ML System setup with complete CI/CD and Devops on Cloud Platforms, Hybrid Clouds and on-premises

ML (Machine Learning) is a critical component of many modern businesses, and having a reliable ML system setup is essential for success. With a complete Continuous Integration and Continuous Delivery (CI/CD) pipeline with well accepted Devops practices, your ML models can be developed and deployed quickly and efficiently with seamless collaboration between development and operations teams.

By setting up this system on cloud platforms, you can take advantage of the scalability and flexibility of cloud computing, while also minimizing hardware costs. Additionally, by utilizing a hybrid cloud setup, you can balance workload distribution between public and private clouds for optimal performance.

Furthermore, deploying your ML system on-premises provides the added benefit of increased security and control over your data while still leveraging the power of modern ML technologies.

DreamAI has a proven track record of architecting, designing, and implementing scalable AI/ML systems for multiple satisfied customers.

Migration to Cloud Platforms from on-premises AI/ML solutions

In recent years, cloud platforms have become increasingly popular for hosting artificial intelligence and machine learning solutions. There are numerous benefits to migrating from on-premises solutions to cloud platforms, including increased flexibility, scalability, and cost-effectiveness.

Whether your AI/ML solution is in the early stages of development or is already mature, Cloud platforms offer a range of tools and services that can make the migration process easier, such as pre-built virtual machines and containers that can be easily configured to run your solution.

One of the biggest advantages of cloud platforms is their scalability. With on-premises solutions, it can be challenging to scale up or down as needed. Cloud platforms, on the other hand, allow for easy scaling by providing access to additional computing resources on-demand. This means that you can quickly respond to changes in demand and avoid over-provisioning or under-provisioning your infrastructure.

Another benefit of cloud platforms is the cost-effectiveness. With on-premises solutions, you need to invest in expensive hardware and maintain it over time. In contrast, cloud platforms offer a pay-as-you-go model, where you only pay for the resources that you use. This can be a significant cost savings, especially for smaller businesses that cannot afford to invest heavily in hardware upfront.

Our team can work with you throughout this migration process including assessment, planning, defining processes and execution with minimal down time and ensuring smooth migration with deployment best practices.

Scalable, distributed Model Training and fine-tuning in Cloud

Model training and fine-tuning are critical aspects of developing high-performing artificial intelligence and machine learning models. However, these tasks can be computationally intensive and require significant computing resources. One way to address this challenge is by leveraging the scalability and power of cloud computing.

Cloud platforms offer a range of tools and services that can be used for scalable, distributed model training and fine-tuning. Most importantly, these platforms provide cost effective access to a range of specialized hardware, including GPUs and TPUs, that can significantly accelerate the training process.

Furthermore, these platforms allow you to quickly spin up and down computing resources as needed, making it easy to handle large volumes of data and complex machine learning models.

Another benefit of using Cloud platforms for ML model training is their distributed computing capabilities. With distributed computing, the workload is divided across multiple machines and GPUs, allowing for faster and more efficient processing. This means that you can train and fine-tune models in a fraction of the time it would take with a single machine.

At DreamAI, we train and fine tune models for images, text, speech and tabular data on an almost daily basis. We have well-tested and mature training code for a variety of model types that can run on multiple machines and GPUs utilizing popular open-source frameworks such as Pytorch, Pytorch-lightning, Tensorflow and Ray.

Distributed Hyper-parameter Optimization in the Cloud

Hyper-Parameter Optimization (HPO) is critical for improving the accuracy and performance of machine learning models. Cloud platforms enable distributed HPO to make this process much more efficient.

These platforms allow distribution of the workload across multiple machines, accelerating the tuning process and improving the accuracy of the models.

By leveraging the power and scalability of cloud computing, businesses can fine-tune their machine learning models faster and more efficiently, ultimately leading to better results and a competitive advantage in the market.

At DreamAI, HPO is a regular component of our ML model building process. We have well-tested software modules and libraries that can perform HPO on popular Cloud Platforms such as VertexAI, as well as on open-source platforms like Ray.io.

Scalable Model Deployment for Batch Predictions and Online Predictions

When it comes to deploying models for prediction, there are several aspects that are often overlooked in ML projects which may lead to significant problems when models are used in the real world. This may even lead to failure of the whole project.

It is critically important to design ML prediction systems to be scalable and elastic on-demand to save costs, and to be able to handle real-time (online) as well as batch prediction (offline) use cases.

Furthermore, a well designed deployment should also enable multi-model scenarios under the control of customized business logic during invocation of different models. In such cases, a complex workflow may need to be implemented, that requires prediction output from multiple models to achieve the final result and thus enable critical business decisions.

At DreamAI, we are fully aware of these challenges and use state-of-the-art tools and technologies in the Cloud to achieve the required level of reliability, scalability, fault tolerance and workflow execution.

We have a strong ML engineering team that has significant expertise in platforms and frameworks such as Kubernetes, Ray.io on Kubernetes, VertexAI (on GCP) and Apache Airflow.

We can work with you in different types of engagements, depending on the background and requirements of your team to design, develop and deploy AI/ML models that perform as desired in the real-world and deliver on the promise of AI/ML for your business.

Cost Optimization for Cloud based AI/ML solutions

Cost optimization and reduction are critical factors for businesses implementing artificial intelligence and machine learning models. Cloud platforms offer solutions for cost optimization and reduction, enabling businesses to minimize costs associated with training, serving, and scalable data processing.

Our team can work with you to analyze your current Cloud deployment, identify cost cutting opportunities, suggest and also help implement the required modifications in your deployment.

Cloud based Chat-Bots and IVR solutions for enterprises

Cloud-based chatbots and IVR (Interactive Voice Response) solutions are powerful tools for enterprises seeking to improve their customer service and engagement. Cloud platforms offer solutions for chatbots and IVR, enabling businesses to provide their customers with a streamlined and responsive experience.

By leveraging cloud-based chatbots and IVR solutions and integrating them with backend business services such as order management, sales and support applications, businesses can quickly and effectively respond to customer inquiries, provide personalized support, fulfill orders automatically, and thus improve customer satisfaction and loyalty. This capability also enables businesses to automate routine tasks and free up staff to focus on higher-value activities, ultimately improving operational efficiency and profitability.

At DreamAI, we have a specialized group of experts who can create automated IVRs and Chatbots according to your business needs using Cloud solutions such as Dailogflow on GCP and Amazon Lex and Amazon Connect on AWS.

We can build, test and deploy an IVR and/or chat-bot solution in your company’s Cloud set up (GCP or AWS) quickly, and integrate it with your back end business services for order fulfillment, human resource operations and several other applications using API interfaces provided by your business back end.