MLOps Consulting Services
Our expertise is in revamping machine learning procedures for increased efficiency, which we do by automating ML pipelines and integrating AutoML platforms. Better planning and development are ensured by the MLOps expertise, which also enables repeatability in model training and deployment. Scaling is made simple with rapid access to essential resources. Our MLOps consulting guarantees a smooth production flow, which results in optimized machine-learning procedures.
Empowering Enterprises with MLOps Consulting Services
Empowering enterprises with streamlined machine learning, leveraging our deep-rooted expertise in model development, deployment, and monitoring.
Our forte is crafting automated ML pipelines engineered to handle data inputs and code, facilitating a smooth training of machine learning models. With our ML pipeline expertise, the precision of data processing is guaranteed, as is the top-notch training of your models.
Armed with a wealth of experience in propelling machine learning models onto cloud-native platforms like Amazon Web Services (AWS), Microsoft Azure, & Google Cloud Platform (GCP), we assure peak availability, scalability, and dependability.
Our CI/CD services allow a data science team to rapidly evaluate new concepts and refine models. This is made possible by automating pipeline components’ creation, examination, and deployment to the intended setting. We thereby speed up the development process of your machine learning pipeline, paving the way for faster market reach and business expansion.
We offer cutting-edge observability solutions, including distributed tracing, log analysis, and anomaly detection, which yield immediate insights into AI system performance. Such insights empower the tuning and optimization of models for superior precision and productivity.
Mastering Machine Learning Pipelines
Unleashing Model Deployment and Rollout
Streamlined Delivery for Machine Learning
Vigilant Model Monitoring
Our ML-Powered Projects
MLOps Process We follow
From a comprehensive review of your existing framework to deploying tailored MLOps practices, our process ensures seamless integration and execution at every step.
Synchronizing Machine Learning with Business Objectives
a. Getting a grip on the business targets and the larger dreams of the firm.
b. Sketching out the problem that machine learning can unravel.
c. Hunting down the right data sources and the data required to shape the machine learning model.
d. Formulating a blueprint for the lifecycle of the machine learning model – from birth to testing, deployment, and babysitting.
Nurturing Data and Governing its Growth
a. Designing a tool for unseen extraction or batch fetching from the cherry-picked data source.
b. Baking in an automatic data validation process to ensure the data remains pristine and sticks to the decided schema.
c. Applying a clever split method to remove separate training and validation data chunks from the validated data pool.
d. Laying the groundwork for a feature store that neatly houses and organizes pre-existing features.
Cultivating the Model
a. Handpicking a diverse range of storage-neutral version control systems that play well with machine learning workflows.
b. Harmoniously blending these version control systems into the platform and getting the settings right.
c. Ensuring that fresh metadata springing from new training runs are auto-saved into the correct version control system.
d. Establishing a metadata library to hoard relevant information for future deep dives.
Appraising the Model
a. Framing up a mechanism for model scrutiny and validation using the toolkit of choice.
b. Switching on the auto-capture for all critical performance data each time the model struts its stuff.
c. Safeguarding all essential details in a manner that facilitates effortless result reproduction.
d. Marking out specific triggers that kick-start pre-training when the model’s performance isn’t up to scratch.
Deploying the Model
a. Settling on the perfect framework to gift-wrap the model as an API service.
b. Or taking a different route – choosing and finetuning a container service for deployment.
c. Crafting a safe, production-ready home for the models.
d. Building a model registry – a logbook to store all metadata that matters for each model.
Keeping an Eye on the Model
a. Picking the best agent to constantly watch over the model in real-time.
b. Tweaking the agent to pick up anomalies, sense shifts in concept, and keep a close watch on model accuracy.
c. Adding extra measures to keep tabs on how much resources the model is munching on.
d. Drawing up the rules for re-training and setting up alerts to match.
Our Proud Clients
Why Partner with Markowate for MLOps Consulting?
Unlock the potential of MLOps with us, as we deliver bespoke solutions designed to optimize your business processes and maximize value.
Accelerating Work Processes
Through streamlining infrastructure, perfecting workflows, and enhancing data preparation via automation and optimization, Markowate ensures a smooth and efficient machine-learning journey. This allows teams to maintain peak productivity levels.
Comprehensive Development Solution
Markowate employs innovative tools and technology, including avant-garde algorithms and advanced automation, for a complete MLOps service. This negates the necessity for expansive in-house expertise.
Adaptable MLOps Toolkit
With a unique blend of powerful open-source tools and reliable commercial frameworks complemented by a curated selection of favorite notebooks and libraries, Markowate offers a unified and seamless user experience.
Reducing TCO in ML Endeavors
Recognizing the importance of flexibility for triumphant machine learning solutions, Markowate’s vendor-agnostic approach facilitates operations in the cloud, on-premise, or hybrid environments without feeling tied down.
Facilitating Effective Collaboration
By automating standard tasks and fostering efficient experimentation workflows, Markowate enables optimal time utilization. All data sets are neatly organized, and high-performing models are constructed to achieve targeted outcomes.
Ensuring Security and Compliance
Adopting robust encryption protocols, Markowate ensures data is protected during usage, transit, and storage in the cloud. With stringent security precautions, peace of mind regarding data safety is a given.
Our Machine Learning Operations Tech Stack
Our ML developers recommend the best technology stack to develop perfect ML solutions for business.
Director of Technology, Trapeze
Sr. VP, Hawaii Revealed
CFO, 3M Corporation
VP Strategy and Operations, Kira Talent
President & CEO, Interactive Ontario
We serve you with
Can you explain MLOps?
MLOps is a combination of practices and tools designed to simplify machine learning model development, deployment, and monitoring. The significance of MLOps lies in its ability to diminish time and expenditure tied to the creation and deployment of ML models, enhance model efficiency, and boost the dependability and scalability of ML systems.
How does your team facilitate the application of MLOps in my enterprise?
Our team thoroughly reviews the existing ML framework in your organization, pinpointing potential areas of enhancement. From there, we assist in designing and executing data pipelines, constructing and deploying ML models, setting up monitoring and alert systems, and formulating ideal MLOps practices within the firm.
Do you provide tailor-made solutions or fixed MLOps packages?
Our service range includes bespoke solutions and standardized MLOps packages to match your business’s specific necessities and stipulations. Our adept team collaborates with you, customizing our services to suit your needs and maximizing value.
What's the process to engage with your MLOps consulting services?
Initiating our MLOps consulting services is straightforward. Complete the contact form or connect directly with our team. We’ll then arrange a consultation to understand your requirements and devise a plan to meet your MLOps objectives.