Applications of deep learning enable businesses from a wide range of industries to develop innovative products, increase revenues, and reduce costs. However, developing and implementing a successful deep learning application has its own set of challenges. Benefiting from deep learning consulting can help businesses overcome these challenges and reduce the chance of spending resources on failed projects.
What is the difference between deep learning consulting and ML consulting?
Machine learning includes a wide variety of techniques, and deep learning is one of the most successful and popular approaches as of today, and has numerous use cases such as computer vision, natural language processing, and automated predictions.
Most machine learning consultants can help you implement deep learning to your business processes. Nonetheless, getting consulting services from vendors that specialize in deep learning applications can provide better results because developing deep learning applications has its specific challenges.
What are the challenges of implementing deep learning into business processes?
There are industry specific challenges of applying deep learning into business processes but some common challenges are as the following:
- DL models are hard to explain
- DL models are data hungry
- DL models are computationally expensive
- Data privacy and security concerns
For a more comprehensive account, feel free to check our article on the challenges of deep learning.
What are the benefits of getting deep learning consulting services?
Some of the benefits of getting deep learning consulting services include:
- Customization: Deep learning involves different types of approaches from generative models to convolutional and recurrent neural networks that are suitable for different applications in a wide range of industries. A consultant that has experience in developing different types of deep learning models can help businesses to implement the technique that is most suitable for their business problem.
- Avoiding common pitfalls: AI and data science failure rate is high. A deep learning consultant would be better aware of the specific challenges of DL and can reduce the chance of failure by discussing them upfront. Getting deep learning consulting services will also help businesses be better informed about the requirements and the value of the project.
What are the typical deep learning consulting activities?
Assessing business needs
A clear business objective is critical for both determining the need for a deep learning solution and the success of implementation. Whether it is predicting a disease based on patient data, or whom to show ads to, misunderstanding business requirements is one of the major reasons for project failure.
Not every problem needs deep learning. For instance, if the problem can be reduced to a set of rules, simple rule-based approaches can suffice. Clearly determining your business problem enables your consultant to suggest appropriate solutions.
Data Collection and Exploration
If you have the data sufficient for developing a deep learning solution, consultants can validate the data and move on to model development.
Consultants develop an appropriate deep learning model for your business problem. This is an iterative process involving thousands of experiments.
Full-stack application development
Taking a model to production requires additional software development and integration work.
Most of the time, ML models are encapsulated in APIs which are easy to integrate with any application. The development of the application which will operationalize the ML model and make it part of the decision-making process can be harder than building the model. Application development may require integration to existing enterprise systems which requires working with external developers.
Developing an application would enable non-technical users to benefit from the developed deep learning model.
For more on consulting in AI/ML landscape, feel free to check our articles on
If you still have questions regarding the deep learning or other types of consulting, we can help:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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