As businesses collect more data, machine learning and other AI technologies are needed to turn data into insights, drive business decisions, and improve products and services. Machine learning platforms and tools use data and clever algorithms to provide business insights and scale new solutions.
In this blog, we will discuss machine learning platforms, what types there are, how to choose the right ones and the top machine learning platforms that you can consider. Check it out!
What are Machine Learning Platforms?
Machine learning platforms are where computers learn and develop without being programmed. It involves creating models and algorithms to analyze data, find trends, and make conclusions.
A cutting-edge AI application, it allows the system to learn and develop autonomously through experience. A machine learning platform automates and speeds up massive data-processing predictive application delivery.
Building blocks on a competent ML platform lets data scientists solve any data science challenge. Data scientists have complete latitude to incorporate solutions into products.
>> Read more: Top 7 Machine Learning Solutions For Growing Your Business
Types of Machine Learning Platforms
All machine learning platforms can be categorized into seven broad categories by their main focus.
Business Intelligence
Statistics science solutions for business intelligence analyze market research, website visitor statistics, sales numbers, financial records, and other data most organizations already record.
All these platforms share point-and-click interfaces and preconfigured algorithms, which are easy to use, pricey, favor domain expertise over data science, and assume deeper service provider collaboration.
Data Management
Data management platforms store and query data. These are your best options if you can write Spark jobs but don't have the in-house experience or ability to maintain massive data clusters.
The interface to access is an abstraction layer below most categories but higher than infrastructure-focused platforms.
Digitalization Data
Data automation, predictive maintenance, productivity bottleneck detection, and uptime projections are used to digitalize manufacturing and other traditional industries. The analyzed data is domain-specific, such as machine sensors or car fuel usage.
These infrastructure data science systems feel more like IaaS than PaaS or SaaS. It takes a lot of glue code to get a machine learning system started, unlike business intelligence solutions. They suit companies that need customized solutions.
Lifecycle Management
Lifecycle management platforms create machine learning solutions around projects and workflow. Define the problem scope, acquire/explore/transform associated data, model solution hypotheses, and deploy/version/monitor the prediction-giving model. These are the most complete end-to-end services that require little glue code but retain extensibility.
Notebook Hosting
For exploratory data research, notebook hosting services offer Jupyter or RStudio notebooks. Individual data scientists should start here, but shared notebooks might compound technical debt to your machine learning system if they are your main mechanism of versioning and releasing code.
Record-keeping Tools
Record-keeping tools visualize machine learning pipeline phases and document artifacts like models. These platforms are mostly add-ons that start reporting without running code. Most platforms have similar features, however, a specialized machine learning system works well in some cases and extra record-keeping wouldn't harm.
Top 9 Useful Machine Learning Platforms for Your Businesses
Amazon Sagemaker
Amazon Sagemaker is a full-featured managed service that gives machine learning developers and data scientists the tools they need to quickly build, train, and use machine learning (ML) models. It's easy for data scientists and developers to use machine learning models on edge devices and embedded systems.
It was made by Amazon Web Services (AWS), which has the most machine learning services and the most advanced cloud infrastructure to serve them.
Key Features:
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Automation simplifies data labeling.
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It supports all popular machine-learning techniques and frameworks.
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Provides data scientists and developers with tools.
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Integrating with AWS IAM, it offers extensive security and compliance.
Google Cloud Machine Learning Engine
Google Cloud ML Engine is a controlled service that lets developers and data scientists use the power of Google Cloud to build top-notch machine learning models.
Key Features:
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TensorFlow and scikit-learn are supported.
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Provides online and batch predictions.
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Built inside Google Cloud's big data and storage tools.
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Auto-scales to meet load increases.
Microsoft Azure Machine Learning
Microsoft Azure Machine Learning is a platform that runs in the cloud and lets you create, train, test, publish, manage, and keep an eye on machine learning models.
Through its Azure software tools, Microsoft offers its services in the fields of data science and machine learning. The Azure Machine Learning Studio in the cloud is great for people who want to use their data to build, test, and run predictive analytics and machine learning solutions.
Besides this, Microsoft Azure makes it simple for users to create, launch, and share cloud-based advanced analytics solutions.
Key Features:
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An interactive workspace for ML resource management.
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Quickly find the optimal model with automated ML.
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Azure ecosystem-integrated.
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Strong MLOps (DevOps for Machine Learning) capabilities.
TensorFlow
TensorFlow, an open-source machine learning library, supports end-to-end platform needs. It supports complicated neural networks and deep learning model creation as well as simple ML model development.
TensorFlow rarely calls itself an MLOps platform, but it has the open-source flexibility, extensibility, and full-lifecycle features MLOps teams need to prepare data, construct models, deploy, and monitor models. For scaling ML pipelines, training and analyzing models, and deploying models in production, TensorFlow Extended (TFX) is ideal.
Key Features:
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Pretrained model garden and TensorFlow Hub models.
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On-premises, mobile, browser, and cloud deployments.
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Easy ML add-on for Google Sheets model training and evaluation.
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Production-ready ML pipelines.
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Prepared data and responsible AI technologies eliminate bias.
Databricks Data Intelligence Platform
Databricks Data Intelligence Platform provides a centralized environment with sophisticated tools and capabilities for machine learning and data preparation for ML model construction.
One notable feature is Managed MLflow, which uses Databricks' open-source platform to manage complicated ML lifecycle interactions. This platform is ideal for organizations that need self-service and guided data management and feature engineering for data from many sources and formats.
Users can utilize the platform to process and prepare data for generative AI, massive language models, and data production pipelines. Model Registry lets them register and manage models. The platform also offers collaborative notebooks, the Feature Registry, and the Feature Provider to assist feature engineering and MLOps with a big-data approach.
Key Features:
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Lakehouse-style open design.
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A REST-API-driven model deployment.
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Trained and optimized LLM integration.
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Data pipeline self-service.
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Tracked and versioned MLflow experiments.
Alteryx
Alteryx is the best tool for data science that speeds up the digital transformation. It gives you access to data and data science tools. Alteryx gives data scientists a way to build models that work with their work.
Their goal is to make it easy for businesses to develop a culture of data analytics without having to hire data scientists. Alteryx is always the leader in self-service data analytics.
Key Features:
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Free deployment and management of analytic models.
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Support most data sources.
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Data workflow simplified via drag-and-drop.
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Offers data blending, cleansing, and preparation solutions.
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Supports predictive, statistical, and spatial analytics.
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Works with Tableau and other visualization tools.
RapidMiner
RapidMiner has a platform for data science that can help people build and run AI systems. It includes every step of the AI creation process, from exploring and cleaning data to building models, deploying machine learning models, and running models.
It has the depth that a data scientist needs to make AI easier to understand through a visual user interface that makes building and understanding complicated models much faster.
Key Features:
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Visual machine learning model workflow designer.
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Model selection and hyperparameter adjustment are automated.
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Supports 1500+ algorithms and functions.
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Manages on-premise and cloud data.
DataRobot
An enterprise AI machine learning platform from DataRobot makes it quick and simple to build, manage, and use AI.
It comes with Automated Machine Learning, Automated Time Series, and MLOps, all of which are separate tools that work together perfectly.
All of them can be easily used in different ways depending on what the business needs. By getting the best predictive model for your data, it helps you figure out how to run your business.
Key Features:
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An engineering and data science collaborative workplace.
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Supports major data sources and file systems.
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Supports popular programming languages such as Python, SQL, R, and Scala.
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Provides open-source ML lifecycle management platform MLflow.
>> Read more: Why Should Python Be Used For Machine Learning?
H2O.ai
The best data scientists can use a lot of different AI and data science tools from H2O.ai. Anybody can use it, and it's a distributed in-memory machine-learning tool that can grow in a straight line.
For people who are looking for deep learning platforms, H2O offers statistical and machine learning algorithms. This is because one of the best things about it is that it can do deep learning.
It has Deep Water for deep learning, but H2O is still the best tool for combining data science and machine learning.
Key Features:
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Processing huge text blocks and building models with NLP.
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Has around 30 pre-trained image transformers and models for image processing.
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Creates data plots and visualizations automatically.
>> Read more:
- Top 9 Best Deep Learning Frameworks for All Levels Developers
- Top 17 Best AI Apps For Individuals and Businesses
How To Choose The Right Machine Learning Platform?
While tackling tough computational issues is the goal, machine learning software differs widely. Review suppliers and platforms and learn how features and tools function. The following features are crucial when choosing machine learning software:
Process and Ingestion Data
Understanding how the program ingests data, what data formats it supports, and whether it can automate data splitting is crucial. Not all packages have several templates and connectors.
Support for Feature Engineering
Feature engineering is essential for data manipulation and algorithm development. The integrated intelligence turns text, dates, and other variables into meaningful patterns and information the ML system uses to provide outcomes.
Algorithm, Framework Support
Most modern ML platforms offer several algorithms and frameworks, which is important. Some business processes involve dozens or hundreds of algorithms. However, automated algorithm selection that suggests and matches algorithms with tasks is crucial. This feature usually simplifies and boosts ML performance. Having many frameworks increases users' flexibility when automating ML development chores.
Training and Tuning Tools
Algorithm performance and ML framework commercial value must be assessed. Most users benefit from sophisticated hyperparameter adjustment, which simplifies algorithm optimization. varied packages have varied tools and capabilities, therefore some work better for certain tasks and algorithms. Regardless of your use case, you'll want tools that make training and fine-tuning straightforward for huge language models and other ML models.
Gathering Tools
ML often uses numerous algorithms to complete a task. This balances strengths and weaknesses and reduces data bias. Ensembling—integrating and employing algorithms—is a fundamental component of ML systems.
Competition Modeling
Competition modeling is often needed because algorithms and ML models cannot be tested before deployment. As the name implies, this compares multiple algorithms' event prediction accuracy and value. Thus, the best algorithms are chosen.
Deployment Tools
ML model implementation requires many phases, and any inaccuracy might lead to poor results or failure. To avoid these challenges, an ML platform should include automated capabilities and, in some cases, one-click deployment. Many top-tier tools support experimental and production deployment methods.
Dashboards, Tracking
Visibility into the machine learning model's performance, techniques, and evolution to suit new needs is crucial. Dashboards and monitoring tools with filters and visuals let stakeholders examine vital data. An organization can add, remove, and alter ML models using this visibility.
Conclusion
Since different platforms have distinct characteristics, your organization may need one of the machine learning platforms. Some tools excel at data preparation for high-quality model creation. Others provide frameworks and integrations for model building. Some provide recommendations and guided support to optimize existing models for future performance.
Remember about features to choose the right machine learning platform for your projects and businesses!
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