Briefing Machine learning Platforms with Services and Solutions

Understanding Modern Machine Learning Platforms: A Comprehensive Introduction

Businesses today are looking towards adopting Machine learning for developing intelligent solutions. However, adopting the Machine learning solution has its challenges. According to Venture Beat, 87% of the data science projects never make the production. There are various reasons for this; the major one is a shortage of skills in developing and deploying the projects.

Organizations have to invest hugely in hiring professionals and training them to build a specialized team, but organizations with limited resources might not have that option. Machine learning platforms provide the solution to this problem. It makes it easy to use an integrated platform for developing end-to-end Machine learning Models.

What is a Machine Learning Platform?

Machine learning platforms provide users with the tools required to develop intelligent business solutions using machine learning techniques with minimum technical knowledge and maximum explainability of the process. These platforms’ primary objective is to make machine learning accessible by giving it as a platform as a service. That means it will be easier for organizations to adopt machine learning. They don’t have to worry about the infrastructure required to operationalize the ML and develop the solutions. We are saying that users need to show the data and specify the output they want, and these platforms will come up with the best ML models suitable for the task.

Today’s data scientists and machine learning engineers now have a wide range of choices for how they build models to address the various patterns of AI for their particular needs. Source: Major Platforms For Machine Learning Model Development

Why Organizations Need Dedicated Machine Learning Platforms

As mentioned above, developing and operationalizing machine learning solutions is challenging. Let’s see the blockers faced when developing ML solutions:

1. Lack of Skill Sets: Organizations with limited resources cannot invest in building a specialized team for ML solutions when they require these ML solutions as a part of their existing products. The best solution is that they can have a Machine learning platform to perform these tasks efficiently.
2. Lack of Standardization in ML life cycle development: Every organization developing an ML solution has its approach for defining and maintaining the ML lifecycle, which means there is no standardization of this process. That means best practices are not adopted, which creates problems when scaling.
3. Deployment Complications: Generally, ML projects are developed as minimum viable products(MVPs) under the proofs-of-concept (POCs) of the project. This causes problems when scaling this to a large number of model variants, or with a shift in the market trends(i.e., drift), the reason being the pipeline used for the development is not flexible enough.
4. Post Deployment Blockers: One of the most important tasks after deploying the ML solution is continuous optimization and improvement of the solution based on its performance. The current practice is that every organization has an experimentation system that requires lots of technicalities, making the overall process slow.

Whenever the organization faces the above challenges, the machine learning platform can be seen as the solution to these problems. These platforms are built to tackle the problems mentioned above, which are generally the main blockers in delivering machine learning solutions.

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How Machine Learning Platforms Transform Data Science Workflows

Machine learning platforms can be seen as solutions to the problems mentioned above. How these platforms solve the above problems can be seen below:

1. Enforcing Best Practices and Standardisation: The machine learning platforms will be developed to follow the best practices. Every process can be standardized as it will be used as a service by the organization.
2. Easy Model Development: With machine learning platforms, it’s unnecessary to have professional skills to develop models with data and a problem statement; simple ML models can be developed Easily.
3. Governing ML life cycle: The model can be deployed from the same platform where it was developed. Moreover, the platform will manage the model’s life cycle, giving end-to-end solutions for the same platform.

Where and When to Use the Machine Learning Platform?

The Machine learning platforms are used when developing a machine learning solution or some other product whose requirements are to use machine learning technologies. In developing such solutions, we need to preprocess the data and create a machine learning model that will include training, validation, and ML model testing. Then, we have to deploy the model.

Finally, we have to monitor the production model’s performance; all these processes come up with their challenges. A machine learning platform can replace all these processes with a single platform to easily develop and deploy the machine learning solution. So we can list the below scenarios when to use the ML platform:

• While developing ML solutions

• While developing solutions that require ML solutions as a by-product.

These are the processes where ML platforms are used:

• Data processing

• Developing ML model- Training, validation, and Testing of ML model

• Deploying ML model

• Monitoring and Governing the Model

Key Stakeholders and Beneficiaries of Machine Learning Platform Adoption

The machine learning platforms make machine learning adaptable across the industry. The Machine learning platform’s main beneficiaries are the growing startups that cannot build a specialized team to leverage ML technologies in their solutions. They can opt for these platforms and enable their solutions to be machine learning enabled.

However, these platforms are not just restricted to growing organizations; any enterprise that wants to make their ML solutions faster with the best standards followed while developing the product can also adopt them. Even these platforms are becoming very popular amongst data scientists as they make experimentation faster with best practices followed.

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Comparative Analysis: Top Machine Learning Platforms

The ML platform can be grouped into the following categories depending on the service they provide.

• Semi-specialized platforms

• High-level platforms as a service

Semi-Specialized Platforms 

These platforms are built to develop the model for specific tasks like text analytics and computer vision problems. The text analytics platform allows users to build their custom models for sentiment analysis, topic modeling, etc., from the input text data. Similarly, the visual platform is used to develop fast and easy computer vision models; users need to provide the data, and these platforms will create the models.

Examples of language platforms (text analytics) are Google AutoML Natural Language, Amazon Comprehends, and IBM Watson. The prominent providers of the Vision platform are Google AutoML Vision, Amazon Rekognition, and IBM Watson.

Let's see the tabular comparison of these services:

High-level ML Platform as a Service

These are more advanced and easier to use than the previous category as they automatically detect the type of problem, automatically prepare the data, and configure the learning by themselves. These platforms are best for those having little technical ML knowledge, not for only non-technical people. Even technical people can also use this to increase the faster experimentation and give more time for deploying and evaluating the model. These are provided as platforms as a service; there is nothing to install or set up; use the service.

Examples of the high-level platforms of Machine Learning 

The below highlighted are the high-level platforms of machine learning:

Microsoft Azure Ml

Microsoft Azure Ml is a machine learning as a service (MLaaS) platform that enables users to use it as automated ML. Everything is done by the platform, from problem detection to model development or as a Designer in which users can edit their model training pipeline. It has integrated deployment services also with MLOps support for the post-development procedures.

Machine Learning on AWS(Amazon web services)

AWS also offers a wide range of machine learning services through its platform. Sagemaker is one of the famous AWS services that enable organizations to adopt machine learning in their projects. Like the previous one, it has Autopilot mode, where one has to do things near nothing, and customization mode, where one can edit the process.

GCP Machine Learning Services

Google Cloud Platform comes with an end-to-end machine learning life cycle that provides data preparation, model building, validation, deployment, and MLOps. All these services are given as a platform, and Users can use them as MLaaS. Let’s see the comparison of these Services:

Based on the types and comparisons given above, one can choose the machine learning platform service that is suitable for their requirements.

MLaaS (Machine Learning as a Service): Driving Organizational Intelligence

A machine learning platform as a Service will be a big advantage for organizations adopting machine learning solutions. The machine learning platform will make the machine learning technologies accessible to everyone. Organizations can utilize this opportunity to enable themselves to use machine learning.

Next Steps in ML Platform Adoption

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