As the adoption of AI continues to accelerate, the role of platforms that handle that data becomes even more important; OpenAI's famous GPT-3 is estimated to have been trained on around 45 terabytes of text data alone.
Seeing the scope of the challenge needed for large language models and other AI models, data science and machine learning platforms are essential tools for organisations looking to harness the power of data to drive decision-making and innovation.
Using data gathered across a number of sources, and considering factors such as core functionality, key features, usability, and user feedback, AI Magazine weighs in on 10 of the leading data platforms in the field, that are improving AI abilities or ushering in new applications.
10: KNIME
KNIME (Konstanz Information Miner) is an open-source platform for data analytics, reporting, and integration. It provides a graphical interface for building data workflows, integration with popular machine learning libraries, and tools for deploying models into production.
KNIME combines an open-source analytics platform with a commercial KNIME Hub software package that supports team-based collaboration and workflow automation, deployment, and management.
9: Altair RapidMiner
RapidMiner is a data science platform that provides a visual workflow designer for building machine learning models.
It offers a wide range of pre-built templates, support for various data sources, and tools for model validation and deployment. RapidMiner makes it easy for expert data scientists and citizen data scientists to work collaboratively and manage end-to-end data science pipelines.
8: Dataiku
Dataiku is a data science and machine learning platform that enables teams to collaborate on data projects. It provides a user-friendly interface for data preparation, exploration, modeling, and deployment. Dataiku supports various data sources and integrates with popular tools like Python, R, and Spark.
The platform automates repetitive tasks and streamlines the end-to-end machine learning lifecycle, making it accessible to both technical and non-technical users.
7: H2O.ai
H2O.ai provides an open-source platform for building machine learning models. It offers a range of tools, including H2O-3 for scalable machine learning, Driverless AI for automated machine learning, and H2O Wave for building AI applications.
H2O.ai is recognised for its robust machine learning capabilities and support for a wide range of algorithms and data sources.
6: DataRobot
DataRobot is an automated machine learning platform that accelerates the process of building and deploying predictive models. It offers a user-friendly interface, automated feature engineering, and model selection, making it accessible to both data scientists and business analysts.
DataRobot supports a range of regression, classification, and time series algorithms, providing model interpretability and bias detection.
5: Databricks Unified Analytics Platform
Databricks Unified Analytics Platform combines data engineering and data science capabilities in a single platform. It is built on Apache Spark and provides a collaborative workspace for data teams to build and deploy machine learning models at scale.
Databricks is known for its ability to handle large-scale data processing and its integration with popular data sources and machine learning libraries.
4: IBM Watson Studio
IBM Watson Studio provides a collaborative environment for data scientists, application developers, and subject matter experts to work together on machine learning projects.
It offers tools for data preparation, model building, and deployment, along with integration with IBM's AI services. Watson Studio is recognised for its robust analytics capabilities and support for a wide range of data science and machine learning tasks.
3: Amazon SageMaker
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly. It offers a range of built-in algorithms, support for popular frameworks, and robust deployment capabilities.
SageMaker is known for its scalability, ease of use, and integration with other AWS services, making it a popular choice for enterprises.
2: Google Cloud Vertex AI
Google Cloud AI Platform offers a comprehensive suite of tools for developing, deploying, and managing machine learning models.
It integrates seamlessly with TensorFlow and provides advanced features like AutoML, which allows users to build high-quality models with minimal effort. Google Cloud AI Platform is recognised for its robust infrastructure, scalability, and support for a wide range of machine learning tasks, making it a top choice for organisations looking to leverage AI and machine learning.
Additionally, Google Cloud AI Platform provides pre-trained models and APIs for various AI applications, such as natural language processing, computer vision, and speech recognition.
It also offers managed services like AI Platform Notebooks for collaborative data science workflows and AI Platform Pipelines for automating and orchestrating machine learning pipelines. Google's expertise in AI and its commitment to open-source technologies like TensorFlow make its AI Platform a compelling choice for enterprises seeking to accelerate their AI adoption.
1: Microsoft Azure Machine Learning
Microsoft Azure Machine Learning is a cloud-based service that provides a robust environment for building, training, and deploying machine learning models.
It offers a wide range of tools, including automated machine learning, drag-and-drop interface, and integration with popular frameworks like TensorFlow and PyTorch. Azure Machine Learning is known for its comprehensive suite of features, scalability, and integration with other Microsoft services, making it a leading platform for data science and machine learning projects.
Its ability to support end-to-end machine learning workflows and its strong enterprise capabilities make it the top choice for organisations worldwide. Azure Machine Learning also provides features like responsible AI, which helps in assessing model fairness and mitigating biases, and MLOps capabilities for streamlining the deployment and monitoring of machine learning models. Microsoft's extensive experience in enterprise software and its commitment to open-source technologies like PyTorch contribute to the strength of its Azure Machine Learning platform.