Industrialize and accelerate your MLOps with ML Works platform
Challenges with Machine Learning Operations (MLOps)
Industry-wide, companies are struggling with AI projects that fail to make it into production due to bias in data, algorithms, or the teams managing them. According to Gartner, by the end of 2022, 85 percent of AI and ML initiatives will deliver erroneous results.
Enterprises worldwide are facing serious MLOps challenges due to the lack of optimal operationalized models. It’s difficult to have a reproducible and deterministically ‘correct’ result when there’s a mismatch between inference and training data. Other issues include black box input, multiple incoherent ML pipelines, manual deployment, lack of audit trails, among others
Cost overruns act as
a huge hindrance
ML pipeline extracts patterns from training data to create model artifacts, however, most modeling machinery fails to see the light because of unscalable production environment. The industry still lacks guidelines on what the best ML infrastructure should look like.
O’Reilly’s ML adoption report says < 10% of surveyed companies are using automated tools for monitoring models in production.
Return on investment
Analysis of data and model drift with automated alerts, enables continuous monitoring of production models. However, it requires a clear understanding of the data biases. The absence of processes/environments/resources to test independent data, bring in challenger models, and (re) caliber the metrics after iteration is still a challenge across the industry.
How We Help in Industrializing Machine Learning Operations?
Based on extensive experience in managing several AI customer engagements, Tredence developed ML Works to scale thousands of machine learning models, reduce outages and simplify model monitoring.
Extensive MLOps Capabilities to take on Enterprise ML Adoption Challenges
Our key offerings for operationalizing MLOps include:
- Build: Experimentation Tracking, Model Registry/Archive, Hyperparameter Tuning
- Test: Model Accuracy, Model Performance, Optimal Model Selection
- Model Deployment: CI/CD Versioning; Paas, Iaas, Container; Release Management
- Model Monitoring: Business KPIs, Model Accuracy and Errors, Alerts and Notifications
Wide Array of MLOps Accelerators
- Data Drift Detection
- Model Explainability
- Bias Detection
- Provenance Graph
- Model Testing Framework
- Specialized Auto ML Models
Platform & Product Partnership for Seamless Pluggability
- Seamless data ingestion from any kind of data sources
- 100% native to the world’s most leading cloud platforms
- Certified resources, equipped to handle out-of-the-box MLOps integrations
MLOps Managed Services
- 24/7 Model Management
- Production SLA Commitment
- Automated Incident Management
- On-demand Data Science Teams
Intuitive MLOps Graph
Immersive visual workflow graph that provides end-to-end model visibility and pipeline traceability.
Active Drift Detection
Persona Based Insights
Tredence, a leading data science and AI Engineering Company focused on solving the last mile problem in analytics, assists the world’s most prestigious brands in overcoming their most pressing machine learning operations challenges.
E2E MLOps Engagement Model
The ML Works platform commence with a POC to show value and then scale across the model universe.
E2E Governance Management
We deliver the right governance controls for delivering high-quality machine learning operations solutions and sustaining the solution in the longer term with optimized deployment strategies.
Autonomous ML Model Monitoring
Our ML Works platform helps organizations ensure their models in production are current, contextual and provide deeper visibility to data scientists for faster value realization.
Tredence aims to make ML adoption simple, pragmatic, and accessible through ML Works.
Focused Data Scientist
With ML Works, data scientists can shift their focus from managing machine learning models and mitigating risks to augmenting AI innovations.
Lean & Agile Adoption
Tredence reduces time-to-value by 30% vs. traditional consulting and technology service companies by leveraging our proprietary suite of accelerators.
- Flexible & Scalable
- Standardized debugging
- AI fairness & Explainability
- 360-degree view of model management