Dis.co Solves ML Model Training Constraints with Parallelization in a Hybrid Cloud Environment

Samantha Joule Fow

Samantha Joule Fow

March 31, 2020 · 3 min read

Machine learning applications are growing more sophisticated and complex to meet growing demands, and ML will continue to drive progress in countless applications for the foreseeable future. However, complex AI and ML models are demanding increasing compute resources, raising key pain points in this growing industry. Dis.co has built a vendor-agnostic, user-friendly serverless computing platform that addresses each of these constraints. Our hybrid cloud environment is opening up new opportunities for the development of more functional and sophisticated ML models.

 

Pain Points in ML Model Training

Training ML models demands massive amounts of data. Models require data for training, validation, testing, and visualization before deployment. Because it involves several phases, just the data management required for model training can be a standalone project requiring extensive resources.

Depending upon the type and nature of the application, collecting and managing the data necessary to train ML models can involve costly and extensive labor hours, licensing fees, and investments in new hardware. After data has been collected and curated, it must be cleaned, visualized, and selected for training in a process known as featurization. From there, training data must be vectorized into numerical representations that models process in standardized ways.

Extensive processes are necessary just to collect, store, and manage the data involved in ML model training. Even when managed impeccably, there is no guarantee that this investment will lead to a commercially viable product. Reducing the intensity of resources demanded in the training phase can mitigate much of the risk in the early stages of product development.

Currently, most developers with utilize costly GPUs in local parallelization to improve model training efficiency – if they have the means to do so. However, Dis.co has found a better way by leveraging the power of cloud computing.

 

Dis.co’s Cloud-Based Parallelization Solution

As AI and ML models are growing increasingly complex, they are demanding growing quantities of data and processing power to train. Compute constraints are limiting opportunities for innovation in these groundbreaking fields, but parallelized computing solutions are among the best means of improving scalability for AI and ML model training. This is particularly true with respect to cloud-based parallelization, which is improving optimization techniques by leaps and bounds over localized options.

Rather than forcing developers to rely on costly GPUs, Dis.co is has built a platform that harnesses the power of cloud computing to improve the speed and efficiency of model training jobs. This approach harnesses the power of distributed model training capabilities while maintaining the convenience and security of cloud-based systems.

Dis.co is continuing to break new ground in this regard as it works towards its ultimate goal of facilitating access to compute power for advanced applications.  If your company is looking for ways to improve training for their most sophisticated ML models, speak with an expert to learn how Dis.co enables easy parallelization of heavy-compute jobs. In the meantime, read all about how cloud parallelization is changing the landscape of ML model training in our latest whitepaper on Parallelized Compute Solutions for Training Machine Learning Models.

 

 

 

Samantha Joule Fow

Samantha Joule Fow

March 31, 2020 · 3 min read