We enhance your company's machine learning processes, boosting productivity and efficiency through the automation of ML workflows and the integration of AutoML solutions. Our proficiency in MLOps guarantees better strategy and development, consistent model training and deployment, scalable access to essential tools and resources, and seamless continuity across the entire production chain, ensuring streamlined machine learning operations.
We Take Pride in Our Numbers!
8+
Years of Experience
30+
Countries serviced
700+
Projects completed
50+
Developers
Digital Engine's MLOps Consulting Services:
ML Pipeline Development
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Our expertise lies in creating automated ML pipelines that efficiently process input data and code, facilitating the smooth training of machine learning models. We provide ML pipeline development services that guarantee precise data handling and high-quality model training.
Continuous Delivery for Machine Learning
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Our continuous integration/continuous deployment (CI/CD) service allows your data science team to rapidly experiment with new concepts and refine models, by automating the construction, testing, and deployment of pipeline components to the designated environment. By optimizing the development workflow of your machine learning pipeline, we assist in hastening the market launch and fostering business expansion.
Model Deployment and Implementation
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Our team is highly experienced in deploying machine learning models on cloud-native platforms optimized for ML tasks, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). We ensure these models are highly available, scalable, and reliable.
Model Monitoring
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Our observability tools, including distributed tracing, log analysis, and anomaly detection, are crafted to deliver instant insights into your AI systems' performance. This allows you to refine and enhance your models, improving their accuracy and efficiency.
Why Hire Digital Engine for MLOps Consulting?
Fastrack Your Workflow
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We enhance your infrastructure, workflows, and data preparation by implementing automation and optimization techniques. This ensures sustained productivity and efficiency across the machine learning lifecycle, helping your team remain effective.
Flexible MLOps Toolkit
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We harness a platform that harmonizes the strengths of both realms, blending the robustness and adaptability of open-source tools with the ease of use and dependability of commercial frameworks. Alongside our curated assortment of preferred notebooks and libraries, this platform offers a cohesive and unified user interface, ensuring a smooth and cohesive experience.
Efficient Collaboration
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By automating mundane tasks and streamlining experiment processes, we enable you to optimize your time effectively. Our meticulous data management system efficiently organizes and stores your datasets, while our advanced modeling techniques produce high-quality models to help you achieve your desired results.
End-to-end Development
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Our end-to-end MLOps service leverages state-of-the-art tools and technologies, such as sophisticated algorithms and automation features, to negate the necessity for extensive internal expertise.
Lower TCO for ML Projects
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We understand that flexibility is key to building successful machine learning solutions. That’s why we offer a vendor-agnostic approach that allows you to run your operations in the cloud, on-premises or in a hybrid environment without ever feeling locked in.
Security and Compliance
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Our robust encryption protocols ensure the security of your data, protecting it both during transmission and while at rest in the cloud. With our stringent security measures, you can trust that your data is well-protected at all times.
Digital Engine's MLOps Process
Aligning Machine Learning Objectives With Business Goals
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Understanding the organizational business goals and objectives thoroughly is crucial.
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Articulating a precise problem statement that can be addressed effectively through machine learning methods is essential.
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Identifying the diverse data sources available and determining the specific data required to train the machine learning model is a critical step.
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Developing a comprehensive plan encompassing the entire lifecycle of the machine learning model, including building, testing, deploying, and monitoring phases, is necessary for success.
Data Preparation and Management
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Creating a program tailored for offline extraction or batch fetching from the designated data source.
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Implementing an automated data validation process to guarantee data integrity and compliance with predefined standards.
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Employing an auto-distribution mechanism to partition the validated data into distinct training and validation datasets.
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Setting up a feature store as a centralized repository for managing and organizing pre-existing features efficiently.
Model Training
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Selecting a lineup of storage-agnostic version control systems tailored for machine learning workflows.
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Integrating the selected version control systems into the platform and configuring them to meet specific requirements.
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Ensuring that metadata generated from new training runs are seamlessly committed to the designated version control system.
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Establishing a metadata store to capture pertinent information for subsequent analysis and reference.
Model Evaluation​
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Setting up a framework for model monitoring and validation using the chosen toolkit.
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Enabling automated capture of essential performance data from every model run.
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Recording and storing all pertinent details to facilitate effortless reproducibility of results.
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Defining precise triggers for initiating pre-training in cases where the model underperforms.
Model Serving
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Identifying the optimal framework for packaging the model as an API service.
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Alternatively, configuring a container service for streamlined deployment.
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Setting up a production-ready repository dedicated to models.
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Developing a model registry to house all pertinent metadata linked to each model.
Model Monitoring
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Choosing the most appropriate agent for real-time model monitoring.
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Setting up the agent to capture anomalies, detect concept drift, and monitor model accuracy.
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Integrating supplementary measures to estimate model resource consumption.
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Establishing re-training triggers and configuring corresponding alerts.
AI Models We Have Expertise In
GPT-4
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A collection of OpenAI models that, because of their enhanced general knowledge and sophisticated reasoning abilities, are very accurate at solving complicated issues.
LLaMA​
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A basic large language model called LLaMA (Large Language Model Meta AI) can create text, hold conversations, summarise textual content, solve mathematical theorems, and predict protein structures.
PaLM 2​
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The most recent version of Google's comprehensive language model excels in carrying out complex reasoning tasks like code interpretation, mathematical solutions, classification, answering queries, and multilingual translation. This model, which outperforms earlier attempts at natural language production, demonstrates Google's dedication to ethical AI.
Claude​
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Anthropic's Claude is a large language model (LLM) that was developed as a virtual assistant and is compatible with business processes. Claude can be accessed in Anthropic's developer console via both an API and chat interface. It is capable of handling a wide variety of conversational and text-processing tasks.
DALL.E
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Based on language prompts, OpenAI's DALL·E produces realistic visuals and artwork. It can create photos with a given size, edit already-existing images, and create several versions of images that the user uploads.
Whisper​
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Whisper is an OpenAI model for general-purpose speech recognition that can also translate speech, identify languages, and recognize speech in many languages.
Stable Diffusion​
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In addition to producing detailed images in response to text cues, Stable Diffusion can be utilized for tasks such as text-guided image-to-image translations and inpainting.
phi-2​
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Phi-2 is a sophisticated 2.7 billion parameter Transformer that performs close to top-tier on important language understanding and reasoning benchmarks. It is enhanced with a variety of NLP data.
Google Gemini
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Gemini is a family of multimodal large language models developed by Google DeepMind, serving as the successor to LaMDA and PaLM 2. Comprising Gemini Ultra, Gemini Pro, and Gemini Nano, it was announced on December 6, 2023.
Vicuna​
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Vicuna is a chatbot designed for AI, NLP, and machine learning research. It is based on Llama 2 and was improved with user interactions via ShareGPT. Its target users are researchers and enthusiasts in these fields.
Mistral-7B-v0.1
Mistral-7B is a decoder-based LM that performs two tasks: it creates material and modifies it by categorizing user prompts and answers into groups that include illicit acts such as fraud, child abuse, and terrorism.
bloom-560m
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In addition to direct language exploration, the Bloom-560M model serves specific tasks including information extraction, question answering, and summary for public research in text generation and analysis.
Technology Stack We Use for MLOps Consulting
Model Training
Software
Library
Data Preprocessing
Data Storage
Version Control
Model Monitoring
Model Development
Data Visualization
CI / CD
Collaboration
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Some of Our AI Projects
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LLM-powered Application for Safer Machinery Troubleshooting
Clinical Decision Support System driven by GenAI
App for Compliance and Security Access Powered by LLM
Our Unique Engagement Ecosystem
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Discovery Consultation​
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Embark on a personalized journey with a discovery consultation tailored to your business needs. Our experts delve deep into understanding your goals, challenges, and aspirations, laying the foundation for a unique engagement strategy.
Dedicated Development Team
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At Digital Engine, our adept developers seamlessly integrate avant-garde cognitive technologies, sculpting a tapestry of high-quality services and bespoke solutions meticulously crafted for our esteemed clients' digital aspirations
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Custom Solution Crafting​
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Our team meticulously crafts bespoke solutions designed exclusively for your business landscape. Leveraging cutting-edge technologies and innovative approaches, we tailor our services to seamlessly align with your objectives and vision.
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Agile Developmental Framework​
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Embrace agility with our development framework, ensuring adaptability to evolving project dynamics. Witness a transparent, iterative process that prioritizes feedback, incorporates changes seamlessly, and delivers tangible results at every milestone.
Continuous Communication Channels
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Stay connected with our dedicated communication channels for regular updates and interactive feedback, fostering a collaborative partnership at every step.
Performance Optimization and Growth Strategies
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Beyond project completion, our commitment extends to performance optimization and growth strategies. Benefit from post-engagement support, data-driven insights, and ongoing enhancements to ensure your digital journey continues to evolve and thrive.
Let's Work Together
1. Contact Us
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Fill out the Contact Form and get in touch with us
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2. Get a Consultation
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Get on a call with Digital Engine's experts
3. Get aCost Estimate​
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For your project needs, we offer a tailored proposal with budget and timeline details.
4. Project Launch​
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After project approval, we assemble a cross-disciplinary team to initiate your project.
We Build for You. Whatever You Need!
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