Elevate Your Business with Machine Learning
When executed well, artificial intelligence (AI) and machine learning (ML) enable businesses to transform products, services, and processes by automating much of what engineering teams do manually. ClearScale can help you realize the full potential of ML across your business using powerful, purpose-built solutions from Amazon Web Services (AWS).
Achieve More With ClearScale and AWS
Identify and eliminate redundant processes by delegating manual tasks to intelligent ML programs.
Solve Complex Challenges
Forecast trends, optimize pricing, and proactively mitigate risk by relying on ML algorithms designed to accelerate innovation.
Deliver Better Services
Process massive volumes of customer data to determine how you should invest in product development and improve the customer experience.
Why Choose ClearScale as Your Machine Learning Consulting Partner?
Proven AI/ML Success
ClearScale earned the AWS Machine Learning Competency, validating our team’s ability to bring AI/ML applications to life for our clients. We have deep experience working with customers in diverse industries to build intelligent, data-driven ML solutions that improve over time and improve bottom-line profitability.
Mastery Over AWS AI/ML Solutions
Our ML experts are well-versed in AWS’s most powerful AI/ML services, including Amazon SageMaker, Amazon Forecast, Amazon Translate, Amazon Comprehend, Amazon Personalize, and more. By implementing these services, we enable companies to transform their businesses for the digital age in the most innovative manner possible.
Common Machine Learning Use Cases
Study end-user behaviors en masse and train up recommendation engines that can deliver tailored results to both new and returning customers.Read more
Automate the lifecycle of ML algorithms in production — from initial model training to retraining against new data. Identify duplicate data to ensure only one copy is stored while the rest is marked as duplicate for further analysis.Read more
Create robust forecasting models based on past data that enable you to plan more effectively for future demand.Read more
“We worked with ClearScale to help set up and configure our initial solutions and data pipelines. We needed to leverage insights faster and launch something in months rather than years. Their experts set up an AWS cloud configuration and related services for using Amazon Personalize to help save us a tremendous amount of effort and thousands of engineering hours.”
- Mikey Centrella, Director of Product ManagementRead Case Study
“ClearScale demonstrated both its technical cloud expertise and creativity through our recent project. It was clear from the get-go that ClearScale had done this type of data management revamp time and time again. Their expertise in machine learning was a valuable complement to our own application experts. We're now well-positioned to create even greater value for everyone in the SRM Ecosystem from manufacturers, to our dealers, fleets and partners.”
- Satish Joshi, Chief Technology Officer, DecisivRead Case Study
“ClearScale recognized the importance of AWS’ Machine Learning paradigms to SeligoAI’s value proposition and their ability to generate probability-based intelligence. Complex AI algorithms also continuously modify the Working Dataset and continually refine the predictive model which refreshes the probability analyses. Essential to this set of core calculations is AWS’ ML platform.”
- Gregory Jordan, SeligoAIRead Case Study
Frequently Asked Questions
What is machine learning?
Machine learning (ML) refers to the process of developing computer programs that are capable of learning and improving towards a specific goal without explicit input from humans. Machine learning has grown increasingly popular in recent years due to advances in cloud computing technologies that make it easier to develop, train, and maintain ML algorithms.
Why do many machine learning programs come up short today?
Despite machine learning’s popularity, the technology is still giving many organizations trouble. A big reason is machine learning algorithms are subject to different types of risk: developer biases, data biases, data drift, and others. When data teams and developers don’t have the insight or expertise needed to keep ML programs on track, the technology’s value to the enterprise diminishes. Plus, optimizing ML programs takes significant time and effort.
What are the benefits of implementing machine learning on the cloud?
Without cloud-based tools and services, it’s hard to maximize ML ROI. That’s why data teams often launch machine learning applications on the cloud where they can leverage managed services and increase the chances of generating positive results over the long term. Cloud providers like AWS anticipate the challenges ML teams face and offer solutions that both automate and simplify ML algorithm management.
What use cases are possible with machine learning?
Machine learning makes many challenging computational problems easier to solve. Some of the more popular use cases for machine learning today include building recommendation engines, chatbots, and forecasting models. Machine learning is also useful for identifying security threats, discovering unmet customer needs, and identifying patterns in large datasets that would otherwise go unnoticed.
What machine learning services does AWS offer?
AWS offers a robust suite of machine learning services. Some of the most popular include Amazon SageMaker, Amazon Forecast, Amazon Translate, Amazon Comprehend, and Amazon Personalize. These services and more solve specific problems that many organizations face, like how to make better predictions and how to turn raw physical data (e.g., paper notes, voice transcriptions, etc.) into digital data for analytical purposes.
What is MLOps?
MLOps refers to the set of technologies and activities that aim to produce machine learning success. In practice, MLOps brings together data engineering, machine learning, and DevOps resources to optimize data management and application development. More and more organizations are creating dedicated MLOps teams, knowing how crucial they are to building effective enterprise ML capabilities.
How do organizations succeed with MLOps?
Succeeding with machine learning and MLOps can be difficult for organizations with limited experience. That’s why it often makes sense to work with a third-party cloud machine learning expert, like ClearScale. ClearScale is an AWS Premier Tier Services partner with the Machine Learning competency. Our AWS machine learning consulting services provide sophisticated machine learning ecosystems and processes that set organizations up for long-term ML success.