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5 AI startups leading the Machine learning Ops Field


5 AI startups leading the Machine learning Ops Field

5 AI Startups You Should Know About 'Leading the  Machine learning  Ops Field'

As interest in AI rises, the thirst for software and infrastructure to utilize AI is also emerging. As a result, many startups have emerged that will guide you to a new world called  Machine learning ops. From data preparation and training to deployment and beyond, they also target a variety of locations. Here are some particularly interesting companies.

Machine learning: W&B

W&B (Weights & Biases) has established itself as an influential figure in the  Machine learning field. It is especially popular among data scientists who want a well-designed, comprehensive experiment tracking service. 

W&B's services have various characteristics. First, it integrates almost all of the popular  Machine learning libraries out of the box. (It is also very easy to add custom indicators.) Second, W&B can be used as much as the user needs. For example, it can be used as an output-enhanced version of Tensorboard , or as a control and reporting means for hyperparameter tuning. Finally, it can be used as a collaboration center where everyone on the data science team can check the results or replicate experiments run by other team members. 

Businesses can also use W&B as a governance and provenance platform. This is because the model provides an audit trail of which inputs, transformations, and experiments were used to build the model as it goes from development to production.

If you have data scientists around, it's very likely that they already know about W&B. And if you're not using W&B within your company, you're almost certainly going to want to. OpenAI, GibHub, Salesforce, and Nvidia are also using W&B. 

Machine learning: Seldon

Seldon(Seldon) is an open core services company that provides additional enterprise features. An open source component, Seldon Core, deploys a model of advanced functionality in a cloud-native manner. Advanced features here include arbitrary model chains for inference, canary deployment, A/B testing, Multi-Armed Bandit, and TensorFlow, Scikit-learn, XGBoost and such as out-of-the-box support for the same framework. Seldon also provides an open-source Alibi library for checking and describing  Machine learning models. Various techniques are included to gain insight into how model predictions are formed.

An interesting feature of the Seldon Core is that it is incredibly flexible in terms of compatibility with user technology stacks. Sheldon cores can be used alone or placed in a Kubeflow batch. You can deploy models created via  Machine learningFlow, or you can use Nvidia's Triton Inference Server. In other words, it allows you to achieve maximum effect in a variety of ways.

An enterprise-facing solution is Seldon Deploy. It provides comprehensive tools for model management, including dashboards, audit workflows, and performance monitoring. It is aimed at data scientists and SREs, as well as managers and auditors. The UK-based startup Seldon has gained huge popularity in the financial world thanks to its focus on auditing and accountability. For example, Barclays and Capital One are using Seldon's services.

With many competitors in the model deployment space, Seldon is focused on providing a comprehensive set of features while providing a Kubernetes deployment as a core service. It also offers enterprise add-ons useful for companies looking for a more end-to-end solution.

Machine learning: Pinecon/Zilliz

Vector searches are very hot these days. Vector search is revolutionizing the search field thanks to recent advances in  Machine learning across domains such as text, images, and audio. For example, a search for 'Kleenex' retrieves tissues selected by the distributor, without the need for a synonym substitution custom rule. This is because the language model used to generate vector embeddings places search queries in the same region of the vector space. This same process can be used to localize sounds or perform facial recognition.

Although today's search engine software is often not optimized for performing vector searches, Elastic and Apache Lucene(Apache Lucene) continues to be studied. In addition, several open source alternatives provide fast vector search capabilities at scale (eg NMSLib, FAISS, Annoy). 

In addition, a number of startups have emerged to ease the burden of the Ops department to install and maintain a vector search engine. Pinecone and Zilliz, for example, are startups that offer vector search for businesses. 

Pinecon is a pure SaaS service. Users upload the embeddings created by their  Machine learning  model to the Pinecon server and send a query through the Pinecon API. All aspects of hosting, including security, scaling, speed, and other operational issues are handled by the Pinecon team. This means users can launch a similarity search engine in just a few hours. 

Zilliz will soon launch a managed cloud solution called Zillow Cloud. This is an open-source library called Milvus , which takes an open core method. Milbus provides a simple deployment of vector search engines with an easy-to-use and expressive API that developers can use to build and maintain their own vector indexes, covering commonly used libraries such as NMSlib and FAISS. 

Machine learning: is PyTorch Lightning(PyTorch Lightning) is a work of related people. PyTorch Lightning is a popular high-level framework based on PyTorch that generalizes many of the common PyTorch standard provisions, making it easy to train on one or 1,000 GPUs with a few parameter switches. takes the simplification brought by PyTorch Lightning and advances it. This allows data scientists to train models using transient GPU resources as seamlessly as when running code locally. 

Want to run a hyperparameter sweep across 200 GPUs at once? lets you do just that. It manages all provisioning (and decommissioning) of infrastructure resources behind the scenes, ensuring that your datasets are optimized for use at scale. In addition, it provides all indicator reports bundled with an easy-to-use web UI. can also be used to spin up instances from the console or connected to a Jupyter Notebook for interactive development.'s efforts to streamline model training at scale are particularly useful for companies that need to regularly spin up training runs that occupy more than 100 GPUs at a time. However, it remains to be seen how many such customers exist. Still, if you need a streamlined training pipeline for your data scientists that minimizes cloud costs, is worth a closer look. 

Machine learning: data robot

data robot(DataRobot) is a company that seeks to cover the entire user's enterprise AI lifecycle, from data preparation to production deployment. DataRobot's data preparation pipeline is fully equipped with web UI features that are useful when you want to facilitate data enrichment. It also includes tools to help users (beginners or experts) automatically profile, cluster and organize data before feeding it into the model.

An automated Machine learning  facility on the DataRobot trains a pair of models on the target. Thus, users can choose either the model with the highest performance or their own uploaded to the platform. For deployment, the platform's integrated  Machine learningOps module tracks everything from uptime to data drift over time. Therefore, you can always see the performance of the model at a glance. 

There's also a feature called Humble AI, which gives the model an extra layer of protection in case low-probability events occur at predicted time. Of course, such events can also be tracked through the  Machine learning Ops module.

What makes DataRobot different from most of the other startups introduced on this page is that it is deployed on bare metal in the user's own data center and Hadoop cluster, while also being deployed in private and managed clouds. This can be interpreted as a willingness to compete in every part of the upcoming enterprise AI platform battle, dealing with clients ranging from fast-moving startups to established Fortune 500 companies.

 Machine learning Ops is one of the hottest AI fields today. As more companies enter the AI ​​space, the demand for accelerators, platforms, management and monitoring will continue to grow. Companies wanting to join the AI ​​Gold Rush can get pickaxes and axes from the 5 startups mentioned above!