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The concept that makes the technological miracles of today possible are defined by data. Enormous amounts of data are collected...
Are you involved in ML training and development but non-proficiency in coding gives you headaches when testing new models and making predictions? AWS SageMaker assists data scientists, business analysts, and engineers in deploying and training efficient models with minimal ML or coding skills!
Organizations can implement machine learning (ML) solutions to decrease costs and promote business growth in challenging verticals such as predicting customer churn, credit scoring, late shipment predictions, best offers identification, pricing, manufacturing quality improvement, and demand forecasting.
Developing ML through the traditional cycles requires data science skills and reliable engineering knowledge, and it can take months to finish. Analysts who have bright ideas have to rely on data science teams since they lack these skills while data scientists are constantly working on more sophisticated projects which fundamentally increases the time-to-market (TTM) for a certain product, and slows down growth.
AWS SageMaker helps accelerate this process with the use of tools like Amazon SageMaker Canvas, which assists analysts in working with data from data warehouses, building ML models, and performing predictions and batch scoring for large datasets, all while writing almost no line of code.
Data scientists and analysts can utilize AWS SageMaker to cooperate with each other efficiently and exchange datasets, and models and build them from the ground up through a simplified point-and-click interface. The integration of the AutoML functionality into tools like Amazon Redshift, Domo, and Snowflake, is especially helpful while building these ML models, especially for non-experts.
The process is quite straightforward and it can be put into these steps:
Analysts aren’t required to master ML in order to produce ML models thanks to the no-code ML workspace offered in SageMaker Canvas. The IDE (Integrated Development Environment) known as Amazon SageMaker Studio allows cooperation between the parties to help them combine their expertise. This way data analysis can contribute with their experimentation results, and domain knowledge and data scientists can build pipelines while streamlining the process.
Coding knowledge, which restricts analysts from having a more active role in the production of models remains a constraint no longer. Analysts can make use of the visual interface and make ML predictions without coding skills. Does this mean that only analysts can benefit from it?
No. Actually, engineers can successfully deploy and manage multiple models by using SageMaker MLOps, and data scientists can prepare and build models through the SageMaker Studio. How does this reflect in benefits for the business?
If ML is made accessible to more people who can provide solutions and help build efficient models, this shortens the time for each model to be built. The simplification of ML into point-and-click interfaces facilitates this immensely.
Oftentimes, the presence of a large amount of data stands in the way of efficient solutions. SageMaker allows the processing, access, and labeling of considerable amounts of unstructured data (video, photos, and audio) as well as structured or tabular data with ease.
An optimized infrastructure aids in reducing the time it takes to train your time in working with ML-related issues. Results have brought a 10X increase in team production thanks to the use of tools that cater to different team members based on their expertise and skillsets.
You can streamline and automate MLOps processes and practices across your organization to build, prepare, deploy and manage multiple models of large sizes, save manhours, and reduce time-to-market.
Business analysts and data scientists can reduce time-to-market by implementing AWS SageMaker into their practices. Integrating it with other tools, such as Snowflake, Amazon Redshift, or Amazon Simple Storage Service (Amazon S3), to allow analysts to work with data stored in these cloud-based systems as well as local datasets.
They can seek feedback from data scientists or decide to perform batch or single predictions to complete their models by themselves. This produces more solutions and gives data scientists time to work on complex problems that require their knowledge.
At Blue Orange Digital, we have extensive experience working with data for medium and large-scale organizations. As partners with Snowflake and certified AWS partners, we can efficiently help you solve your data problems. Schedule a free 30 minute discovery call with us to learn more.