MLOps

The Need for an Effective MLOps Platform

Get a Quote
Machine Learning (ML) is the most commonly implemented form of the growing Artificial Intelligence (AI) market. In 2020 the ML market was estimated at $1.41B and is projected to reach $8.81B by 2025. The areas where ML models are being applied are constantly growing. On average, companies leading in the application of AI to their business are realizing a 4.3% ROI for their projects.
However, the risks are high. In order to realize the benefits of data-driven insights from ML initiatives, multiple studies have shown that companies need to maintain focus on clear business objectives as well as have mature processes, talent and platforms in place. Estimates of AI project failure rate vary but most studies put it well above 50%. Our team focuses on providing solutions that streamline the work in each major operations phase in order to realize the anticipated ROI:
  1. Data Preparation
  2. Experimentation
  3. Model Training
  4. Deployment

The importance of the MLOps data layer

ServiceLaunch’s MLOps practice ensures that our customers’ Machine Learning (ML) platforms are optimally configured and ready for use by data scientists in order to accelerate data science projects. Data scientists often spend the bulk of their time dealing with data preparation and infrastructure tasks unrelated to actual ML activities. Tool selection, standardization and process automation are essential to maximizing ML project efficiency. The ServiceLaunch MLOps practice team has Certified Kubernetes Administrators (CKAs) that specialize in design, deployment and operation of cloud-native ML platforms using best-of-breed tools and workflow automation to enable a streamlined flow of ML project activities for the enterprise.

Delivering a reliable MLOps platform

ServiceLaunch enables enterprise customers to realize the business impact of a mature data science platform and methodology. Customers can overcome specific challenges and realize a number of benefits from engaging our team such as:

  • Acceleration of data science initiatives;
  • Efficiency gains in management of resources consumed by data science projects;
  • Easier collaboration amongst data scientists;
  • Rapid sharing of data, results and insights amongst data scientists and business consumers;
  • Secure data science projects appropriately; and, 
  • Properly protect data science project resources and output from loss.

Our portfolio of services in the MLOps space

  • Requirements discovery and analysis
  • Tool selection and evaluation
  • Platform design and deployment
  • Automation
  • Tool integration

Do any of these scenarios apply?

  1. Are your data scientists competing for limited resources (GPUs)?
  2. Has your data science team lost data?
  3. Is your data science team having difficulty collaborating?
  4. Are your data scientists spending more time maintaining infrastructure than doing data science work?
  5. Are you struggling with regular standardized ML model deployment?

Our Technology Expertise

Infrastructure & Orchestration
Kubernetes / Kubernetes Performance Optimization Rancher k3s HPE Ezmeral Container Platform and Data Fabric (MapR)
AWS Azure GCP
Docker Istio Traefik
MetalLB Calico MinIO
Various RDBMS (MySQL, PostgreSQL, MS SQL, etc.) ELK Stack Prometheus
Grafana
GitOps / DevOps
ArgoCD Kustomize Helm
Ansible Terraform Git
Azure DevOps
Artifact Management
Harbor Registry Nexus Repository Manager Artifactory
MLOps
DVC Kubeflow MLFlow
Jupyter Seldon Deploy
Request Free Consultation