Top 10 coolest machine learning tools you need to know in 2021

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by Satavisa Pati


July 20, 2021

The coolest machine learning tools you should know about in 2021.

Machine learning tools help companies understand trends in customer behavior and business operating models, as well as support the development of new products. Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to be more precise at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

Big Kraken Squid

Big Squid’s Kraken AutoML is a machine learning platform for building and deploying machine learning models for business analysis, including in existing analysis stacks, without the need to write code. Kraken’s code-less capabilities simplify adoption of machine learning and AI, helping data analysts, data scientists, data engineers and business users collaborate on machine learning projects and predictive analytics.

Spell DLOps

Spell.ML is developing a machine learning platform for deep learning operations (DLOps) that the company says goes beyond traditional machine learning with its capabilities to prepare, train, deploy and full lifecycle management of machine learning and deep learning models. Spell.ML claims that its cloud-independent platform can help reduce the costs of developing deep learning models. Deep learning is a segment of the machine learning world that integrates complex learning models based on AI-based neural networks and is often used for complex tasks such as image recognition and natural language processing. Deep learning models are computationally intensive and often require high performance systems running on next generation GPUs and AI processors.

Iterative.ai DVC Studio

In June, when Machine Learning Operations (MLOps) started, Iterative.ai launched DVC Studio, a visualized user interface based on the open source Data Version Control (DVC) and Continuous Machine Learning (CML) projects. Iterative.ai is the company behind the development of these projects and DVC Studio is its first commercial product. DVC studio simplifies the development of ML models and improves collaboration by extending traditional software tools such as Git and CI / CD (continuous integration and continuous delivery) to meet the needs of ML researchers, ML engineers and data scientists.

Arrikto Enterprise Kubeflow

Arrikto says the goal of its technology is to apply the same DevOps principles that are used for developing and deploying software to manage data throughout the machine learning lifecycle – which the company calls “treat data as code”. Enterprise Kubeflow is Arrikto’s machine learning operations platform, which the company says “simplifies, accelerates and secures” the entire machine learning model development lifecycle through to production, enabling MLOps teams to accelerate time-to-market for machine learning models 30 times faster than traditional ML platforms. Enterprise Kubeflow provides automated workflows, repeatable pipelines, secure data access, and consistent desktop-to-cloud deployment.

tecton

Startup Tecton has garnered a lot of attention with its enterprise feature store technology for machine learning. The company, which left stealth in April 2020, was founded by the developers who created Uber’s Michelangelo machine learning platform. Feature stores are an essential component of the machine learning stack. They are used to create and deliver data to production machine learning systems – what the company says is the most difficult part of operationalizing machine learning. Delivered as a fully managed cloud service, the Tecton Feature Store manages the full lifecycle of machine learning features, enabling ML teams to build features that combine batch, streaming, and real-time data. Tecton claims that his system orchestrates feature transformations to continuously transform new data into new feature values. Features can be availed instantly for online training and inference with operational metrics monitoring. And ML teams can use Tecton to research and discover existing functionality to maximize reuse between models.

DotData Py Lite

DotData, a developer of AI automation and operationalization tools, offers DotData Py Lite, a containerized AI automation system that enables data scientists to quickly deploy the DotData system to their desktops and run proofs of machine learning concepts. Py Lite, launched in May, is designed for Python data scientists and provides automated feature engineering and automated machine learning in a portable environment.

OctoML Octomizer

The OctoML Octomizer acceleration platform is used by engineering teams to deploy machine learning models and algorithms to any hardware system, cloud platform, or edge device. Octomizer automatically optimizes and compares the performance of machine learning models, helping to bridge the gap between building ML models and going live, according to the company. Octomizer is a commercial version of Apache TVM, an automated deep learning model compilation and optimization stack that was developed by the founders of OctoML. Octomizer has been available for early access since December 2020.

Explorium external data platform

Explorium’s external data platform automatically discovers thousands of relevant data signals and uses them to improve the performance of machine learning models and the predictive analytics they generate. At the heart of the platform is the AutoML engine that powers automated system data discovery, automated feature generation and selection, and model building and deployment capabilities.

Neuro-thinking

Neurothink, which just came out of stealth in May, offers its machine learning platform Neurothink as an alternative to building models on public cloud service platforms. The company says its goal is to bring ML capabilities to a wider audience with less complexity and significantly improved API security in machine learning workflows. The Neurothink system, based on high performance GPU and CPU servers, provides an end-to-end environment and tools for building, training and deploying machine learning models.

Qeexo AutoML

The Qeexo AutoML platform focuses on automating end-to-end machine learning for advanced computing devices, automatically creating what the company calls “tinyML” machine learning models. Qeexo is particularly targeted at Internet of Things and Industrial IoT applications, developing ML models for integrated sensors for anomaly detection, predictive maintenance and other tasks.

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