Automate your Model Training with advanced monitoring and reporting

What is
Machine Learning as a Service.

Machine Learning as a Service (MLaaS) is not a new concept, but it has recently gained popularity due to its usefulness and power for data scientists, machine learning engineers, data engineers, and other machine learning experts.

Machine learning as a service refers to a collection of cloud-based platforms that use machine learning techniques to create solutions that can assist ML teams with:

Our services include predictive analysis for several use cases, data pre-processing, model training and tweaking, and model deployment through run orchestration.
It uses the power of cloud computing to provide machine learning services on the go.

Why Rococo Consultant For MLaaS

What to expect from MLaaS platform

What to expect from MLaaS platform

Data Management: As more businesses migrate their data from on-premise storage to cloud storage solutions, the requirement to properly manage this data grows. Because MLaaS platforms are essentially cloud providers, they offer cloud storage as well as means to correctly manage data for machine learning experiments and data pipelining, making it easier for data engineers to access and handle that data.

Access to ML Tools: Businesses can use predictive analytics and data visualization tools provided by MLaaS providers. They also provide APIs for sentiment analysis, face recognition, creditworthiness evaluations, corporate intelligence, healthcare, and more.

MLaaS vendors encapsulate the actual computations of these procedures, so data scientists don’t have to worry about them. Some MLaaS providers even feature a drag and drop interface for machine learning experimentation and model development (with some constraints, of course).

Rococo Consultant Dedicated MLaaS Team

Benefits of MLaaS

Reduced Operational Risk
Reduced Operational Risk

Eliminate bottlenecks in your training by tracking hardware use during your experiments.

Cost-effective
Cost-effective

Save resources by ending training early when models begin to diverge.

Highly Productive
Highly Productive

Make training more responsive by adjusting hyperparameters or training techniques on the fly.

Supported Features

Maximize your model training performance

01
Compare experiments
With all of your experiment details in one place, you can see which training tactics work best and why. Iterate through alternative hypotheses with more certainty and in less time.
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02
Monitor model training
Stop waiting hours for training to conclude just to discover that your model diverged quickly—and you could have halted it sooner. Save time and resources by receiving quick feedback on your trials.
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03
Reproduce experiments
Stop wasting time searching for models you taught two months ago. Keep all of the metadata you need to duplicate your best outcomes in one location.
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04
Version models
Easily track the full model lifecycle. From the first dataset to the final model version, deploy it to production.
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Let's Train Your Model

Lets Create Something
Awesome Together !

 
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