What are our data sources?
We use the data sources on the side for ranking solutions and awarding badges in deep learning software category. Our data sources in deep learning software category include;
Deep learning has been one of the most innovative areas of AI in the last decade. Commercialization of AI has been spearheaded by deep learning algorithms. Deep learning software enable users to build, test and deploy deep learning models which are models based on multi-layer artificial neural networks.
If you’d like to learn about the ecosystem consisting of Deep Learning Software and others, feel free to check AIMultiple AI Solutions.
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We use the data sources on the side for ranking solutions and awarding badges in deep learning software category. Our data sources in deep learning software category include;
review websites
social media websites
search engine data for branded queries
According to the weighted combination of 7 data sources
Timely Time Tracking
DataRobot
Keras
Clarifai AI Platform
Valohai
Taking into account the latest metrics outlined below, these are the current deep learning software market leaders. Market leaders are not the overall leaders since market leadership doesn’t take into account growth rate.
Timely Time Tracking
Keras
DataRobot
MIPAR
Clarifai AI Platform
These are the number of queries on search engines which include the brand name of the solution. Compared to other AI Solutions categories, Deep Learning Software is more concentrated in terms of top 3 companies’ share of search queries. Top 3 companies receive 65%, 12% more than the average of search queries in this area.
361 employees work for a typical company in this solution category which is 340 more than the number of employees for a typical company in the average solution category.
In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. 17 companies with >10 employees are offering deep learning software. Top 3 products are developed by companies with a total of 700k employees. The largest company building deep learning software is IBM with more than 300,000 employees.
Taking into account the latest metrics outlined below, these are the fastest growing solutions:
DataRobot
Timely Time Tracking
Clarifai AI Platform
Keras
Valohai
We have analyzed reviews published in the last months. These were published in 4 review platforms as well as vendor websites where the vendor had provided a testimonial from a client whom we could connect to a real person.
These solutions have the best combination of high ratings from reviews and number of reviews when we take into account all their recent reviews.
This data is collected from customer reviews for all Deep Learning Software companies. The most positive word describing Deep Learning Software is “Easy to use” that is used in 8% of the reviews. The most negative one is “Difficult” with which is used in 2.00% of all the Deep Learning Software reviews.
According to customer reviews, most common company size for deep learning software customers is 1-50 Employees. Customers with 1-50 Employees make up 57% of deep learning software customers. For an average AI Solutions solution, customers with 1-50 Employees make up 49% of total customers.
These scores are the average scores collected from customer reviews for all Deep Learning Software. Deep Learning Software is most positively evaluated in terms of "Overall" but falls behind in "Value For Money".
This category was searched on average for 500 times per month on search engines in 2022. This number has decreased to 480 in 2023. If we compare with other ai solutions solutions, a typical solution was searched 3k times in 2022 and this decreased to 2.9k in 2023.
Business and management teams need to align on the level of granularity they will discuss to understand model output. This level can be individual results, result patterns that cover 10 cases, 100 cases or 1000 cases. Aligning this at the beginning of the exercise minimizes unncessary discussions between the teams. This is important because deep learning's lack of an easy mechanism for explainability can lead to long discussions between technical and business teams as they try to understand why specific errors take place.
As in any PoC, it is helpful to have a list of goals/assessment areas with quantifiable values. This enables different PoCs to be be compared and PoC to be useful during vendor selection.
In case of deep learning software, PoC should be mainly focused on assessing usability and model accuracy. The best way to assess usability is to have the team that will use the product to build a project with it. After the project, they should assess the software using the list they prepared in advance which should allow for objective assessment of different software.
To assess model accuracy, the team assessing the product needs to identify business value of different model outputs. Read our comprehensive guide on machine learning accuracy to learn more and to be able to assign a numeric value to different machine learning models.
As with any software implementations, benefits should be measured and ROI should be cross-checked against targets so teams understand if their initial estimates were accurate. This helps teams understand and improve the accuracy of their estimates.
There are 3 criteria specific to choosing a deep learning solution: Support for deep learning techniques, flexibility and model accuracy in areas where your company needs deep learning models.
On top of these, typical tech procurement best practices should be followed to ensure that an economical and effective solution is chosen.
A machine learning model can replace a deep learning model for predictions. After all, deep learning is a subset of machine learning with its pros (i.e. better predictions) and cons (i.e. data hungry, lacks explainability). Finally, manual processes can replace models for predictions though this is unlikely to be economical.
When data is limited or when explanation needs to be provided for predictions, other machine learning techniques can be more successful. Especially explanability is a concern for businesses. Since deep learning architecture results in a complex net of artificial neurons, the reasons for its predictions are not obvious. Other machine learning techniques such as decision trees may not produce results that are as accurate as deep learning but are easy to explain as they show the exact factors that lead to each prediction
Finally, manual solutions are always usable as an alternative however, given the high cost of manually analyzing data and making predictions, this is unlikely to be an economical alternative
Built-in support for building latest deep learning structures/architectures is important to ensure that technical personnel spend minimum time implementing solutions based on such architectures.
Data ingestion and processing speed are important features of deep learning software and depend on the hardware the system is running on. Ideally different software should be run on the company's hardware/software stack to see if there are significant performance differences.
Visualization support both for visualizing error rates and the network itself, help developers while building models. While not all solutions support visualizations, some leading ones such as TensorFlow offer such functionality
Once a model is chosen, the aim should be to deploying the model to production systems and make it a part of the decision making process. In deployment, engineers have to ensure that model runs fast enough.
Integrations to databases such as Apache Hadoop and Spark are useful for deep learning software as deep learning models rely on data and integrations make it easier to pull data from company's systems.
Industries with the most data are likely to benefit the most from deep learning models. Some example industries are: