Feature
Companies turn to machine learning (ML) to gain insights from the vast amounts of data they’ve accumulated. By training an ML model on a subset of their data, businesses can create models to provide all sorts of analytics — from forecasting product demand to fraud detection and optimizing interactions with their customers. A number of data companies offer products to companies seeking to build such models, with tools that allow teams to collaborate and accommodate users with various levels of expertise in ML. Here, we look at some of the top companies offering machine learning products in the AI market.
3. Microsoft
Microsoft offers Azure Machine Learning to help companies build ML models at scale. It allows users to streamline deployment and management of thousands of ML models. It uses repeatable pipelines to automate ML workflows and continually models various metrics in the models, detects data drift and triggers re-training of models to improve their performance. It allows users to rapidly create models for tasks, such as classification, regression, vision and natural language processing (NLP). Azure builds in security and compliance for the models created. Microsoft also offers courses in ML to help people get started.
SAS offers Viya, an API that includes visual data mining and machine learning, a visual and programming interface. It automatically identifies the most common and most important variables across all ML models and uses natural language generation to provide a project summary in simple language. It allows users to embed open-source code within an analysis, call algorithms within a pipeline and access models from a common repository, making it easy for people in an organization to collaborate. It allows parallel processing and offers building blocks for pipelines, so different approaches can be compared quickly. Users can visually explore data and generate synthetic data for their models. SAS also offers training for learning their software as well as professional certification for AI and ML.
Dataiku offers AutoML, which provides builders of ML models with automatic feature generation. It lets them find reference sets in the company’s feature store and integrate them into their projects. It is designed to be used by people of varying levels of expertise, allowing users to accept default settings or modify any part for their own needs. It offers a guided methodology for developing models, with built-in guardrails and explainability that allow users to compare models. Advanced developers can create custom models using languages, such as Python, R, Scala, Julia and Pyspark or import models developed with MLFlow.
8. Databricks
Databricks provides a platform for building ML models that includes taking in data, selecting features, tuning and turning the model into a product. It allows users to automatically track experiments, code, results and artifacts in one central hub. Fine-grained access control and data lineage help users meet compliance needs. It allows a team to work together by providing collaborative notebooks that support several languages, including Python, R, Scala and SQL. They can also use tools such as Jupyter Lab, PyCharm, IntelliJ or RStudio.
9. Mathworks
Mathworks offers MATLAB to engineer features from data and fit ML models. Its Classification Learner app trains models to sort data using various classifiers. Users can perform automated training to search for the best classification type for their needs. The Regression Learner app trains models to predict data. Both apps allow supervised learning, using a known set of input data and known responses. Trained models can be integrated with the company’s Simulink system. The system can also generate C/C++ code, allowing trained models to be deployed to hardware systems.
1. IBM
IBM is a leader in ML, offering services to companies through its Watson Machine Learning, which allows users to build, train and deploy models. It provides a variety of tools that allow users to choose an appropriate level of automation for their needs. They can automatically process structured data to create pipelines of candidate models, then select the best-performing pipeline. Data scientists, developers and domain experts can collaborate on managing model data. IBM also offers training courses on using machine learning and provides professional ML certification.2. NVIDIA
NVIDIA, the maker of graphics processing units (GPUs) that provide the compute power to handle ML, offers companies help in accelerating their ML operations, whether they’re building a new model from scratch or fine-tuning their existing processes. The company’s offerings combine hardware and software optimized for high-performance ML, so companies can make the best use of their data. NVIDIA’s parallel computing platform, Compute Unified Device Architecture (CUDA), and its RAPIDS open-source libraries allow data scientists to speed up ML pipelines on GPUs. Overall, the company increases the efficiency of ML, letting customers use massive amounts of data and try new iterations of features.3. Microsoft
Microsoft offers Azure Machine Learning to help companies build ML models at scale. It allows users to streamline deployment and management of thousands of ML models. It uses repeatable pipelines to automate ML workflows and continually models various metrics in the models, detects data drift and triggers re-training of models to improve their performance. It allows users to rapidly create models for tasks, such as classification, regression, vision and natural language processing (NLP). Azure builds in security and compliance for the models created. Microsoft also offers courses in ML to help people get started.4. Google
Google Cloud offers the Vertex AI platform to allow data scientists and engineers to create, train, test, monitor, tune and deploy ML and artificial intelligence (AI) models. It includes Model Garden, a collection of more than 130 foundation models from Google and its partners, letting businesses choose an existing model that fits their needs, which they can then customize with their own data. AutoML lets developers who have limited expertise in ML train models specific to their needs. Google also provides access to hardware for different needs, including GPUs, CPUs and Tensor Processing Units (TPUs). The company offers courses in ML as well.5. Amazon
Amazon Web Services (AWS) offers ML products, infrastructure and deployment resources. Its SageMaker is a fully managed service that enables high-performance ML. The integrated development environment uses tools, such as notebooks, debuggers, profilers and pipelines. It supports various ML frameworks, toolkits and programming languages, including TensorFlow, Pytorch and Jupyter. Amazon also offers high-performance infrastructure, such as NVIDIA H100 Tensor Core GPUs. The company offers training through AWS Skill Builder as well.6. SAS
SAS offers Viya, an API that includes visual data mining and machine learning, a visual and programming interface. It automatically identifies the most common and most important variables across all ML models and uses natural language generation to provide a project summary in simple language. It allows users to embed open-source code within an analysis, call algorithms within a pipeline and access models from a common repository, making it easy for people in an organization to collaborate. It allows parallel processing and offers building blocks for pipelines, so different approaches can be compared quickly. Users can visually explore data and generate synthetic data for their models. SAS also offers training for learning their software as well as professional certification for AI and ML.
7. Dataiku
Dataiku offers AutoML, which provides builders of ML models with automatic feature generation. It lets them find reference sets in the company’s feature store and integrate them into their projects. It is designed to be used by people of varying levels of expertise, allowing users to accept default settings or modify any part for their own needs. It offers a guided methodology for developing models, with built-in guardrails and explainability that allow users to compare models. Advanced developers can create custom models using languages, such as Python, R, Scala, Julia and Pyspark or import models developed with MLFlow.
