Compare the Top Machine Learning as a Service (MLaaS) Platforms in 2024
Machine Learning as a Service (MLaaS) platforms provide users with cloud-based access to pre-trained models and algorithms for predictive analytics and data analysis. MLaaS makes machine learning accessible by eliminating the need to develop and maintain complex infrastructure, instead allowing users to leverage remote computing resources in order to generate insights quickly and easily. These platforms vary in terms of features, services and pricing, offering different combinations designed to cater to a range of user needs. MLaaS allows organizations to focus their efforts on building products or optimizing business processes while minimizing the resources needed to deploy models into production. Here's a list of the best machine learning as a service platforms:
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1
Vertex AI
Google
Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection. -
2
Dialogflow
Google
Dialogflow from Google Cloud is a natural language understanding platform that makes it easy to design and integrate a conversational user interface into your mobile app, web application, device, bot, interactive voice response system, and so on. Using Dialogflow, you can provide new and engaging ways for users to interact with your product. Dialogflow can analyze multiple types of input from your customers, including text or audio inputs (like from a phone or voice recording). It can also respond to your customers in a couple of ways, either through text or with synthetic speech. Dialogflow CX and ES provide virtual agent services for chatbots and contact centers. If you have a contact center that employs human agents, you can use Agent Assist to help your human agents. Agent Assist provides real-time suggestions for human agents while they are in conversations with end-user customers. -
3
TensorFlow
TensorFlow
An end-to-end open source machine learning platform. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Build, deploy, and experiment easily with TensorFlow.Starting Price: Free -
4
Domino Enterprise MLOps Platform
Domino Data Lab
The Domino platform helps data science teams improve the speed, quality, and impact of data science at scale. Domino is open and flexible, empowering professional data scientists to use their preferred tools and infrastructure. Data science models get into production fast and are kept operating at peak performance with integrated workflows. Domino also delivers the security, governance and compliance that enterprises expect. The Self-Service Infrastructure Portal makes data science teams become more productive with easy access to their preferred tools, scalable compute, and diverse data sets. The Integrated Model Factory includes a workbench, model and app deployment, and integrated monitoring to rapidly experiment, deploy the best models in production, ensure optimal performance, and collaborate across the end-to-end data science lifecycle. The System of Record allows teams to easily find, reuse, reproduce, and build on any data science work to amplify innovation. -
5
Dataiku DSS
Dataiku
Bring data analysts, engineers, and scientists together. Enable self-service analytics and operationalize machine learning. Get results today and build for tomorrow. Dataiku DSS is the collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build, and deliver their own data products more efficiently. Use notebooks (Python, R, Spark, Scala, Hive, etc.) or a customizable drag-and-drop visual interface at any step of the predictive dataflow prototyping process – from wrangling to analysis to modeling. Profile the data visually at every step of the analysis. Interactively explore and chart your data using 25+ built-in charts. Prepare, enrich, blend, and clean data using 80+ built-in functions. Leverage Machine Learning technologies (Scikit-Learn, MLlib, TensorFlow, Keras, etc.) in a visual UI. Build & optimize models in Python or R and integrate any external ML library through code APIs. -
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Levity
Levity
Create your own AI that takes daily, repetitive tasks off your shoulders so your team can reach the next level of productivity. Levity is a no-code platform that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code. Levity enables you to upload your own labeled data to train custom models that fit your business like a glove. If you want to get started even quicker, it also provides countless templates for frequent use-cases, such as sentiment analysis, customer support or document classification. Got a repetitive task that requires more than rule-based automation that standard RPA tools offer? Try Levity out for free and see within minutes what cognitive automation is capable of.Starting Price: $99 -
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Valohai
Valohai
Models are temporary, pipelines are forever. Train, Evaluate, Deploy, Repeat. Valohai is the only MLOps platform that automates everything from data extraction to model deployment. Automate everything from data extraction to model deployment. Store every single model, experiment and artifact automatically. Deploy and monitor models in a managed Kubernetes cluster. Point to your code & data and hit run. Valohai launches workers, runs your experiments and shuts down the instances for you. Develop through notebooks, scripts or shared git projects in any language or framework. Expand endlessly through our open API. Automatically track each experiment and trace back from inference to the original training data. Everything fully auditable and shareable. Automatically track each experiment and trace back from inference to the original training data. Everything fully auditable and shareable.Starting Price: $560 per month -
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Qwak
Qwak
Qwak simplifies the productionization of machine learning models at scale. Qwak’s [ML Engineering Platform] empowers data science and ML engineering teams to enable the continuous productionization of models at scale. By abstracting the complexities of model deployment, integration and optimization, Qwak brings agility and high-velocity to all ML initiatives designed to transform business, innovate, and create competitive advantage. Qwak build system allows data scientists to create an immutable, tested production-grade artifact by adding "traditional" build processes. Qwak build system standardizes a ML project structure that automatically versions code, data, and parameters for each model build. Different configurations can be used to build different builds. It is possible to compare builds and query build data. You can create a model version using remote elastic resources. Each build can be run with different parameters, different data sources, and different resources. Builds c -
9
TrueFoundry
TrueFoundry
TrueFoundry is a Cloud-native Machine Learning Training and Deployment PaaS on top of Kubernetes that enables Machine learning teams to train and Deploy models at the speed of Big Tech with 100% reliability and scalability - allowing them to save cost and release Models to production faster. We abstract out the Kubernetes for Data Scientists and enable them to operate in a way they are comfortable. It also allows teams to deploy and fine-tune large language models seamlessly with full security and cost optimization. TrueFoundry is open-ended, API Driven and integrates with the internal systems, deploys on a company's internal infrastructure and ensures complete Data Privacy and DevSecOps practices.Starting Price: $5 per month -
10
MonkeyLearn
MonkeyLearn
MonkeyLearn makes it simple to clean, label and visualize customer feedback — all in one place. Powered by cutting edge Artificial Intelligence. All-in-one text analysis and data visualization studio. Gain instant insights when you run an analysis on your data. Use ready-made machine learning models, or build and train your own – code free. Discover our templates, tailored for different business scenarios and equipped with pre-made text analysis models and dashboards. Identify the topics and interests that matter most to target markets. Execute demand generation and sales strategies based on accurate analyses of customer opinions and feelings. Slice and dice your survey responses by requests, intent, and sentiment. See more than the survey intended.Starting Price: $99 per month -
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BigML
BigML
Machine Learning made beautifully simple for everyone. Take your business to the next level with the leading Machine Learning platform. Start making data-driven decisions today! No more wildly expensive or cumbersome solutions. Machine Learning that simply works. BigML provides a selection of robustly-engineered Machine Learning algorithms proven to solve real world problems by applying a single, standardized framework across your company. Avoid dependencies on many disparate libraries that increase complexity, maintenance costs, and technical debt in your projects. BigML facilitates unlimited predictive applications across industries including aerospace, automotive, energy, entertainment, financial services, food, healthcare, IoT, pharmaceutical, transportation, telecommunications, and more. Supervised Learning: classification and regression (trees, ensembles, linear regressions, logistic regressions, deepnets), and time series forecasting.Starting Price: $30 per user per month -
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Google Cloud AutoML
Google
Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs. It relies on Google’s state-of-the-art transfer learning and neural architecture search technology. Cloud AutoML leverages more than 10 years of proprietary Google Research technology to help your machine learning models achieve faster performance and more accurate predictions. Use Cloud AutoML’s simple graphical user interface to train, evaluate, improve, and deploy models based on your data. You’re only a few minutes away from your own custom machine learning model. Google’s human labeling service can put a team of people to work annotating or cleaning your labels to make sure your models are being trained on high-quality data. -
13
IBM Watson Machine Learning is a full-service IBM Cloud offering that makes it easy for developers and data scientists to work together to integrate predictive capabilities with their applications. The Machine Learning service is a set of REST APIs that you can call from any programming language to develop applications that make smarter decisions, solve tough problems, and improve user outcomes. Take advantage of machine learning models management (continuous learning system) and deployment (online, batch, streaming). Select any of widely supported machine learning frameworks: TensorFlow, Keras, Caffe, PyTorch, Spark MLlib, scikit learn, xgboost and SPSS. Use the command-line interface and Python client to manage your artifacts. Extend your application with artificial intelligence through the Watson Machine Learning REST API.Starting Price: $0.575 per hour
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14
Conversus.AI
Converseon
Take control of your models with the leading Machine Learning-as-a-Service Platform for unstructured social and voice-of-customer text analytics. Our experienced team of experts can help you build highly-advanced, bespoke social models to leverage, deploy, generalize and utilize your information properly. Align your social listening initiatives with best practice AI strategies for transparency, accuracy and fairness. New to social listening or want to advance the effectiveness of your solution? We provide the industry’s most experienced team to help guide your journey. You have the data but now want to discover critical insights. We provide a wide range of industry-leading programmatic insight solutions powered by our AI classifiers. Choose from a robust library of industry-based prebuilt machine learning models for immediate deployment. Help ensure your models are not inadvertently discriminatory and inaccurate. Understand the performance of each model clearly before deployment. -
15
Azure Machine Learning
Microsoft
Accelerate the end-to-end machine learning lifecycle. Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML. Productivity for all skill levels, with code-first and drag-and-drop designer, and automated machine learning. Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete ML lifecycle. Responsible ML capabilities – understand models with interpretability and fairness, protect data with differential privacy and confidential computing, and control the ML lifecycle with audit trials and datasheets. Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R. -
16
Wallaroo.AI
Wallaroo.AI
Wallaroo facilitates the last-mile of your machine learning journey, getting ML into your production environment to impact the bottom line, with incredible speed and efficiency. Wallaroo is purpose-built from the ground up to be the easy way to deploy and manage ML in production, unlike Apache Spark, or heavy-weight containers. ML with up to 80% lower cost and easily scale to more data, more models, more complex models. Wallaroo is designed to enable data scientists to quickly and easily deploy their ML models against live data, whether to testing environments, staging, or prod. Wallaroo supports the largest set of machine learning training frameworks possible. You’re free to focus on developing and iterating on your models while letting the platform take care of deployment and inference at speed and scale. -
17
Amazon SageMaker Studio
Amazon
Amazon SageMaker Studio is an integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps, from preparing data to building, training, and deploying your ML models, improving data science team productivity by up to 10x. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, collaborate seamlessly within your organization, and deploy models to production without leaving SageMaker Studio. Perform all ML development steps, from preparing raw data to deploying and monitoring ML models, with access to the most comprehensive set of tools in a single web-based visual interface. Quickly move between steps of the ML lifecycle to fine-tune your models. Replay training experiments, tune model features and other inputs, and compare results, without leaving SageMaker Studio.
Machine Learning as a Service (MLaaS) Platforms Guide
Machine Learning as a Service (MLaaS) is an umbrella term for the services provided by cloud-based providers to carry out machine learning tasks or operations. MLaaS offers customers the ability to leverage advanced machine learning capabilities without having to build their own infrastructure or develop complex algorithms. Instead, they can simply use an existing service to gain access to state-of-the-art technologies such as deep learning, image recognition, natural language processing, and more.
MLaaS platforms provide an array of features that make them appealing for data scientists looking for a quick solution or users in need of quick results without having to invest time and resources into developing their own models. These features include prebuilt models that require minimal customization, automated feature selection and engineering techniques, real-time access to results, and APIs that allow customers to easily integrate with existing applications. Additionally, many MLaaS solutions offer scalability options so that customers can quickly adjust their usage according to need. This makes them particularly useful for time-sensitive tasks like fraud detection and automated customer service.
Using a MLaaS platform also allows users the opportunity to take advantage of advanced analytics tools such as deep learning which can be expensive and difficult to implement from scratch. It also gives users access to powerful GPUs and cloud computing power which are not always available on local systems making it easier for them to handle large amounts of data in near real-time settings with less latency than traditional methods would require. Finally, since most MLaaS solutions are pay-as-you go models they present businesses with cost efficient ways of leveraging powerful machine learning capabilities without investing too much upfront capital into hardware or software setup costs.
Features Provided by Machine Learning as a Service (MLaaS) Platforms
- Model Development and Optimization: MLaaS platforms provide a suite of tools for data scientists to create, optimize, and deploy models. This includes algorithms that can be used to explore complex datasets, identify patterns and relationships in data, and generate predictive models.
- Automated Model Deployment: MLaaS helps developers quickly deploy their models into production with automated pipeline management tools such as model versioning, A/B testing, and data validation.
- Machine Learning Infrastructure: MLaaS provides an ecosystem of cloud-based services needed to support the development of machine learning applications. This includes compute resources, storage systems, databases, development frameworks, toolkits (such as TensorFlow), and other necessary infrastructure components.
- Data Integration: MLaaS often offers advanced data integration capabilities that allow organizations to quickly ingest data from multiple sources into the platform for analysis. It also allows them to store structured or unstructured datasets efficiently for use in machine learning applications.
- Scalable Machine Learning Infrastructure: MLaaS platforms often have built-in scalability solutions so developers can easily scale up or down based on the needs of their application or project requirements. This helps users access resources on demand without having to invest in physical infrastructure solutions.
- Visualizations and Reporting Tools: Most MLaaS platforms come with various reporting features such as real-time visualizations of insights derived from machine learning models for easy understanding by customers or decision makers within an organization.
- Collaboration Tools: MLaaS often offers collaboration solutions such as private versioning, collaboration hubs, and data sharing tools so teams can easily collaborate on projects. This helps streamline the process of developing and deploying machine learning solutions.
- Security and Regulatory Compliance: MLaaS platforms are designed to provide security for users’ data and adhere to specific industry regulations such as GDPR or HIPAA. This ensures that users can trust the platform with their sensitive data.
Different Types of Machine Learning as a Service (MLaaS) Platforms
- Supervised Learning MLaaS: This type of platform offers automated systems that use labeled datasets to identify patterns in data and make predictions about future outcomes.
- Unsupervised Learning MLaaS: This type of platform uses algorithms to discover patterns and correlations in data without the use of labeled datasets.
- Reinforcement Learning MLaaS: This type of platform allows a machine to learn from its own experience, making decisions based on trial and error. It can be used for decision-making, control, optimization, and prediction tasks.
- Transfer Learning MLaaS: This type of platform enables a machine to apply knowledge acquired from one domain (source task) to solve problems in another domain (target task).
- Model Management MLaaS: These platforms provide tools for managing models created by various supervised or unsupervised learning algorithms, including monitoring their performance and accuracy over time.
- NLP (Natural Language Processing) MLaaS: These platforms allow machines to understand natural language input and generate natural language output such as speech recognition or text analysis.
- Image Recognition MLaaS: These platforms provide tools to identify objects, faces, or text from images.
- Time Series Forecasting MLaaS: These platforms use statistical models and algorithms to predict future outcomes based on historical data.
- Anomaly Detection MLaaS: These platforms use algorithms to detect patterns or anomalies in data that may indicate an unusual event.
Advantages of Using Machine Learning as a Service (MLaaS) Platforms
- Flexibility: MLaaS platforms provide users with the flexibility to customize and deploy machine learning models without having to develop a complex infrastructure. This makes it easy for businesses to quickly adapt their models and take advantage of new technologies without spending time or money on building out their own infrastructure.
- Scalability: MLaaS allows users to scale up or down as needed, making it easier for companies to handle large data sets and meet their evolving needs. With this feature, organizations can easily add more resources when needed or delete them when they’re no longer required.
- Cost-Effectiveness: Using an MLaaS platform is often more cost-effective than building a custom infrastructure from scratch. Organizations can focus on what they do best while leveraging the expertise of third-party providers without having to invest in expensive hardware and software solutions upfront.
- Accessibility: As MLaaS platforms are cloud-based, users have access to all the tools necessary for machine learning development from virtually anywhere in the world. This level of accessibility makes it easier for companies operating in multiple locations to collaborate effectively on projects across continents.
- Automation: Many MLaaS providers offer automated features such as automated model optimization which helps make model deployment simpler and faster by automating common tasks like data preprocessing, feature selection, setting hyperparameters, etc., saving time and resources that would otherwise be spent manually executing these tasks.
- Security: As MLaaS providers are responsible for the security of their systems, organizations can rest assured that their data is in safe hands. Most platforms also offer encryption, authentication protocols, and other advanced security features to ensure that user data remains secure.
- Compatibility: MLaaS platforms are designed to be compatible with other software and hardware solutions, making it easy to integrate machine learning models into existing systems. This ensures that organizations can combine their existing technologies with emerging ones in order to get the most out of their investments.
Who Uses Machine Learning as a Service (MLaaS) Platforms?
- Businesses: Businesses of all sizes can use MLaaS to develop and apply predictive analytics using web-based platforms. These platforms make it easier to access data, build machine learning models, and deploy them into production environments.
- Data Scientists: Data scientists often use MLaaS as a way to quickly experiment with different machine learning algorithms and rapidly prototype solutions. By leveraging the power of cloud computing, they can easily create complex models without a large upfront investment in computing resources.
- Developers: Developers often prefer MLaaS because they don't need to have an extensive knowledge of mathematics or computer science in order to use the service. The platforms provide an easy-to-use interface that allows developers to quickly spin up machine learning projects without having to learn too much about how the algorithms work.
- Marketers: Marketers frequently use MLaaS for forecasting customer behavior, predicting market trends, and automating decision making processes for marketing campaigns. This helps them analyze customer data faster and gain better insights into what tactics are most effective for their business goals.
- Researchers: Researchers across many disciplines utilize MLaaS as a way to access large datasets and develop powerful algorithms more efficiently than ever before. By harnessing the compute power available on these cloud services, researchers are able to run experiments faster while also reducing costs associated with acquiring hardware or software licenses needed for traditional research approaches.
- Startups: Startups often rely on MLaaS services to quickly build and deploy machine learning models without having to invest a large amount of capital. MLaaS platforms provide startups with the flexibility to begin prototyping projects without needing an extensive infrastructure, allowing them to focus more time and resources on developing their product instead of spending money on hardware.
How Much Do Machine Learning as a Service (MLaaS) Platforms Cost?
The cost of Machine Learning as a Service (MLaaS) platforms can vary greatly depending on the type of services offered and the complexity of the project. Generally speaking, MLaaS platforms provide cloud-based computing infrastructure that allows users to quickly and easily access advanced machine learning algorithms. The cost of these services can range anywhere from a few hundred dollars per month to thousands, depending on the level of support needed.
For smaller projects requiring basic or entry level machine learning capabilities, customers may be able to find MLaaS providers offering basic packages at lower price points. These packages often include limited access to features such as pre-trained models, datasets, and real-time predictions. Prices for these packages can typically start around $50-$100 a month but can go up depending on the number of users and features required.
On the other hand, larger and more complex projects requiring more sophisticated machine learning algorithms may require more expensive packages from MLaaS providers. These packages generally offer access to additional tools such as model training and evaluation capabilities, automated machine learning pipelines, performance monitoring tools, hyperparameter optimization services, etc., which come with higher price tags ranging from several hundred dollars per month up to several thousand dollars.
Overall, due to its wide range in offerings and prices, it is difficult to put an exact cost on MLaaS solutions without knowing more about the specific needs of each project. Customers should determine what their needs are first and then shop around for different options until they find one that best fits their budget.
What Software Do Machine Learning as a Service (MLaaS) Platforms Integrate With?
Software such as cloud-based databases, data processing tools, and application programming interfaces (APIs) can all integrate with machine learning as a service (MLaaS) platforms. MLaaS platforms are designed to enable developers to access machine learning capabilities without having to create complicated models from scratch. By integrating with these types of software, MLaaS platforms allow developers to quickly and easily access the data needed for their models - ensuring that they can get their applications up and running in no time. Furthermore, many MLaaS providers have comprehensive APIs that can be used to develop custom integrations with any other software or platform needed for the project at hand. This allows developers to quickly customize the integration of their project according to their specific requirements.
What Are the Trends Relating to Machine Learning as a Service (MLaaS) Platforms?
- MLaaS platforms are becoming increasingly popular as they reduce the need for businesses to buy expensive and complicated hardware, software and programming to access machine learning technologies.
- MLaaS providers are offering various models that can be customized for specific needs, such as natural language processing, predictive analytics, image recognition, etc.
- The platform often offers real-time feedback with the help of AI models that capture and analyze data in real-time to build predictive models.
- Many businesses are turning to MLaaS due to its scalability. This allows businesses to access more computational resources when needed without needing to purchase additional hardware or infrastructure.
- MLaaS providers have made it easier for developers and organizations to experiment with different types of algorithms quickly without needing specialized knowledge or large upfront investments.
- Due to the proliferation of cloud computing capabilities, MLaaS providers have been able to offer powerful computing power at an affordable price point for businesses who don’t have access or resources for expensive equipment.
- Companies are recognizing the value of using MLaaS platforms in their operations; this is resulting in a rising demand for software development teams who specialize in AI-driven solutions such as automated marketing campaigns and chatbots.
- MLaaS platforms are also becoming more affordable, allowing both small businesses and large corporations alike to benefit from the cost savings of using cloud-based services.
How To Pick the Right Machine Learning as a Service (MLaaS) Platform
- Identify the problem you are trying to solve. It is important to clearly define and understand the problem that you need your MLaaS platform to address in order to ensure that the right platform is selected.
- Research MLaaS providers. Once the problem has been identified, research into different MLaaS solutions available in the market and narrow down your selection based on features, cost, scalability, customer reviews, etc.
- Compare and contrast different platforms. Consider all available information before making a decision by comparing factors such as accuracy rates, ease of use, data requirements and pricing models of various platforms side-by-side. This will help you decide which service best fits your budget and other requirements.
- Test out the platform(s). After selecting a few options it is also recommended to test out these services with sample data or a trial period if one was offered by the provider so that you can get a better feel for how they work and if they fit your needs perfectly or require some adjustments first before committing to them long-term wise.
- Finalize the decision. Once you have tested out all available options, go through your notes and compare each platform based on their features, scalability, customer service and cost in order to make the final decision.
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