Machine learning has grown as a game-changer for different businesses and organizations in the past decades. Both small and medium-sized businesses use this technology to collect powerful insights from their data. According to Forbes, the global machine learning market is estimated to grow from $7.3billion in 2020 to $30.6billion in 2024. ML engineers are assigned […]
Updated 12 February 2024
VP – Pre Sales at Appventurez
Machine learning has grown as a game-changer for different businesses and organizations in the past decades. Both small and medium-sized businesses use this technology to collect powerful insights from their data. According to Forbes, the global machine learning market is estimated to grow from $7.3billion in 2020 to $30.6billion in 2024. ML engineers are assigned to develop more models to maintain every customer and shareholders’ dynamic business needs to increase this growth rate.
But, just models’ development is not enough, you have to:
As many data scientists are not developers, achieving the business’ needs at scale can often seem impossible. That’s where MLaaS (Machine Learning as a Service) comes in! MLaaS allows people to manage complex issues comfortably. It makes technology scalable and affordable, so you only need to pay for what you use.
MLaaS is similar to SaaS (Software as a Service) or PaaS (Platform as a Service), or IaaS (Infrastructure as a service). It means you can use the company’s services instead of creating your own.
ML as a service offers machine-learning tools as a part of cloud computing services. Machine learning is right for data analysis, APIs, face recognition, natural language processing and deep learning. The main attraction point of this service is that customers can get started with ML apps quickly without installing specific software or supplying their servers.
Currently, the major MLaaS platforms provide solutions for popular machine learning applications such as recommender systems, prediction, image & video analysis, speech generation, machine translation, automated transcription and many more. Currently, the major MLaaS platforms suggest ready-made solutions for the most popular machine learning applications, including recommender systems, forecasting, image and video analysis, advanced text analytics, machine translation, automated transcription, speech generation, and conversational agents.
Many cloud providers like Microsoft, Amazon and IBM provide these services. MLaaS providers can ensure efficient training, scalability, one-click deployment and automated model tuning.
Let’s discuss MLaaS types.
NLP is a type of machine learning designed to automatically analyze all kinds of texts for emails, social media, online reviews and customer service data. It uses advanced AI-enabled algorithms to break down the text and understand it more than human. Its applications consist of machine translation, grammar interpretation, part-of-speech tagging and many more. It offers different platforms including Amazon Comprehend, Azure Web Language Model API, Google Cloud Natural Language API.
MLaaS image and video analysis programs can be cost-effective as you can purchase them according to your usage. CCTV surveillance is one everyday use case that can record accidents and crimes and use facial recognition image analysis to find criminals. However, this type of machine learning tool analyzes traffic patterns to improve control and reduce overall traffic. Machine learning as a service developer is gaining popularity in retail industries to detect when organizers need to analyze proper product placement and improve customer experience. It offers platforms including Amazon Transcribe, Azure Custom Speech Service, Google Dialogflow Enterprise Edition.
Computer vision uses image and video analysis. However, it analyzes and reacts to data in real-time by following a human dream. This technology is behind things like driverless vehicles that run with ML programs trained on millions of miles of highways. Manufacturing, remote inspection, resource management, safety compliance and retailing theft & fraud are significant computer vision applications.
Speech recognition software uses NLP to understand human language. Siri and Alexa are the most famous examples of using this technology. However, if you are not a massive company like Amazon or Google, it’s not affordable to create your own devices. Retail chains, airlines and banks usually use MLaaS speech recognition for phone-based customer support. Plus, smartphone apps, video games consoles and email programs use this technology to enhance their services.
Developing your machine learning tools and programs makes sense only when you are an established organization owner. Otherwise, MLaaS (Machine Learning as a Service) is the best way to go for medium or small businesses. Building your ML solution can produce great results, but it requires a lot of time and money. On the other hand, MLaaS can have the same results and is usually scalable to any needs.
If you are still why MLaaS is popular, here are some convincing reasons why you should go with MLaaS for your business’s growth.
Good data is essential for practical model training and excellent performance; however, data preparation, labelling and management can waste your productive time, especially when the most valuable records are stored deep within on-premise systems. Apart from this, MLaaS platforms offer elastic & cost-effective data storage, pre-made pipelines for data ingestion and toolkits for setting up effective data governance.
The new type of Machine Learning platforms come pre-furnished with high-quality tools, libraries and frameworks for operating machine learning projects. Certain providers also have pre-made APIs for common ML use cases such as forecast analytics and image recognition. With ready-to-use setup toolkits, you can drastically reduce the prep time for new projects and improve scaling abilities.
Machine Learning as Service platforms enables teams to get down to business faster. It allows data scientists to focus on training, validation, and successful deployments with suitable infrastructure pre-configured and pre-supplied.
Computing power is a hot item, especially when you constantly need to purchase new GPUs to scale your delivery capabilities. If you deduct hardware maintenance and electricity costs, the deal gets even better. Besides, you only need to pay for hardware when it is in use. Finally, some platforms allow you to run experiments in hybrid environments, mixing on-prem and cloud resources.
MLaaS offers ready-made, slightly generic machine learning tools for any organization. These services include data visualization, facial recognition, NLP, deep learning and many more. Many algorithms such as convolutional neural networks, deep learning network, restricted Boltzmann machine and pattern recognition support MLaaS. The key to the success of MLaaS platforms depends on the effect – ML process’s stages, including data storage, model development, performance monitoring and support, ensuring maximum efficiency of the complete machine learning process.
MLaaS platforms help store input data for the ML models in the cloud, use open datasets and import data from other storage locations.
Here, again several options are available in the market. In this model, you follow some steps:
First, you need to build your model from scratch using popular frameworks such as TensorFlow and PyTorch and open-source Python packages such as Scikit-learn.
Now, you can either write code or use a visual interface for model development. In the code-free environment, you can create automated ML experiments in an easy-to-use interface and perform drag-and-drop experimenting in the graphical interface.
Cloud service providers fully manage model training when you use ML as a service. It means you don’t need to worry about the underlying infrastructure, computing resources or model scalability. Even data centres of providers can handle all the computations and manage the underlying infrastructure to scale different models quickly.
MLaaS allows you to deploy ML models you have developed and trained elsewhere. The main reason is, its providers can handle packaging and rectifying models, validating and profiling models and optimizing models.
When you use machine learning as a service, you will get support after model deployment. This support includes:
With MLaaS, you can detect inconsistency, build a recommender system and perform ranking. Besides, Machine Learning as service providers offer high-level APIs that cover:
It includes speech recognition, topic extraction, intention analysis, low-quality audio handling and machine translation.
It includes object detection, face recognition, inappropriate content detection and written text recognition.
It includes activity detection, facial analysis and person tracking.
The MLaaS market is pretty big; it is valued at $1 billion in 2019 and is projected to reach $8.48 billion by 2025. There are four key players in machine learning as a service market. Let’s dive in:
When choosing the best-automated solutions, Amazon’s predictive analytics play a vital role in the market. It can load data from different sources, including Amazon RDS, Microsoft SQL Server, MySQL, Github, Jira, etc. This automation level can be an advantage and disadvantage for ML use. Let’s take an example; while automatic preprocessing saves time, often processed data won’t fit in the data, and you need to provide extra customization.
Amazon uses Amazon Machine Learning services and Amazon SageMaker IDE to offer a robust collection of ML tools. It doesn’t require any coding experience or ML expertise as this platform provides pre-trained AI services. So, it is easy for newbies and also provides a great baseline solution for more advanced items.
All these services are easy to understand and use due to their quite comprehensive implementation documentations.
If you are looking for the proper development environment, Azure Machine Learning is the right MLaaS platform. It creates a creative playground for both newbies and experienced data scientists. From data analysis and data visualization to data labelling and deep learning, this platform has various tools. You can use a graphical drag-and-drop interface for performing many operations (data exploration, preprocessing and validation of modelling results) in this platform.
Azure ML service provides an environment where data scientists can build models, manage and monitor models. It integrates well with Visual Studio and Github easily so that engineers can access and track development. It also supports data transmission tools that are helpful during data analysis.
It includes various machine learning services such as:
Google provides Google Cloud Machine Learning for tech-savvy data specialists and Cloud AutoML. Cloud AutoML is a cloud-based machine learning platform that offers various machine learning products for startup data scientists. Users can upload their datasets, train custom models and deploy them on websites. It depends on Google’s state-of-the-art transfer learning and neural architecture search technology.
Cloud AutoML is custom-built for deep learning models as it implements automatic deep transfer learning and neural architecture search for language pair translation and natural language classification. While GCP supports other languages and frameworks, its focus is clearly on one framework, such as Tensorflow and Google’s baby. Google also caters to data scientists and marketers through its Data Studio – One of the most popular tools for visualizing data.
As a data scientist and developer, you can use IBM Watson Machine Learning for accelerating their AI and machine-learning deployment. It also offers three options at the moment, including:
It is an integrated solution that automates the AI deployment use cases in production.
It is a set of APIs that you can take from any programming language to build apps that make intelligent decisions, solve challenging issues and improve user experience.
As a part of the IBM Watson Studio, Watson Machine Learning Server offers scalable computational resources and deployment management services. IBM machine learning gives flexibility over deployment and model retraining at scale to data scientists thanks to its open-source tools. Watson Machine Learning also facilitates team collaboration within a single modelling space due to its built-in configurable dashboard. Here is a comparison between MLaaS platforms in an image.
Here are some ways Machine Learning as a Service impacts the business world:
As a part of Machine Learning, NLP is perfect for performing customer service, speeding up transactions and serving as personal assistants. These technologies have become popular in human resource management.
Machine learning is ideal for automating tasks such as product recommendation, manual data entry and detecting spam. In the past, these processes would have been tedious and difficult for a massive team of employees. But, now, these tasks are simple and affordable to perform thanks to Machine Learning as a Service.
Expert decisions often need a complicated tree of knowledge bases using various rules. These processes can often be programmed into machine learning systems which can find hidden correlations between variables and outperform experts in their domains.
Transportations and logistics need synchronizing various complicated supply chain processes. MLaaS can instantly and automatically innovate complex supply chain processes for specific situations.
Customer churn is one of the biggest challenges for business, especially for startups. ML algorithms can examine complicated risk factors for churn and help identify which customers need the most support. Even those customers can get advanced help for special deals.
Hopefully, you have understood everything about MLaaS, from what it is to how it impacts the business world. Machine learning as a service offers various tools and services to work more efficiently and tackle all types of situations every day without any hassle. What’s more, it doesn’t require building infrastructure from scratch, paying for the setup and maintenance of the machine. So, if you are looking for the best machine learning development company, get in touch with Appventurez!
Appventurez is one of the most leading mobile app development companies with many years of experience in delivering reliable machine learning as a service solution to both startups and established businesses. We can empower you with machine learning services as part of the cloud computing services. Our experts offer deep learning, predictive analytics and NLP as ML services.
Elevate your journey and empower your choices with our insightful guidance.
VP – Pre Sales at Appventurez
Anand specializes in sales and business development as its VP - Sales and Presales. He supervises the pre-sales process by upscaling on establishing client relationships. He skillfully deploys instruments such as cloud computing, automation, data centers, information storage, and analytics to evaluate clients’ business activities.
Our team looks forward to hearing from you and these details will help us to get back
Elevate your journey and empower your choices with our experts' insightful guidance.
© Copyright 2018 – 2024 Appventurez. All Rights Reserved
38 reviews
You’re just one step away from turning your idea into a global product.
Everything begins with a simple conversation.
+44 7768229682
