5 leading AI startups in MLops

In addition to the huge and growing demand for AI applications, there is a complementary thirst for the infrastructure and supporting software that enables AI applications. From data preparation and training to deployment, and beyond, many startups arrive in the field, MLops.. Let’s take a look at some of the more interesting things that make AI initiatives more successful.

Weights and biases

Weights and biases It has a significant presence in the field of machine learning, especially among data scientists who require a comprehensive and well-designed experimental tracking service. First, W & B can be quickly integrated with almost any popular machine learning library (and it’s easy to add custom metrics).

Second, you can use as many W & Bs as you need — as a turbocharged version Tensor boardIt can also be used as a way to control and report hyperparameter tuning, or as a collaborative center where everyone on the data science team can see the results and reproduce the experiments performed by other team members. For enterprises, W & B can also be used as a platform for governance and history, providing an audit trail that uses input, transformation, and experimentation to build a model as it moves from development to production.

Your data scientists certainly already know about W & B, and if they aren’t using it internally, they almost certainly want to be. If OpenAI, GitHub, Salesforce, Nvidia are using W & B, why?


Seldon Is another company that offers open core products that provide additional enterprise functionality on top. The open source component is Seldon Core. This is a cloud-native way to deploy models with advanced features such as any chain of models for inference, canary deployment, A / B testing, multi-armed bandit, and support for frameworks such as: is. TensorFlow, Scikit-learn,and XGBoost You can use it immediately. Seldon also provides an open source Alibi library for testing and explaining machine learning models. This library contains various ways to gain insight into how model predictions are formed.

An interesting feature of Seldon Core is its extremely flexible compatibility with the technology stack. You can use Seldon Core alone or insert it into a slot. Kubeflow Deployment.You can deploy the model created through MLFlow, Or you can use Nvidia Triton inference serverAs a result, there are various ways you can take advantage of Seldon to get the maximum gain.

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5 leading AI startups in MLops

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