MLOps Consulting

MLOps Consulting Services

Machine Learning Models: Deployed Faster, Managed Better.

MLOps simplifies managing your machine learning models. Business success depends on how accurately you can find patterns, extract insights and continuously learn from data . Codewave’s MLOps Consulting speeds up deployment and simplifies model management for you.

Our consulting process starts with analyzing gaps and inefficiencies in ETL pipelines, model versioning, and orchestration. Then, we design a robust MLOps architecture aligned with your cloud, on-prem, or hybrid stack. Kubernetes handles container orchestration and horizontal scaling of models. TensorFlow Extended (TFX) streamlines data validation, transformation, and serving pipelines. Kubeflow optimizes hyperparameter tuning, pipeline orchestration, and seamless model deployment.

The results are clear– 40% faster deployment times and a 30% increase in model accuracy. Your models become more reliable, scalable, and cost-efficient—delivering real business value.

We make AI work for you:

90%+

Accuracy

99.95%

Availability/Uptime

2x

Faster model retraining cycles

60%

Lower Model deployment errors

Ready for smoother MLOps integration? Let’s connect and create something great!

Precision MLOps Services

We help you manage your machine learning models and scale up your ML efforts.

MLOps Readiness Assessment

Our readiness assessment helps you prepare your ML systems for smooth scaling. You get a complete review of deployment processes, version control, and data pipeline efficiency. Model Drift Detection identifies when models lose accuracy due to data changes. Pipeline Stress Testing helps us spot bottlenecks in data handling or deployment. We review retraining strategies to keep models up-to-date with fresh data.

For example, the assessment can uncover outdated steps if your models fail to adapt to changing data. This helps you prioritize fixes and improve results without delays.

End-to-End ML Pipeline Consulting

Efficient ML pipelines lead to faster model training, deployment, and accurate real-time predictions. Our consulting simplifies every step of your ML pipeline, from data preprocessing to deployment. DataRobot automates data preprocessing and feature engineering, and provides clean, standardized inputs for model training. Jenkins manages continuous integration (CI) and continuous delivery (CD), for automated updates without downtime.

For example, a retail company integrates automated preprocessing and CI/CD workflows to optimize stock predictions. This approach updates inventory insights instantly, reducing stockouts and improving customer satisfaction.

Scalable Model Deployment Consulting

Frequent model deployment issues– inconsistent retail environments or scaling challenges in fintech, we help solve them for every industry. We use Helm to manage Kubernetes applications and simplify deployments and scaling. Terraform automates infrastructure provisioning for consistent and reliable environments across all stages.

For example, a fintech company ensures smooth scaling during peak times by optimizing its deployment process while maintaining consistent infrastructure across environments to minimize delays and improve model performance.

CI/CD for ML Consulting

We help simplify your model updates and deployments with well-structured CI/CD pipelines. We automate tests whenever you commit code with GitLab CI, catching issues early and preventing costly production failures. CircleCI accelerates deployment cycles by automating builds and updates and ensures your models deploy consistently with minimal manual effort. This results in faster model iterations, fewer errors, and improved reliability in production environments.

For instance, if manual updates cause delays, automated pipelines solve the problem. This leads to faster deployments and fewer errors.

Model Monitoring and Performance Optimization

Monitor your model performance, accuracy, and drift. Seldon and New Relic let you have quick insights into your models performance. Seldon tracks model metrics, alerts you to drift, and ensures your models stay aligned with evolving data. New Relic monitors latency and throughput, instantly notifying you if accuracy drops or error spikes occur.

For example, if accuracy starts declining, our tools help pinpoint the root cause, allowing you to take immediate action to optimize and ensure consistent performance.

Data Engineering and Governance Advisory

Data-driven operations rely on seamless, real-time data flows and strong governance standards. We build efficient data pipelines that streamline data processing and ensure complete traceability. Apache Pulsar handles real-time data streams, providing low-latency, high-throughput capabilities essential for industries like finance and retail. Talend automates complex data integration, ensuring accurate data movement while fully complying with industry-specific governance frameworks, such as GDPR or HIPAA.

For example, a retail company optimizes its supply chain by controlled data flows. The company ensures smoother operations and quicker decision-making by managing real-time inventory data and maintaining compliance.

Cloud-Based MLOps Implementation Consulting

Cloud solutions drive scalable, efficient ML operations. Google AI Platform automates the end-to-end model lifecycle, improving operational efficiency from model training to deployment. AWS SageMaker supports seamless ML workflow management, enabling large-scale model deployment with robust integration into AWS infrastructure. These platforms enable dynamic scaling, reducing operational overhead and enhancing model performance through on-demand resource allocation.

For example, a healthcare provider improves its predictive analytics models to scale patient diagnosis tools across multiple regions. The cloud platform enables efficient resource allocation and real-time model updates while controlling infrastructure costs.

Security and Risk Mitigation for MLOps

Secure your ML models and workflows with controlled access, encrypted data, and monitored threats. Okta ensures secure authentication, limiting access to authorized personnel only. HashiCorp Vault encrypts both data in transit and at rest, preventing unauthorized data access. These solutions integrate into your ML pipeline to safeguard sensitive information and mitigate risks effectively, with ensured compliance and reduced potential breaches.

For example, a healthcare organization controls access to sensitive patient data by setting strict user permissions. This prevents unauthorized personnel from interacting with the models, ensuring data security while maintaining regulatory compliance.

MLOps Strategy and Transformation Advisory

We develop a robust, scalable MLOps strategy that aligns with business objectives for long-term growth. We use MLflow to manage the entire machine learning process from data collection to model monitoring and ensure smooth deployments. Flyte automates complex workflows like data processing, hyperparameter tuning, and deployment. This way it is easy to scale models and integrate them into your infrastructure.

For example, if your models face challenges scaling as your business grows, we ensure better integration and adaptability. This leads to smoother operations and more reliable model performance.

Want to optimize your MLOps? We’ve got you covered—let’s chat!

Making Generative AI Work for You

Codewave makes MLOps effortless. We focus on optimizing every stage—from development to deployment—ensuring your models perform error free.

Hyperparameter Tuning Automation

Automating hyperparameter tuning boosts your model's performance quickly and efficiently. Hyperopt identifies optimal hyperparameters like learning rates, batch sizes, and the number of hidden layers in neural networks. Optuna is used to conduct multiple trials simultaneously.

For example, in a healthcare application, the system adjusts hyperparameters to improve the precision of diagnostic models, reducing error rates and delivering faster results. This continuous optimization ensures the model adapts and performs at its peak.

Real-Time Data Ingestion

We keep your model updated through optimized processing of live data streams. Apache Pulsar collects real-time data from sources like IoT devices and web logs. Apache Storm processes this data instantly by filtering and aggregating for immediate model updates.

For example, a smart city application monitors traffic data in real-time. As vehicles pass through sensors, the model instantly updates traffic predictions, helping to optimize traffic flow and reduce congestion.

Edge AI Deployment

Processing data locally ensures faster responses and minimizes latency for time-critical tasks. We use Intel Movidius to handle tasks like object detection and face recognition at the edge. OpenVINO optimizes models for faster real-time performance, such as improving speed in autonomous vehicle navigation or real-time video analysis.

For example, a drone uses AI models to analyze its surroundings in real time. As it navigates, the model processes visual data locally, enabling quick decisions– avoiding obstacles and ensuring safe and efficient flight.

Dynamic Resource Allocation

Dynamic Resource Allocation ensures that your computing resources are used efficiently based on current demand. We use Docker Swarm to orchestrate container deployment and manage resources, while HashiCorp Consul automates scaling by coordinating service discovery across multiple clusters.

For example, an e-commerce platform adjusts resources automatically during flash sales. The system scales workloads as traffic spikes, ensuring fast response times and preventing downtime while maximizing performance.

Integrated A/B Testing for Models

Integrated A/B Testing for Models helps you test different model variations in real time. LaunchDarkly runs feature flag experiments to test different model variations in real time. ModelDB keeps track of model versions for smooth testing.

For example, an e-commerce platform runs A/B tests to compare two recommendation models. The best-performing model is chosen, improving customer engagement and accuracy.

Serverless ML Operations

Executing machine learning models seamlessly becomes easier with a serverless approach, as it eliminates the need for manual server management. We use Azure Functions to scale resources automatically. IBM Cloud Functions automate tasks like data preprocessing and model retraining.

For example, a retail company adjusts resources automatically during seasonal traffic surges. Repetitive tasks, like model retraining, are automated, ensuring optimal model performance without manual intervention.

Visualization Dashboards for Insights

Visualization Dashboards for Insights turn complex data into easy-to-understand visuals. Looker provides interactive reports that allow users to explore key metrics, while Qlik Sense organizes and analyzes the data, making it easier to identify trends and patterns.

For example, a marketing team tracks customer engagement metrics on a dashboard. They quickly spot trends and adjust strategies in real time, driving more effective campaigns.

Interested in transforming your machine learning processes? Let’s talk!

From Concept to Deployment – MLOps Implementation Made Easy.

We help you in model development to deployment— faster results, smooth operations, and reliable performance. You can focus on innovation while we manage the process end to end.

Defining Business Goals

We first identify what you want to achieve with machine learning— reduce churn, predict demand, or personalize user experiences. Based on your goals, we choose models like decision trees for classification and regression models for forecasting. We focus on solving the right problems - towards tangible business outcomes.

Data Preparation and Pipeline Setup

Data is collected from various sources and cleaned using Alteryx and Trifacta., We use tools like Airflow to automate workflows and Flink for real-time data processing and pipeline setup. This ensures the data is reliable and always ready for model training. We design the pipeline to scale smoothly as your data grows.

Model Development and Testing

Our team develops models tailored to your use case, using TensorFlow, PyTorch, and scikit-learn. We test and validate models using cross-validation techniques and custom evaluation metrics. This helps us find the model that performs best. Continuous A/B testing ensures models perform well in live environments.

Want to accelerate your MLOps? We’re just a message away—contact us now!

Our Go-To MLOps Technologies

Data Collection & Ingestion	Apache NiFi, Google Pub/Sub, AWS Kinesis, Kafka, Flink
Data Cleaning & Transformation	Databricks, Trifacta, Alteryx, Apache Spark, Pandas
Pipeline Orchestration	Apache Airflow, Prefect, Kubeflow Pipelines, Dagster, Luigi
Model Training & Experimentation	MLflow, TensorFlow, PyTorch, Jupyter Notebooks, Keras
Model Deployment	Docker, Kubernetes, Seldon Core, TensorFlow Serving, BentoML
Monitoring & Logging	Prometheus, Grafana, Evidently AI, ELK Stack, New Relic
Version Control & Registry	DVC, Git, MLflow Registry, Pachyderm, GitLab CI/CD
Infrastructure Management	Terraform, Ansible, AWS CloudFormation, Azure DevOps, Helm
Cloud Platforms	AWS SageMaker, Google AI Platform, Azure Machine Learning, IBM Watson
AutoML	H2O.ai, Auto-sklearn, Google AutoML, DataRobot, Amazon AutoGluon
Testing & Validation	Deepchecks, Great Expectations, Pytest, CheckList
Security & Compliance	HashiCorp Vault, Aqua Security, Clair, AWS IAM

Where We Make an Impact: Industries Served

Codewave has worked with over 15 industries, delivering real results. We’ve partnered with 400+ businesses around the world, from VC firms to startups, SMEs, and even governments. No matter the size, we’re here to help you solve business problems.

Healthcare	Deploy models for predictive diagnosis and patient monitoring. Set up real-time data pipelines for clinical data ingestion. Automate model retraining to improve accuracy over time.
Transportation	Build machine learning models for route optimization and fleet tracking. Use real-time data processing for dynamic traffic management. Continuously monitor model performance to handle changing traffic patterns.
Energy	Develop models for energy demand forecasting and renewable output prediction. Automate data collection from smart grids for real-time analysis. Implement scalable MLOps pipelines to handle growing datasets.
Retail	Create models for personalized product recommendations and dynamic pricing. Use automated pipelines for inventory forecasting and demand planning. Monitor and update models regularly to reflect changing consumer behavior.
Insurance	Deploy fraud detection and claims prediction models with automated monitoring. Set up CI/CD pipelines for quick updates to risk assessment models. Implement drift detection to maintain model reliability over time.
Agriculture	Build models for crop yield prediction and automated irrigation control. Automate real-time weather data ingestion for better decision-making. Use continuous monitoring to adapt models based on environmental changes.
Education	Develop models for personalized learning paths and engagement tracking. Automate data processing from student interactions for real-time insights. Use CI/CD frameworks for rapid feature updates in e-learning platforms.

Have a project in mind? We’re here to help. Contact us to get started!

What to expect

What to expect working with us.

Frequently asked questions

.01 /What is MLOps and why is it important?

MLOps (Machine Learning Operations) is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. It’s important because it bridges the gap between data science and IT operations, enabling faster deployment of ML models, improved collaboration, and more reliable AI systems.

.02 /How can MLOps benefit my business?

MLOps can benefit your business by accelerating the deployment of ML models, improving model performance and reliability, reducing operational costs, enabling seamless scaling of ML workflows, and enhancing collaboration between data science and IT teams. This leads to faster time-to-market for AI-powered solutions and improved ROI on ML initiatives.

.03 /What services does Codewave offer for MLOps?

Codewave offers end-to-end MLOps consulting services including assessment of current ML workflows, custom MLOps strategy development, implementation of automated ML pipelines, continuous integration/deployment setup, monitoring and observability solutions, and ongoing optimization and support.

.04 /How does Codewave's design thinking approach apply to MLOps?

Our design thinking approach ensures that we develop MLOps solutions tailored to your specific needs and challenges. We focus on understanding your unique workflow, identifying pain points, and co-creating solutions that align with your business goals and user needs, rather than applying a one-size-fits-all approach.

.05 /What technologies does Codewave use for MLOps implementations?

We leverage a combination of best-in-class open-source and commercial MLOps tools, including but not limited to Kubeflow, MLflow, Airflow, TensorFlow Extended (TFX), and cloud-native services from major providers. Our technology stack is flexible and we choose the most appropriate tools based on your existing infrastructure and specific requirements.

.06 /How long does it typically take to implement an MLOps solution?

The timeline for implementing an MLOps solution can vary depending on the complexity of your existing ML workflows and the scale of your operations. Typically, an initial assessment and strategy development can take 2-4 weeks, with implementation ranging from 1-3 months for basic setups to 6+ months for more complex, enterprise-wide solutions.

.07 /How does Codewave ensure the security and compliance of ML operations?

We prioritize security and compliance in all our MLOps implementations. This includes implementing robust data governance practices, ensuring model reproducibility and auditability, setting up secure CI/CD pipelines, and integrating with your existing security infrastructure. We also stay up-to-date with relevant regulations and industry standards to ensure compliance.

.08 /Can Codewave help with the cultural and organizational changes required for successful MLOps adoption?

Yes, our consulting services go beyond technical implementation. We work with your teams to foster a culture of collaboration between data scientists, ML engineers, and IT operations. This includes providing training, establishing best practices, and helping to define new roles and responsibilities to support a mature MLOps practice.

.09 /How do you measure the success of an MLOps implementation?

We measure the success of MLOps by tracking key metrics such as model performance, deployment speed, operational efficiency, and business outcomes. Success is also evaluated based on the stability of the production environment, the scalability of workflows, and the ability to continuously improve models.

.10 /Can MLOps integrate with my existing IT infrastructure?

Yes, MLOps can integrate with your current IT infrastructure. We assess your existing systems and ensure that our solutions fit seamlessly with your data storage, cloud platforms, and security measures. Our goal is to enhance your existing setup without major disruptions.

.11 /What kind of ongoing support does Codewave provide for MLOps?

We offer ongoing support to ensure the continuous success of your MLOps solution. This includes monitoring model performance, troubleshooting issues, optimizing workflows, and updating models as new data becomes available. Our team remains available for regular check-ins and maintenance as needed.

.12 /How do you handle model versioning and deployment in MLOps?

We manage model versioning using tools like MLflow or DVC, ensuring that each model version is tracked and reproducible. For deployment, we set up CI/CD pipelines to automate the process, ensuring smooth updates and rollbacks while minimizing downtime.