DataCanvas

What do you like best about DataCanvas?

DataCanvas is helpful in Real-time and offline business analytics. The main advantage of using DataCanvas is the User Interface is very smooth and friendly. The Data Scientist with Python knowledge can draw valuable and informative insights from DataCanvas. The modification can be made to the million of rows of data with ease using this tool.

What do you dislike about DataCanvas?

Nothing to dislike so far - DataCanvas can be further improved to provide weekly, monthly and yearly basis analytics to any specific projects that runs for lifetime.

What problems is DataCanvas solving and how is that benefiting you?

We do analytics on daily basis. DataCanvas play a critical role on it. The research project we conducted to analyse the recent trends in the talent market across various locations helped us to figure out most beneficial insights using DataCanvas. We gathered data from various sources. For instance, the population of specific location from various government portals, graduate data from various university websites, and the current location and designation of the talent from their public professional platforms. So, that the data are uneven and of different formats. The data analytics features in this tool helped us to process these data and gain evident insights. We can aslo be able to visualise this data and process these data in Real-time. The insights helps us to get the clear view of talent market and the possible growth of domains across various locations.

What do you like best about DataCanvas?

DataCanvas is a versatile, end-to-end machine learning platform for data science teams. As an end-to-end automated modeling platform, DataCanvas APS uses AI technology to deliver efficient, agile responses to business needs, enabling model development and application to align with industry scenarios, and addressing the challenges of generating, managing, and operating multiple business models on a large scale. Provide unified standard automation modeling capabilities and model lifecycle management functions, realize the operator open source to users, enabling enterprise customers to build independent AI capabilities.

What do you dislike about DataCanvas?

DataCanvas is well designed with no obvious flaws.

Recommendations to others considering DataCanvas:

Whether you are an engineer from an enterprise or a developer/student from a research institution, you can consult DataCanvas for data science and ML development.  Assess your own situation, including the requirements for the data science platform for each business scenario, the hardware and technical architecture, and the skill level acquired by employees.  If you're going to build a data science platform, you should look to DataCanvas for advice.  They have a lot of experience in this area, and they can offer the help of experienced data scientists.  It offers a full range of autonomous AI capabilities, independent of specific hardware or algorithm providers.  We hope that this product can continue to be optimized and upgraded, and keep up with the latest artificial intelligence technology, so our company will continue to upgrade the platform.

What problems is DataCanvas solving and how is that benefiting you?

DataCanvas enables developers and data scientists to quickly and efficiently build, train, and deploy machine learning models of any size, with rich feature analysis and presentation capabilities.

What do you like best about DataCanvas?

Basically, there is no threshold limit for users. Second, it can realize end-to-end model construction. Third, it can also carry out the whole life cycle of the model.

What do you dislike about DataCanvas?

Although it supports three different modeling methods: coding, drag and drop and automatic modeling, the method used depends entirely on the user's technical ability. In the absence of data scientists today, perhaps it can provide some tutorials to help users learn more kinds of new modeling technologies.

What problems is DataCanvas solving and how is that benefiting you?

It enables me to manage models more clearly and methodically, including those under construction and those already built, which plays a good role in improving the efficiency of model use.

What do you like best about DataCanvas?

The most useful function of DataCanvas is the AutoModeling part. Our operation team can use its built-in models to analyze industry data, customer data, etc., to support business decision-making more efficiently. The second reason that we chose it is that DataCanvas doesn't provide or storage users' data that fit the company's data security policy.

What do you dislike about DataCanvas?

Some of the built-in models are not applied to our industry and busines scenarios, hope they can rich them in the future.

What problems is DataCanvas solving and how is that benefiting you?

It helped our team members who could not code to complete data analysis works quickly with technical results. The best part is model results could also be stored and shared between all the team, which made the model property now considerable than we excepted.

What do you like best about DataCanvas?

This company provide flexible, professional enterprise data science platform for end users.

To lower the barrier of adopting machine learning, they also develop automl tools and open source this tool.

During the past years, they have solid customer base and being broadly recommended by their customers.

Founder & CEO of this company is very focused on data science software domain. Their employee are also very professional, dedicated and interesting.

What do you dislike about DataCanvas?

Their business strategy is not so aggressive, they spent all their efforts on the software product.

As analyst, I think they should expand their voice and marketing activities under the current market environments.

They should accelerate their market expansion.

Sorry that do not have so many negations.

Recommendations to others considering DataCanvas:

If you are considering your company's enterprise AI strategy, you should consult DataCanvas.

If you are going to build data science platform, you should ask DataCanvas for advice. They have great experience in this domain, and they can provide experienced data scientist for help.

No matter you are engineer from enterprise, or developer/student from research institution, you can refer to DataCanvas for data science and ML developing.

What problems is DataCanvas solving and how is that benefiting you?

As analyst, I work with DataCanvas to interview their clients. From their clients, I learn that:

DataCanvas emphasize to provide data science platform for both tech department and Lobs. They use big data analytics and machine learning for risk management business, product recommendation system, and quality investigation in manufacture.

Their clients recommend DataCanvas because the platform is open, flexible and easy to use.