Consulting & Development
MLOps
Build & Run Models with Ease
Machine Learning Operations (MLOps) monitor and deploy models while automating predictions and the detection of errors in a precise, efficient manner. We help you acquire center of excellence for machine learning operations and production AI.
Why
Implement MLOps?
How we empower data scientists and engineers to acquire data and AI excellence?
MLOps, Machine learning operations, a close associate of DevOps, is meant to operationalize machine learning activities and deliver resiliency, automation, and unification across your organizations and routine processes.
Model Management
The purpose of model management and processes is to offer convenience in handling your AI inventory and exercise control over the full lifecycle of ML models.You can catalog your own models, track how, when, and where the files were updated, offer lineage through training materials and parameters, and induce responsibility in terms of a reliable, trusted artifact store.
Experiment Tracking
Experiment tracking saves all the experiment related information and is a valuable tool for data scientists to become more productive in their work. Through experiment tracking you can provide multiple jobs at the same time, enhance the research output, make comparisons between defend’ activity runs, track back to the previous experiments, and understand how data science professionals represent pipelines.
Scalable Pipelines
Scaling your training pipelines and data acquisition activities which complements your business needs. Through pipelines users can train large models, leverage pipelines that save costs and are easily scalable to meet needs, turn model training more repetitive and strong, leverage modern computing tools and high memory instances like GPUs, and run your pipelines in the cloud, on-premise, or both.
Model Deployment
Model deployment can be a complicated prospect but the process can be simplified by automatically deploying models in production, even with the least human input; transform models into containers for optimum scalability and ownership record; serve files through different protocols such as GRPC and REST; and scale up deployments to cater demand, or scale down to limit expenses.
Monitoring & Alerts
You can work on concept and data drift, use data health checks, deployment monitoring, user satisfaction and more to incorporate analytics into your AI system, execute continuous learning to retain your models through pipelines, deploy appropriate models into production, enable notifications before the occurrence of any unfortunate events like broken pipelines, endpoints, and data quality checks.
Embedded Governance
You can establish a framework that will help you govern the status of your AI projects across the entire company. Through customizable governance policies, users can have complete authority over who would access and review, approval workflows, access the history of model updates and prediction activity for regulatory compliance. You’ll always be aware what files have been created and how they are used and updated.
Your Journey for MLOps 101
Our machine learning operations services help companies acquire the technological arsenal to counter AI-based issues and manage their ML lifecycle through automation and scalability.
Before Hiring MLOps Experts
Every machine learning model and its deployment is a struggle due to models crashing frequently. Infrastructure setup is expensive, tedious and version control is non-existent, and it is difficult to scale AI tendencies.
After Hiring MLOps Experts
Our technical personnel and strategic resources make sure you’re your entire cloud and on-premise infrastructure is completely setup and ready to use. We automate deployment pipelines and rectify the data for a new training round.
Why
Choose MLOps Experts?
Short Time to Productivity
Your team can instantly start working and become more productive with infrastructure configured, data cleansed, pipelines automated, and workflows enabled.
Repetitive Experimentations
Securing all your data and employing strict encryption protocols to protect all your business information in, out, and on the cloud.
Continuous Delivery for Machine Learning
Using the development pipeline to automate the process for acquiring ML tools from version control into production adding all the approvals, testing, stages, and deployment to various environments.
Flexible MLOps Toolkit
Connecting top open source tools with commercial frameworks, notebooks, and variety of libraries within a platform.
Operational Automation
Helping restrict the toil involved with active ML models into production, precisely deciding on what you automate and how you plan to do it.
Efficient Collaboration
Gathering your team and allowing you to review shared models, data sets, and results when routine tasks need to be automated and experiments run at regular intervals.
Are
Your Models Accurate Today?
Machine learning models can quickly turn from assets into liabilities for the business. When your training data and infrastructure isn’t prepared for certain situations, the models are prone to make unauthentic, inaccurate predictions that will risk your users’ trust and make your business vulnerable.
Most machine learning deployment activities are complex and done manually which hinder IT teams, data scientists, and businesses in conducting rapid detection exercises and model performance repairs.
To acquire powerful scalability tendencies at the level of modern AI adoption, your business needs a better approach and workaround to deployment and manage all your production models across the organization.
How
MLOps Incorporates Value Throughout Machine Learning Model Lifecycle?
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Data Preparation & Management
Performing manual data preparation can consume up to 80% of your time. While MLOps can build an automated data preparation and management timeline and make your team competent enough to handle data better.
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Model Training
Model training can become a real nuisance when a user is unable to suitably attribute and handle the generated artifacts and code branches. And MLOps can automate version control and metadata management.
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Model Evaluation
Manual model testing is an unskilled job which often misses some significant performance metrics, particularly when testing is done across various data segments. MLOps automate model evaluation and re-training.
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Model Serving
Less than 10% machine learning models fail to pass through successful deployment mostly due to the incompetency of research team failing to pass the model to production. MLOps set up a model as a service cloud deployment.
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Model Monitoring
Keeping close checks on your model performance is important, if not done there’s a high chance you’re going to miss a huge concept drift coming your way. MLOps automate model monitoring and enable auto-triggers for retraining.
Upcoming
Webinars & Events
In-person Event
Modern Data Analytics with Data Lakehouse
Subramanian Iyer – Manager, Partner Solution Architects at Databricks
July 07th, 2022 03:30 PM CST
Workshop
Data Science with Databricks
Hassan Sherwani – Head of Data Analytics & Data Science
Aug 11th, 2022 11:00 AM CST
Workshop
Data Engineering with Databricks
Mustafa Ali – Data Engineer GCP Practice
at Royal Cyber
Aug 25th, 2022 11:00 AM CST
Resources
Managed IT services customized for your industry
We can understand and support all areas of your IT systems from back-end infrastructure to front-end personal productivity. Our goal is create innovative IT solutions that enable your business to be more agile and competitive.
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