How to Reveal True Value of Data with Bespoke Deep Learning Algorithms
The next big trend in data analytics is Deep Learning (DL) applications. While Artificial Intelligence (AI) and Machine Learning (ML) continue to grow exponentially, DL has started to make its presence felt across industries.
Today, data analysts around the world leverage DL algorithms to optimise data collection and analysis protocols. However, although there’s significant hype surrounding universal DL models that can be applied to any use case, the reality is just the opposite.
Successful DL systems leveraged in data analytics are primarily bespoke creations. These were custom-built for each individual application to match specific requirements.
Although the DL revolution is one of the most significant advances of our time, humans are still needed to create, curate (datasets for training), and oversee the whole project.
At least in the short term, this isn’t going to change. So, if you want to get the most out of your big data project, you must take a bespoke approach to data analytics.
What’s the Difference between Machine Learning and Deep Learning?
ML and DL are similar but not the same. ML algorithms learn data analysis from experience.
ML is linear, while DL is a series of non-linear units that leverage a neural network to function more like the human brain.
These processing units take information from previous units to further process and refine data for analysis. This approach helps data analysts considerably improve data collection and analysis protocols.
This means that data scientists and analysts don’t have to spend much time engaging in tedious work getting raw data ready for analysis. Instead, they can focus on more meaningful tasks for the business, for example, improving productivity and operational efficiency. After all, bespoke DL algorithms already perform twice the work in half the time.
Why Is It Important to Deploy Bespoke Deep Learning Algorithms?
There are plenty of point-and-click model generators. But these still demand labelled training data and the creation of new models for each use case or application.
The transfer of learning is also tremendously limited. This is because universal algorithms only encode low-level pattern relationship knowledge. In contrast, bespoke DL algorithms enable high-level abstract knowledge representation and transfer.
In this scenario, transfer of learning, automatic tuning, and automated neural architecture help reduce the amount of manual labour needed to construct and tune the algorithm.
While this approach helps reduce the amount of novel training data that’s required, every new DL application will still demand the creation of a new bespoke model. As a result, building DL systems still depend heavily on bespoke processes. This involves the creation of custom solutions that are tailored to your specific needs.
Who Needs Bespoke Deep Learning Algorithms?
Data analytics powered by DL solutions are perfect for industries like finance and banking, healthcare, human resources, cyber security, and more. For example, DL is far better at fraud detection than its human counterparts.
However, regardless of the industry, you must create brand-new models for each, and this is a lengthy, resource-intensive process. There’s also limited underlying code, training datasets, and models shareable between them.
As this process is costly and challenging, several companies try to fill the gap with their turnkey solutions. But as mentioned above, they continuously fail to provide any real business value.
Anyone selling a universal generalized DL application is just promoting a glorified correlation engine. While these applications are more capable than an Excel spreadsheet, they are also just as limited.
Bespoke DL models are delicate and cannot seamlessly adapt to changes in their operating conditions or changes in their input data. For example, an image recognition algorithm trained to identify ground-level daytime images can’t suddenly recognise infrared night-time images shot by drones. Even something as small as swapping the camera could lead to algorithmic failure.
So, bespoke intelligent algorithms can’t be purpose-built for the industry alone. They must be purpose-built and regularly updated for each task. Sometimes, you might even have to rewrite these models to reflect our rapidly changing world.
How Does Evolved Ideas Help Businesses Develop Bespoke DL Algorithms?
At Evolved Ideas, we have worked on multiple AI, ML, and DL projects using unique enterprise data to build bespoke models. Over the years, we have updated and finetuned our processes to enhance client experiences.
We take a five-step approach to develop bespoke DL algorithms:
1. The Planning Phase
From the first discussion, we’ll work closely with you to better understand your business goals. This approach will help us choose an appropriate ML or DL approach for the project.
Our DL engineers also engage in further research to identify the best models for your project. They will also work closely with you to identify and better understand the data to ensure that the bespoke model and training are highly effective. Whenever companies don’t have a data set, we also help create them.
2. The Designing Phase
Upon completion of the planning phase, we’ll design the architecture and infrastructure that will run your data models. Whether it’s hosted on-premises or in the cloud, we ensure that it meets all your specific requirements.
3. The Prototype Phase
To scale with confidence, we’ll first build a Minimum Viable Product (MVP). This small MVP will use a subset of the data to solve part of the problem.
4. The Building and Deployment Phase
Once clients are satisfied with the performance of the MVP, we’ll scale it and release it. Our experienced ML and DL engineers will leverage their extensive experience to ensure successful deployment across Edge services running on-premises and in the cloud with partners like AWS, Azure, and GCP.
5. Ensuring Long-Term Success
We will work with you to gain an in-depth understanding of your in-house teams’ expertise and experience. We will work with them, train them, and help them successfully manage and maintain the project in the long term. If you don’t have an in-house data science team, we can also provide them.