How to select the Most Suitable ML Platform
Adoption of ever-changing technology and the development of digital transformation initiatives has prompted businesses to embrace Business Analytics – businesses no longer rely on IT-driven activities and intelligence functions. encompassing.
Stakeholders and decision-makers in today’s business world require pertinent and current insights from units and processes across the organization. Using quarterly reports and end-of-the-month statement as conventional information sources is no longer enough in today’s world. Company-wide data-powered analytics solutions empower different approaches that influence decision-making.
Machine Learning (ML), therefore, becomes an imperative in this regard. Most companies believe that ML, Artificial Intelligence (AI), and analytics are the magical answer to optimize processes and uncover new opportunities.
Several studies show how ML and AI help companies to gain an upper hand. However, many organizations rush into deploying an ML platform without considering all the important aspects, and often, reach a point of no return in their analytics journey.
Therefore, organizations should be completely prepared when they adapt, develop or deploy ML. In this article, we go through some aspects of determining the most appropriate ML platform.
Data scientists should not stress over infrastructure installation and tuning, following complex procedures for custom dashboard creation, training experiments, model provenance, model deployment and monitoring. The selected ML platform should provide all these features. When assessing such a platform, we should focus on the three aspects; simple to customize, simple to set up, and easy installation process.
ETL or ELT Pipeline Support
ETL (export, transform, and load) and ELT (export, load, and transform) are the common data pipeline configurations for loading data. Deep learning and ML augment the requirements for these, particularly the transform phase. ELT gives you greater flexibility when your transformations need to change, as the load stage is typically the most tedious for big data.
A good ML platform should support various pipeline flavors, and go beyond. For example, raw data is often very noisy and may need to be filtered. This should automatically be done by the ML platform. Or, such data often have differing ranges: One variable may have a very large range ( 0 to 1 million), while another has a much smaller range, say – 0.1 to – 0.001. A good ML platform would automatically normalize such variables to prevent the ones with huge ranges from ruling the model.
APIs: Cloud ML Services
APIs are usually important when you want to opt for a cloud-based ML platform. Such platforms are mainly APIs disposed towards application developers and individuals that wish to integrate AI into already existing applications. Such platforms are purpose-built and will always zero in on explicit tasks, say – computer vision, language processing, and translation. Amazon Web Services, Google Cloud, and Microsoft Azure are top sellers in this category.
When production deployments of models is being considered, a scalable platform is an absolute necessity. The organization can pick a platform that meets its present requirements as well as can scale for what’s to come. The selected platform should be able to oversee cost versus user experience productively, sophistically gauge for both batch and real-time workloads, scale automatically with the expanding traffic, serve high throughput situations, follow the safe implementation and deliver best practices.
Support Deep Learning and AI Frameworks
Most data scientists have their own favorite frameworks and programming languages for AI and deep learning. For folks who lean toward Python, a top choice for ML is Scikit-learn, while TensorFlow, PyTorch, Keras, and MXNet are top picks for deep learning. In Scala, Spark MLlib will, in general, be liked for ML. In R, there are numerous native ML packages and a decent interface to Python. In Java, H2O.ai rates high, as do Java-ML and Deep Java Library.
Deep learning and cloud machine platforms usually have their variety of algorithms. They tend to support external frameworks as containers with explicit entry points or in at least one language. You can incorporate your algorithms and statistical methods with the AutoML capacities in the platform and this can be very advantageous.
When managing a model life cycle, it is impoerative to be able to track models, change them easily, and reproduce them at will. Whether working together with colleagues, emphasizing a current model, or investigating a production failure, reproducibility is critical. Therefore, you have to make sure the ML platform is built with reproducibility as an inbuilt feature.
With all these features and requirements comes another question, should we Build or Buy ML Platforms?
The three important factors to consider when deciding whether to build or purchase an ML platform include its abilities, ultimate objectives, and the quality and number of people present in the data science team. A company can pick an augmented or commercial platform if it has a small data science team and wishes to unburden some of the model building work onto a ML platform provider. The seller and platform should be chosen when they assess and affirm that it will address the goals and objectives of a company in regards to ML.
There are a lot of providers who accomplish the grunt work through patching together a suite of open-source packages. When working with an ML platform provider, you should ask what tools they are utilizing because it is plenty significant as going out and working out on what tools can be used in-house.
Organizations with an incredible data science team and an up-to-date AI strategy can utilize their in-house tools to deal with ML goals or issues. However, you should not forget that choosing customized model structures or choosing one’s parameters is not generally a quick or straightforward beginning of an ML platform architecture.