Overview
Machine learning (ML) has emerged as one of the most revolutionary technologies of our time that can solve previously unsolvable problems. Businesses that leverage ML effectively can accelerate innovation and gain a competitive advantage with its vast potential. From advanced customer data analytics to vulnerability and threat detection, ML has a range of use cases and benefits.
Amazon’s SageMaker is a managed service in the public cloud that offers the tools to build, train and deploy ML models for any use case. It offers a fully managed infrastructure, tools, and workflows. Where traditional ML development can be complex and iterative. Amazon SageMaker Studio simplifies this process by providing framework to prepare, build, train and deploy ML models and a broad range of capabilities specially created for machine learning.
These purpose-built capabilities allow you to build highly accurate machine learning models that improve continuously with data. Using SageMaker, businesses can kickstart their ML journey without the heavy lifting required to manage ML environments and infrastructure. Yet, deploying ML models can be quite challenging even for seasoned application developers.
At CloudJournee, our ML experts can help you in your machine learning journey by customizing and optimizing SageMaker for your unique business and use cases. Right from initial strategy to building ML models, integrating systems and processes, to implementing the right tools for your success, we help you build superior machine learning models.
Why Machine Learning?
The exponential growth of data science has led businesses to improve their processes by harnessing the power of technologies such as big data, machine learning and artificial intelligence. Machine learning empowers businesses to gain actionable insights from raw data. More specifically, ML algorithms can be used to learn from a data set and iteratively improve the learnings, understanding patterns, behaviors etc., with little or no training. This continuously learning capability of ML processes allows businesses to make sure they are always on top of the most crucial customer and business needs.
Leveraging Machine Learning with SageMaker
SageMaker is easy to deploy and set up for model training jobs but each business has unique data patterns and needs. At CloudJournee, our experts help you customize, integrate and optimize your ML models using SageMaker
Jumpstart ML deployment
We can help you rapidly prototype and create the proof of concept for ML solutions using Amazon SageMaker
Streamline the ML lifecycle
We help you automate and streamline MLOps across your organization so that you can build, train, deploy and manage models easily
Designing your strategy
Our ML experts will design an impact-driven ML strategy and build a model that achieves your key performance indices. We will then deploy it and maintain it in the production environment. Our expertise in AWS cloud allows us to integrate your ML model into an optimal infrastructure
Uncover Valuable Insights
Gain actionable insights from your data to take informed business decisions and innovate faster than your competitors
Our Approach
Gather and prepare data
Using SageMaker, we will help you gather and prepare your data to create sophisticated model features
Build ML models
Our SageMaker consultants can help your business teams to build machine learning models and create accurate predictions with ML service. Publish results, interpret models and share information with others
Train and tweak models
We can leverage SageMaker’s flexible and distributed training options to adjust to your specific workflows to train and tune models as per your needs. Use “Experiments” to track iterations and browse, search, review and compare your iterations to get the best results
Deploy
We can help you build automated workflows to manage your ML lifecycle end to end from data preparation to model training and deployment with SageMaker Pipelines. Launch your model with a few clicks into a secure and scalable environment
Why CloudJournee?
CloudJournee customizes ML architecture as per your use case
Amplify your efforts with our cloud experts to build custom ML solutions from the ground up
Migrate legacy ML software to SageMaker to get the most out of your ML investment
Streamlined MLOps workflows for a smoother and faster collaboration between teams leading to faster deployment