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Many AI start-ups in MLops have been joining the field

With technological advancements, AI applications have accelerated rapid growth as there is a huge demand for infrastructure and software that supports AI applications. Many start-ups have been joining this field of MLops. Here are 5 leading AI start-ups in MLops in 2021

5  Data Robot

Data Robot wants to own a company’s AI lifecycle starting from data preparation till the production deployment. The features of Data Robots include relating to the web UI which can simplify the data and it can also assist users by automatically clearing previous data.  The Humble AI feature adds to the company as it lets the user place additional guardrails in case of any low probability event occurring during the prediction. The unique quality of Data Robots is that they can install their own data center and bare metal in Hadoop clusters and can deploy cloud services to private and managed companies.

4 absorbs and runs the simplifications that PyTorch Lightning brings, and trains models using temporary resources of the GPU. manages all provisioning of infrastructure resources in the background by ensuring that the datasets are optimized for large-scale use.

It’s best for a streamlined training pipeline for data scientists that can minimize cloud costs.

3 Pinecone/ Zilliz

The two start-ups Pinecone and Zilliz, offer vector search to businesses. It is a pure SaaS that offers uploads by machine learning model to the server and submits a query via the API. The team of Pinecone handles all the aspects ranging from security to operational concerns hassle-free.

2 Seldon

Seldon also offers open core products that can provide additional enterprise functionality on top. The core of the company is its open-source component. It also provides an open-source Alibi library for testing and explaining machine learning models. The unique feature of Seldon Core is flexibility with the technology stack.

1 Weights and biases

Weights and biases have made a mark in the field of machine learning impacting the data scientist looking for well-designed experimental tracking service. The W&B can quickly integrate with all machine learning libraries. It can also be used as a way to control hyperparameters where everyone in the team can see results and reproduce the experiments.

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