Companies have recognized the latent power of machine learning and AI technology. Hence, they are investing heavily in ML and AI to gain a competitive edge in the new market. A BCG survey indicates that at least six in ten companies are developing algorithms and hiring data scientists in the current trend of machine learning and AI.

Unfortunately, only one in ten of these companies derives a significant return on investment from their AI and ML projects. A big reason why most machine learning projects fail is the chasm between data science and computer science practitioners.

Computer technologists burn the midnight oil to make ML projects usable and stable, while the data scientist is an experimenter who breaks and iterates projects for maximum efficiency. That said, your company can stand out from the crowd and accelerate the development of its AI innovations by adopting machine learning operation tools (MLOps).

The Qwak platform, for example, will unify your data science and IT teams, creating allied ML project data and engineering operations. Qwak eliminates all engineering inefficiencies from the code, data and configuration processes of an ML project.

Therefore, your ML project will not go down the drain due to friction between your teams. Instead, Qwak fulfills its mandate by supporting the build, deploy, maintain, and monitor phases of your ML project on an automated, streamlined platform.

Features of the Qwak platform

Although there are other machine learning (ML) production platforms such as Amazon SageMaker, Google’s Vertex AI, MLflow, and IBM Watson, Qwak’s advanced features are above the rest. To illustrate the point, Qwak’s Build feature can turn your project’s code into a production-grade self-learning bot in minutes.

Build is the entry point of the Qwak ML project. It will standardize your project’s code, data, and settings and define the logic for creating your model. Besides that, Build can work as an ML model training tool. If you have a pre-trained model, upload its data to Qwak and speed up its deployment date.

The Qwak build tool has an easy-to-use code screen that will highlight your project’s complete code and other model builds. Qwak’s service feature, on the other hand, is a high-end ML project management and deployment tool.

Qwak Serving supports one-click deployment of ML models via Command Line Interface (CLI), User Interface (UI), or the Qwak SDK. The one-click ML project deployment features of this powerful tool eliminate friction between engineers and data science teams, creating scalable and repeatable AI projects. Other Serving features include alerting, analysis, and logging functions.

Plus, Qwak’s Data Lake, where your team can collect, store, and analyze data, and then collaborate on managing it. A data lake supports the seamless transformation of raw, unstructured data into SQL analysis, machine learning, and data science-ready format.

Unlike other machine learning (ML) production platforms, the Data Lake is an off-the-shelf product, an integral part of the Qwak platform. As a result, your teams can store huge amounts of raw data on its object storage and flat architecture systems for easy access, at a lower cost.

Finally, the Qwak machine learning (ML) production platform has a feature store and automation capabilities. The Feature Store improves collaboration between all ML teams by creating a single source of truth for all ML features.

The Qwak Automations feature, on the other hand, configures ML project triggers, improving the operational monitoring of the ML project in the production environment.

Conclusion

The most successful AI teams have robust MLOps tools like Qwak in their toolbox. Qwak will train, test and deploy your ML model much faster, predictably and repeatedly, achieving higher returns on ML investments.

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