Machine Learning Applied @ The Speed Of Cloud
It is well evident that we are in the age of big data dominance, exponentially decreasing cost of compute and storage, coupled with the speed of cloud. Many organizations have harnessed this power and are seeking deeper, faster and more accurate understanding of complex data.
Companies are integrating a variety of data from multiple data sources to “fill” their data lake and point the machine to it to detect patterns. These machines apply techniques or ‘models’ to learn from data. These learnings are reapplied and reused in practical ways to solve diverse use cases such as –
- Recommendations – Personalized marketing
- Payment Frauds detection
- Recognition – whether it is for safety reason, e.g. “person of interest” or for identifying common landmarks in social posts & images
- Genomics – Either to predict specific disease pathways or to determine which treatment will be more effective
- Route detection and Self driving cars
- Natural Language Processing – understanding variances in dialect, figures of speech, mannerism, emoticons across omni channel interactions such as – chat, voice, text etc. and providing a meaningful response while continuously learning from interactions.
No surprise here – More and more organizations are seeing the benefits and ML use cases are rapidly evolving. However, at the same time, many organizations struggle to pick up pace in this field. Level of readiness to below questions can help determine how successful your approach and mission to ML is going to be
- Have you carefully identified and prioritized your target ML Business use cases? Is there a clear statement of the problem space or hypothesis to be explored?
- Can you ascertain the value, complexity and inter-dependencies for selected use cases before you start investing significant resources – especially when it is not an experimental endeavor?
- Are Subject matter experts (business context, data science, Technology & Cloud) working together? Or is it a black box exercise?
- Is the algorithm or “model” being applied appropriate to the problem at hand?
- How much data is enough to learn?
- Do you know how the results should be applied? Have you accounted for a potential bias?
- Can you put the learnings into action in a sustainable and human trustable manner?
In Nutshell, successful ML implementations require the right blend of Science, Art & Approach. At USEReady, we navigate you through the intricacies of ML Journey and accelerate your adoption and deployment, incrementally and continuously.
Our ML Services include –
1. Strategy, Assessments, Roadmaps & Coaching
You have heard of the ‘Machine Learning’ hype and/or have decided to harness ML capabilities to seek deep insights but looking for credible answers to one or more of below questions.
- Can I benefit from machine Learning?
- What changes and is our organization ready for change?
- How does my org. currently compare to my peer orgs.?
- Where do we need to be?
- Which technology to choose & why? – Tensor flow, H2o.ai, PY torch, AWS Sagemaker, Azure ML, Google GCP, other etc.?
- How can I optimize and leverage cloud for Machine Learning? if so – How?
- What should be the budget?
- What’s the business value and ROI?
- What teams should I engage & when?
- Do teams know what to do, how & when?
- How do I ensure security & compliance?
At USEReady, we work alongside and guide you using an accelerated and proven methodology for :
- ML Readiness or Gap assessments
- Identifying the most valuable use cases for your business and/or function
- Define Organization or Department level Machine learning strategy and Iterative/ prioritized roadmap
- Transformation and change management Planning, including but not limited to Coaching, budgeting, resources, skills, timeline, technology & Processes
2. Solution Architecture, Engineering & Deployment
Your use cases have been defined and you want to accelerate the architecture and engineering efforts. At USEReady, we
- Define and apply ‘fit to purpose & cloud optimized architectures” to fulfill your needs for migration of your current bigdata analytics and ML ecosystem and for modernizing your Machine Learning capabilities on Cloud.
- We design a well architected solution using optimal platforms such as H2o.ai, Pytorch, Tensorflo, etc and help deploy on cloud – e.g. AWS Sagemakers and/or cloud virtual infrastructure.
- Our proven playbooks and automation libraries inherently account for right level of maturity desired by you across operations, Security, compliance, efficiency and reliability.
- Our execution approach is nimble and agile. We accelerate release of features and capabilities in incremental and continuous manner.
3. Pilots & Prototypes
Whether you are evaluating and comparing different ML platforms, building a business case, or are elevating your learnings on machine learning, we offer “Pilot as a Service”. We have specific approach and automation libraries to accelerate deployment of Pilots & POCs.