What is Machine Learning as a Service? Advantages and Best MLaaS Platforms
Machine learning uses statistical analysis to generate prediction output without requiring explicit programming. It uses a chain of algorithms that learn to interpret the relationship between data sets to achieve its goal. Unfortunately, most data scientists aren’t software engineers, which can make it difficult to scale to meet the needs of a growing business. Data scientists can easily manage these complications with Machine Learning as a Service (MLaaS).
What is MLaas?
Machine learning as a service (MLaaS) has recently grown in popularity due to its benefits for data science, machine learning engineering, data engineering, and other learning professionals. automatique. The term “machine learning as a service” refers to a wide range of cloud-based platforms that use machine learning techniques to deliver answers.
The term machine learning as a service (MLaaS) refers to a suite of cloud-based offerings that make machine learning resources available to users. Customers can experience the benefits of machine learning with MLaaS without having to incur the overhead of building an in-house machine learning team or the associated risks. A wide variety of services, including predictive analytics, deep learning, application programming interfaces, data visualization, and natural language processing, are available from various vendors. The service provider’s data centers do all the computing.
Although the concept of machine learning has been around for decades, it has only recently entered the mainstream, and MLaaS represents the next generation of this technology. MLaaS aims to reduce the complexity and cost of implementing machine learning within an organization, enabling faster and more accurate data analysis. Some MLaaS systems are designed for specialized tasks such as image recognition or speech synthesis, while others are designed for broader, cross-industry uses, such as sales and marketing.
How does MLaaS work?
MLaaS is a set of services that provides pre-built, rather general machine learning tools that each company can adapt to their needs. Data visualization, API galore, facial recognition, NLP, PA, DL, etc. are all on the menu here. Data pattern discovery is the primary application of MLaaS algorithms. These regularities are then used as the basis for mathematical models, which are then used to create predictions based on new information.
In addition to being the first comprehensive AI platform, MLaaS unifies a wide variety of systems including but not limited to mobile apps, enterprise data, industrial automation and control and advanced sensors like LiDar. In addition to pattern recognition, MLaaS also facilitates probabilistic inference. This provides a complete and reliable ML solution, with the added benefit of allowing the organization to choose from different approaches when designing a workflow to suit their unique needs.
What are the benefits of MLaas?
The main benefit of using MLaaS is not having to worry about setting up your infrastructure from scratch. Many businesses, especially small and medium-sized enterprises (SMBs), lack the resources and capacity to store and manage large amounts of data. The expense is compounded by the need to purchase or build massive storage space to house all this information. Here, the MLaaS infrastructure takes care of data storage and administration.
Because MLaaS platforms are cloud providers, they offer cloud storage; they enable proper data management for machine learning experiments, data pipeline, and more, making it easier for data engineers to access and analyze data.
Businesses can use predictive analytics and data visualization solutions from MLaaS vendors. Additionally, they provide application programming interfaces (APIs) for a wide variety of other uses, such as emotion analysis, facial recognition, credit risk assessment, business intelligence , health, etc.
With MLaaS, data scientists can start using machine learning immediately instead of waiting for lengthy software installs or provisioning servers, as is the case with most other cloud computing services. With MLaaS, the actual computing takes place in the provider’s data centers, making it extremely convenient for businesses.
Best MLaaS Platforms
1. AWS Machine Learning
When it comes to cloud services, AWS Machine Learning can do it all. It paves the way for businesses to use nearly unlimited resources, including computing power and data storage. There are even more advanced technologies, like MLaaS.
The machine learning solutions provided by AWS Machine Learning are – Amazon Polly, Amazon Lex, Amazon Sagemaker, Amazon Rekognition, Amazon Comprehend and Amazon Transcribe.
2. Google Cloud Machine Learning
Developers and data scientists can use the Google Cloud Machine Learning (GCP) AI platform to build, launch, and manage machine learning models. The Tensor Processing Unit, a chip developed by Google specifically for machine learning, is a key differentiator for this service.
The machine learning solutions provided by GCP are: Build with AI, Conversational AI and Dialogflow CX
3.Microsoft Azure ML Studio
Microsoft Azure ML Studio is the online interface that developers and data scientists can use when developing, rapidly training, and deploying machine learning models. Despite its beginnings in the offline world, Microsoft has made great strides in catching up with major web players.
Sci-kit learns that TensorFlow, Keras, MxNet, and PyTorch are popular frameworks that can be used with Azure Machine Learning Studio.
4. IBM Watson machine learning
You can build, train, and publish machine learning models with IBM Watson Machine Learning. Popular frameworks such as TensorFlow, Caffe, PyTorch, and Keras provide graphical tools that make building models easy.
BigML is a comprehensive machine learning platform with many methods for managing and building machine learning models. The tool facilitates predictive applications in many fields, including aviation, automotive, energy, entertainment, finance, food and agriculture, healthcare, and the Internet of Things. BigML offers its services through a web interface, command line interface, and application programming interface.
Global market and impact so far
ReportLinker, a market research provider, forecasts the machine learning-as-a-service market to reach $36.2 billion globally by 2028, growing at an annual growth rate (CAGR) of 31. 6% between 2018 and 2028.
Key growth drivers for machine learning as a service include growing interest in cloud computing and developments in AI and cognitive computing. The need for effective data management is growing as more companies move their data from on-premises storage to the cloud. Since MLaaS platforms are essentially cloud providers, they make it easier for data engineers to access and process data for machine learning experiments and data pipelines.
Global economic and financial institutions are in shambles after Covid-19 killed millions. With the rise of this COVID-19 pandemic, it is conceivable that artificial intelligence technologies will help combat it. Using population surveillance strategies enabled by machine learning and artificial intelligence, COVID-19 cases are being monitored and traced in many countries.
Below are the drivers of the MLaaS industry:
- Machine learning as a driver of artificial intelligence
- The rise of Big Data and the need for cloud computing
To sum up:
Many different tools exist to aid in the creation of ML. Machine learning development environments can be found with specialized tools that support automation, allow for many versions, and provide a comprehensive framework for ML research and development. Since it can be scaled up to infinity and then down to the size of a current PC in just a few clicks, MLaaS is a solution adapted to the complexity and dynamics of the modern world.
If you’re a data scientist or engineer, you know how hectic your days can be. MLaaS provides a wealth of resources to help you get more done in less time. The main benefit is that you won’t be spending money on brand new infrastructure, computers, setup, or maintenance.
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Dhanshree Shenwai is a content writing consultant at MarktechPost. She is a computer engineer and works as a Delivery Manager in a leading global bank. She has a good experience in FinTech companies spanning Finance, Cards & Payments and Banking with a keen interest in AI applications. She is enthusiastic about exploring new technologies and advancements in today’s ever-changing world.