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Business Efficiency with AI

Our MLOps Consulting Services

  • MLPipelineDevelopment
    ML Pipeline Development
    • Specialization in Automated ML Pipelines
    • Seamlessly Process Input Data and Code
    • High-Quality Model Training
    • Data Accuracy Assurance
  • ModelDeploymentandImplementation
    Model Deployment and Implementation
    • Extensive Experience in Model Deployment
    • Cloud-Native Infrastructure (AWS, Azure, GCP)
    • High Availability, Scalability, and Reliability
    • Optimized for ML Workloads
  • ContinuousDeliveryforMachineLearning
    Continuous Delivery for Machine Learning
    • CI/CD Service for Rapid Testing and Iteration
    • Automated Pipeline Component Management
    • Accelerated Time-to-Market
    • Facilitating Business Growth
  • ModelMonitoring
    Model Monitoring
    • Real-Time Performance Insights
    • Observability Solutions (Distributed Tracing, Log Analysis)
    • Anomaly Detection for AI Systems
    • Precision Enhancement and Optimization

Our MLOps Process

MLOps-Process
businessgoals

Aligning Machine Learning Objectives with Business Goals

  • Understanding the organization’s business aims and objectives.
  • Formulating the machine learning problem statement that aligns with these objectives.
  • Identifying the necessary data sources and data required for model development.
  • Crafting a comprehensive plan encompassing model construction, testing, deployment, and ongoing monitoring.
data_preparation_manegment

Data Preparation and Management

  • Designing a program for offline data extraction or batch retrieval from the targeted data source.
  • Implementing an automated data validation process to ensure data quality and adherence to predefined standards.
  • Leveraging an automatic data distribution mechanism to partition validated data into separate training and validation datasets.
  • Establishing a feature repository as a centralized storage solution for organizing pre-existing features.
model_training

Model Training

  • Selecting storage-agnostic version control systems tailored for machine learning workflows.
  • Integrating and configuring the chosen version control systems within the platform.
  • Ensuring that metadata generated during new training runs is automatically committed to the appropriate version control system.
  • Creating a metadata store to capture relevant information for subsequent analysis.
model_evalution

Model Evaluation

  • Developing a framework for model monitoring and validation, utilizing the selected toolkit.
  • Enabling automated capture of critical performance data from each model run.
  • Recording and storing all pertinent details to facilitate the reproducibility of results.
  • Defining specific triggers for initiating pre-training when the model’s performance falls below expectations.
model_serving

Model Serving

  • Determining the most suitable framework for encapsulating the model as an API service.
  • Alternatively, configuring and selecting a container service for model deployment.
  • Establishing a production-ready repository for housing models.
  • Creating a model registry to store comprehensive metadata associated with each model.
model_monitoring

Model Monitoring

  • Carefully selecting the best-suited agent for real-time model monitoring.
  • Configuring the agent to capture anomalies, detect concept drift, and monitor model accuracy.
  • Incorporating additional measures for estimating model resource consumption.
  • Defining re-training triggers and configuring alerts accordingly.

Our Technology Stack

AI Development Services

python

Python

dot-net-core

.NET Core

java

Java

AI Development Tools

anaconda

Jupyter / Anaconda

colab

Colab

kaggle

Kaggle

Cloud Computing Platforms

aws

AWS

azure

Azure

google_cloud_platform

Google Cloud

DevOps

synk

Synk

jfrog

JFrog

jenkins

Jenkins

Frameworks / Libraries

tensorflow-1

Tensor Flow

pytorch-1

PyTorch

keras-2

Keras

Data Storage & Visualization

bigquery

Big Query

power-bi

Power BI

tableau-icon

Tableau

Our Engagement Models

  • Dedicated AI Development Team
    Dedicated AI Development Team

    Our proficient AI and blockchain developers are fully immersed in leveraging cognitive technologies to provide exceptional services and solutions to our clients.

  • Extended Team Enrichment
    Extended Team Enrichment

    Our extended team model is thoughtfully designed to support clients in expanding their teams with the necessary expertise for AI-driven projects.

  • Project-focused Strategy
    Project-focused Strategy

    Embracing our project-based approach, our skilled software development specialists collaborate directly with clients and the triumphant realization of AI-infused projects

Get Started Today

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Contact Us

Complete our secure contact form, Book a calendar slot and set up a Meeting with our experts.

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Get a Consultation

Engage in a call with our team to evaluate the feasibility of your project idea. We’ll discuss the potential, challenges, andopportunities.

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Receive Cost Estimates

Based on your project requirements, we provide a detailed project proposal, including budget and timeline estimates.

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Project Kickoff

Upon agreement, we assemble a cross-disciplinary team to initiate your project. Our experts collaborate to launch your project successfully.

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    Frequently Asked Questions

    What is MLOps?

    MLOps stands for Machine Learning Operations, which encompasses a set of practices and tools aimed at streamlining the development, deployment, and monitoring of machine learning models.

    Why should I opt for MLOps consulting?

    1. Faster Time-to-market for New Models: MLOps provides a framework that helps organizations streamline the development process for ML models, reducing development time and costs while improving the quality and reliability of the models. With most of the pre-development out of the way and effectively automated, development teams can fully focus on building viable ML models that provide value to the business.

    2. Full Visibility and Reproducibility: MLOps provides full visibility and reproducibility throughout the development lifecycle, making it easier for teams to manage their machine learning models. With a version environment and tools for building, evaluating, and comparing models’ performance, teams can quickly identify what’s working and what’s not, enabling them to optimize their models and ensure that they provide maximum value to the business.

    3. Lower Risk of Production Failure: MLOps provides a framework that enables development teams to lower the risk of production failure by bridging the communication gap between the research and production environments. With a model registry detailing all the model metadata, teams can ensure that models are thoroughly tested and validated before they are deployed to the production environment, minimizing the risk of issues and maximizing the value of the models for the business.

    4. Accelerate Experimentation Rate: MLOps accelerates the experimentation rate in machine learning development by streamlining the deployment process for viable models and allowing development teams to replicate models quickly. This increased experimentation rate leads to more innovative solutions, enabling development teams to focus on new projects and improve the accuracy and value of their machine-learning models.

    5. Reducing Time on Data Collection and Preparation: MLOps reduces the time spent on data collection and preparation by creating machine learning pipelines that design and manage reproducible model workflows. MLOps enables development teams to focus on developing more accurate and valuable machine learning models by automating many of the data collection and preparation tasks and delivering consistent model performance.

    6. Scalability of ML Models: MLOps helps with the scalability of ML models by increasing the acceleration, automation, and quality of the ML development process. By automating many of the tasks involved in developing and deploying models, monitoring and managing models at scale, and improving the quality of ML models, MLOps makes it easier to scale the development and deployment of ML models across multiple environments and use cases.

    What specific MLOps services do you offer?

    Our MLOps consulting services cover a wide range of areas, including data pipeline design and implementation, model training and deployment, performance monitoring and optimization, and team training and development.

    How can your team help implement MLOps in my business?

    We start by assessing your current ML infrastructure and identifying areas for improvement. Based on our assessment, we assist in designing and implementing data pipelines, deploying ML models, setting up monitoring and alerting systems, and developing MLOps best practices tailored to your organization.

    Do you offer customized MLOps solutions or pre-packaged packages?

    We provide both customized solutions and pre-packaged MLOps packages to suit your specific needs and requirements. Our team collaborates with you to ensure our services align with your unique goals and maximize their value.

    How can I get started with your MLOps consulting services?

    To begin, you can fill out our contact form or reach out to our team directly. We will then schedule a consultation to discuss your needs and create a personalized plan to help you effectively achieve your MLOps objectives.

    What role does automation play in MLOps?

    Automation is central to MLOps, as it streamlines repetitive tasks such as data preprocessing, model training, and deployment, enabling faster and more reliable ML pipelines.

    Can MLOps be implemented in both cloud and on-premises environments?

    Yes, MLOps can be implemented in various environments, including cloud, on-premises, and hybrid setups, providing flexibility to organizations.

    How does MLOps ensure model governance and compliance?

    MLOps facilitates model governance by tracking model versions, ensuring proper documentation, and enabling auditing and compliance checks.

    What is the typical ROI (Return on Investment) for organizations adopting MLOps practices?

    ROI can vary depending on the organization, but MLOps often leads to reduced development time, improved model accuracy, and cost savings, contributing positively to ROI.

    Is MLOps suitable for small businesses and startups, or is it primarily for large enterprises?

    MLOps principles can benefit organizations of all sizes, as they promote efficiency, reliability, and scalability in ML projects.

    Impressions

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    May 14, 2024

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    Pose Estimation: Transform Your Fitness Routine With AI

    May 13, 2024

    Pose Estimation: Transform Your Fitness Routine With AI

    In recent years, the fitness world has transformed significantly due to technological advancements. One notable innovation is the integration of pose estimation in workout apps powered by artificial intelligence (AI). Pose estimation, a technology that analyzes and tracks human body movements, combined with AI algorithms, is redefining our approach to fitness and workout routines. This […]

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    How Software Innovations Help Patients Join Clinical Trials

    May 8, 2024

    How Software Innovations Help Patients Join Clinical Trials

    In the ever-evolving healthcare landscape, embracing technology in clinical trials has become crucial for optimizing patient engagement and therapeutic outcomes. Traditional methods are giving way to digital innovations, revolutionizing how we approach clinical trials, and making them more patient-centric and data-driven. Introduction In the rapidly evolving healthcare sector, the integration of technology into clinical trials […]

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