Machine learning services and deep learning models are hard to come by. One can never be sure of the efficacy and reliability of the service providers or platforms that offer machine learning services. A copious amount of data is needed to create effective and efficient machine-learning models. Machine learning models use data to perform engineering-associated functions and tasks. Businesses are always finding ways to deploy machine learning models, monitor their performance, study data trends, and retrain the models according to organizational demands. The easy way of doing this is through ML service providers or cloud platforms.
ML service providers or platforms put their maximum effort into constructing strong machine learning models that train and retrain over time to generate productive results. They also support the management of the ML lifecycle, starting from project planning to model maintenance. There are numerous benefits and reasons for hiring ML service providers to meet your ML needs within an organization. But it is unclear how to pick the best machine-learning service providers or platforms. Which one will meet your business needs? In this blog, we will highlight the capabilities of ML service providers that are necessary and make them the ideal choice for an organization. Based on these characteristics, features, qualities, and capabilities, you can choose machine learning service providers.
Encourages Proximity to the Data
A company should always prioritize the service providers that can develop ML models without requiring mass data transmission. There are many databases and service providers that support proximity to the data and do not need mass data transmission. Only they are the ideal ones because data transmission latency tends to slow down the process even if the network has infinite bandwidth. Long distance translates into latency in terms of time needed to build precise models if a company has a large amount of data. So, a service provider that can build the model with a given amount of data in a close-by area is an ideal choice.
Supports an ETL or ELT Pipeline
A service provider that supports ETL or ELT pipeline framework is the ideal choice for building machine learning models. Export, transform, and load or export, load, and transform are types of data pipeline configurations used in the database world. Since ML depends on data, it increases the need for these two pipelines. The transformation part is more important to the ML models. ELT pipeline gives much-needed support for transformation.
Capable of Scale-Up and Scale-Out Training
A good ML service provider encourages and supports training models that can be scaled up and down as needed. There is a general idea that computing and memory requirements are quite less when it comes to notebooks. But that is not the case with training models. A notebook that can juggle training models and jobs on different virtual machines and containers is ideal. Additionally, if the training models access accelerators, then the turn days will be shortened.
Supports the ML and Deep Learning Frameworks
A good service provider knows its way around the best ML and deep learning frameworks for programming language. For Python, the preferred and favorite platform for machine learning is usually Scikit-learn. Similarly, Scala prefers Spark ML lib. R favors different native ML packages along with a good interface for Python. So, always pick the service provider that is well-versed and supportive of ML frameworks.
Provides Pre-Trained Models and Promotes Transfer Learning
The biggest benefit of pre-trained models is that they save a considerable amount of time and computing resources needed for training. Therefore, a service provider that offers pre-trained models and services is ideal. To demonstrate with an example, consider the ImageNet dataset. It is quite huge, and training a complex, highly technical neural network against this dataset would be cumbersome, time and resource-demanding. So, a service provider that provides a pre-trained model will suit your organization the most under such circumstances.
Conclusion
Apart from these capabilities and features, machine learning service providers should also be able to provide pre-tuned and trained artificial intelligence services. It should also have the capability to monitor prediction performance for times when data shifts or changes. An organization cannot solely depend on one model and forget it after deployment. It undergoes multiple changes, and therefore the service provider should be able to handle those changes and maneuver itself according to changes over time.
