5 Factors to Consider in Developing Deep Learning-Based Applications

If you are using deep learning algorithms to power your AI-based applications – keep reading. This blog unlocks insights that will help you develop and deploy powerful models in less time and with minimum effort.

Building Deep Learning Models the Old Way

Deep learning algorithms are very powerful and effective for building AI-infused products, but AI teams often face challenges when developing deep neural networks. 

Data scientists and machine learning engineers must be able to design and tune models, optimize training and inference, and troubleshoot any issues that may arise. Apart from data collection and preprocessing, one of the most complex and time-consuming processes in deep learning is the creation of models.

Choosing the right model architecture for a specific task can be difficult, as there are many different architectures to choose from and it is not always clear which one will perform the best. Hyperparameter tuning is another tedious task as it requires training multiple models with different hyperparameter configurations and selecting the best one. While training, you must ensure that the model is converging and not overfitting or underfitting the data. Lastly, before deployment, models need to be optimized for the target inference hardware to deliver efficient and scalable inference in production.

In short, developing and training deep neural networks requires a combination of technical skills, domain expertise, and a lot of trial-and-error experiments. 

Three years ago, building models manually was probably your best option, but today, manually tweaking open source models to build your custom application is like trying to navigate at sea, with no GPS or sonars, relying only on the stars.

This is why Deci built a deep learning development platform. It empowers teams to remove development bottlenecks and easily build superior DL-based products.

5 Key Considerations When Building Deep Learning Models

This blog presents a thorough comparison between the old way of building deep learning models via manual processes and the new way by using advanced tools instead.

Here are five key considerations to keep in mind when deciding which approach to take.

  • Time to market
  • Development cost
  • Model performance
  • Team’s core competency
  • Support

1. Time to Market

An important factor to consider is how much time you have to invest in building your deep learning models. Using a development platform like Deci can save you time by providing tools and infrastructure, allowing you to focus on training and tuning your models. On the other hand, building models manually can be a slow and labor-intensive process, especially if you’re new to deep learning.

If you build it manually, how long will it be before you can make significant (or any) headway toward productization? In best-case scenarios, teams typically take months to a year to develop one model, assuming it all goes smoothly.

Can you afford to wait that long? 

Using Deci’s platform shrinks your time-to-value drastically. Your development investment is reduced to the time it takes you to define your performance targets and train your model. 

On average, when using Deci, teams go from data to production-ready models in just 3 weeks  (including training time).

2. Development Cost

One of the main advantages of using a development platform like Deci is that it can save you money. Using Deci typically costs less than manually building your models and maintaining your own deep learning infrastructure. In order to train a deep learning model, one needs to implement the training loop and the surrounding infrastructure. These are the code that loads and samples the data, the code that augments the data, the code for saving and loading checkpoints, and for loss functions, metrics, logging, etc. Integrating any third-party tools (like tensorboard, weights and biases) or implementing high-end features (like EMA, AMP, quantization, and LR scheduling) will require extra efforts. These efforts may be crucial, and can sometimes account for a substantial part of the time a researcher or engineer spends on developing a model.

Another key saving is around the compute resources used for training. Building models manually means you have to test many different architectures. Teams typically train dozens of different model architectures, each with many different hyperparameters and variations to the data or the training process in order to find the combination that will deliver the desired performance. By working with Deci, these cost-intensive experiments are dramatically reduced.

Using Deci’s AutoNAC engine at the beginning of the process assures you that your architecture is optimal and allows you to skip the time and resource-consuming search of the architecture. teams save 30% of their development costs on average.

3. Model Inference Performance

Most data scientists don’t develop their own model architecture from scratch but turn to open source models as a starting point. While there are many open source models available, these models may not always be the best solution for your particular use case. Here’s why:

It’s not all about accuracy – Achieving scalable inference in production takes more than high accuracy. Performance metrics such as latency, throughout, model size, and memory footprint are key for efficient inference. The process of manually tweaking open source architectures is limited in its ability to generate efficient production grade models as it solves for a single objective – accuracy.

Deci’s AutoNAC engine, enables teams to easily carry out a multi-objective search that aims to deliver the optimal accuracy – runtime tradeoff.

Hardware awareness –  Different hardware types work in different ways in terms of what they can parallelize, which operator they run most efficiently, and their memory cache sizes. Hardware awareness is therefore an important consideration in the development of deep learning models. An ideal model architecture would utilize the hardware attributes in the best way possible. Open source models are typically developed and trained on GPUs and as a result do not take into account the constraints of the inference hardware. The result –  suboptimal performance and low hardware utilization which translates into higher inference costs.

AutoNAC generated model architectures are hardware aware are tailored for your specific dataset, use case, and inference hardware. Models built with Deci deliver 3-10x better performance compared to SOTA open source models.

4. Core Competency

Building models manually is not only a resource-intensive and time-consuming process but one that requires a high level of technical expertise.   By using a platform like Deci your team can work more effectively. They get tools and resources that can augment their capabilities and allow them to build better models and solve more complex problems. Your team can focus on the creative and analytical aspects of their work, rather than worrying about the technical details of building and maintaining a deep learning infrastructure.

Deci’s platform simplifies the processes and empowers teams with powerful tools to easily develop production grade models.

5. Support

Another key factor to consider is the level of support available for the approach you choose.  Deci comes with extensive documentation, tutorials, and dedicated support experts, making it easier to get help when you need it. Building models manually, on the other hand, may leave you on your own if you run into problems or have questions.

Build with Deci and benefit from the expertise and experience of our team.

Build Better Models Faster

Teams spend a lot of precious time trying to manually design models. This manual process means you have very long development cycles and need to go through endless iterations and without any certainty that this process will yield the desired results or that the model will perform well in production.

Ultimately, whether you use a development platform like Deci or build your own models, training infrastructure and inference server from scratch will depend on your specific needs and priorities. Consider your time horizon, cost, desired performance, team’s core competency, and choose the approach that best suits your needs and project.

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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")