器中使用它们(当前为Beta)。

显然是AI(Obviously AI)

Obviously AI uses state of the art natural language processing to perform complex tasks on user-defined CSV data. The idea is to upload the dataset, pick the prediction column, and enter questions in natural language and evaluate results.

显然，AI使用最先进的自然语言处理来对用户定义的CSV数据执行复杂的任务。 想法是上传数据集，选择预测列，然后以自然语言输入问题并评估结果。

The platform trains the machine learning model by choosing the right algorithm for you. So, just with a few clicks, you can get a prediction report be it for forecast revenue or predicting the inventory demand. This is incredibly useful for small and medium-sized businesses looking to get a foot into the field of artificial intelligence without having an in-house data science team.

该平台通过为您选择合适的算法来训练机器学习模型。 因此，只需单击几下，您便可以获得预测报告，无论是用于预测收入还是预测库存需求。 这对于希望在没有内部数据科学团队的情况下涉足人工智能领域的中小型企业非常有用。

Obviously AI lets you integrate data from other sources as well such as MySQL, Salesforce, RedShift, etc. So, without knowing how linear regression and text classification you can leverage their platform to run predictive analysis on your data.

显然，AI使您可以集成来自其他来源(例如MySQL，Salesforce，RedShift等)的数据。因此，在不知道线性回归和文本分类如何的情况下，您可以利用其平台对数据进行预测分析。

超级注释(SuperAnnotate)

Beyond model training, data processing eats up a major chunk of time in developing machine learning projects. Cleaning and labeling data can certainly consume lots of hours especially when you’re dealing with thousands of images.

除了模型训练之外，数据处理还占用了开发机器学习项目的大部分时间。 清理和标记数据肯定会耗费大量时间，尤其是在处理成千上万张图像时。

SuperAnnotate is an AI-powered annotation platform that uses machine learning capabilities(specifically transfer learning) to boost your data annotation process. By using their image and video annotation tools you can quickly annotate data with the help of built-in predictive models.

SuperAnnotate是一个由AI驱动的注释平台，它使用机器学习功能(特别是转移学习)来增强您的数据注释过程。 通过使用他们的图像和视频注释工具，您可以在内置的预测模型的帮助下快速注释数据。

So, generating datasets for object detection, image segmentation will get a whole lot easier and faster. SuperAnnotate also handles duplicate data annotation which is common in video frames.

因此，生成用于对象检测的数据集，图像分割将变得更加容易和快捷。 SuperAnnotate还可以处理视频帧中常见的重复数据注释。

教学机(Teachable Machine)

Last but not the least, we have another Google no-code machine learning platform. Unlike, AutoML which is a little developer-friendly, Teachable Machines let you quickly train models to recognize images, sounds, and poses right from your browser.

最后但并非最不重要的一点是，我们还有另一个Google无代码机器学习平台。 与AutoML不同，它对开发人员有点友好，可教学机器使您可以快速训练模型以从浏览器直接识别图像，声音和姿势。

You can simply drag and drop files to teach your model or use the webcam to create a quick and dirty dataset of images or sounds. Teachable Machine uses the Tensorflow.js library in your browser and ensures that your training data stays on the device.

您可以简单地拖放文件来教您的模型，也可以使用网络摄像头来创建图像和声音的快速而肮脏的数据集。 Teachable Machine使用浏览器中的Tensorflow.js库，并确保您的训练数据保留在设备上。

This is certainly a big step by Google for people who wanted to practice machine learning without any coding knowledge. The final model can be exported in Tensorflow.js or tflite formats which can then be used in your websites or app. You can also convert the model into different formats using Onyx.

对于那些想在没有任何编码知识的情况下进行机器学习的人们来说，这无疑是Google迈出的一大步。 最终模型可以Tensorflow.js或tflite格式导出，然后可以在您的网站或应用中使用。 您也可以使用Onyx将模型转换为不同的格式。

Here’s a simple image classification model I managed to train in less than a minute.

这是我在不到一分钟的时间内便完成的简单图像分类模型。

结论(Conclusion)

We saw how no code machine learning platforms bridge the gap between data scientists and non-ML practitioners. While there’s no one size fits all solution, you can always pick a platform to build models or generate datasets at express speed.

我们看到了没有代码机器学习平台如何弥合数据科学家和非ML实践者之间的鸿沟。 尽管没有一个适合所有解决方案的规模，但是您始终可以选择一个平台来快速构建模型或生成数据集。

Moreover, such tools make machine learning a lot more fun to work with. SnapML is another great no code machine learning tool that lets you train or upload your own custom models and use in Snap Lenses. This certainly helps indie developers and creators to put forth their creativity in front of millions of people.

而且，这样的工具使机器学习变得更加有趣。 SnapML是另一个很棒的无代码机器学习工具，可让您训练或上传自己的自定义模型并在Snap Lenses中使用。 这无疑有助于独立开发人员和创作者在数百万人面前展现他们的创造力。

That’s it for this one. Thanks for reading.

就这个。 谢谢阅读。