If You Already Have a Data Science Programme
Big data and machine learning projects are, unsurprisingly, a huge growth area in many organisations. The benefits on offer, in terms of increased margins, lower costs, greater reliability and the opening of new opportunities cannot be ignored. Your business may have grown from judgement-based to evidence-based decisions and quite recently taken the step into leading through predictive models. The next stage of the journey is automated machine learning, expediting the journey from business objective to active solution and providing a platform to scale. Automating some or all of your machine learning pipeline will help you to concentrate your data science resources on their highest-value tasks in a virtuous circle of competitive advantage.
Forbes declared Data Scientist as the top job of 2018, attracting top salaries and high job satisfaction. The competition for skilled data scientists is fierce. According to KDNuggets, two thirds of data scientists will stick it out with an employer for no more than four years, and four in every ten are gone within 24 months. Keeping in mind that industry experts suggest a successful predictive modelling project may take 12 months to reach production, the resource pool of data scientists is a real impediment to growing your big data programme. And what happens to all of that working knowledge they have leave? It exits your organisation and is carried with them to their next job. Automated machine learning protects your intellectual property and enables growth by redirecting the role of data scientists away from the repetitive and towards high-value activities around understanding the problem domain and the data itself and, ultimately, deliver more of your projects into production and more value to your business.
Robotica Machine Learning will work with you to deliver automation across your data science pipeline so that you can work through your backlog rapidly, efficiently and transparently. Robotising your processes frees up the precious data science resource to concentrate on “squeezing the pips”, overseeing and stretching the value from each stage.
Robotica Machine Learning with DataRobot and Microsoft Azure ML can automate across the entire ML life cycle:
- Gathering data from multiple sources
- Cleansing data to tidy missing values and correct dirty data
- Enrichment of fields to draw out supplementary information, such as credit score from a person’s identity
- Feature engineering to derive more pertinent information from fields, such as age or day of the week from a date
- Selecting the right algorithms from the hundreds available and applying it appropriately with sensible hyper-parameters
- Row partitioning to ensure an appropriate spread of outlying or unusual values and to prevent over-fitting
- Training models to find and learn from patterns hidden with the data
- Tuning models to refine their efficacy to the problem domain
- Ensembling discrete but related modelling steps into effective workflows
- Head-to-head model competitions to determine the balance of accuracy, spread and performance
- Detailed explanations of the predictive models for regulators and business stakeholders
- Comprehensible insights into the reasons behind predictions
- Bespoke deployment strategies to benefit the application of the predictive models
- Application and API integration
- Model monitoring and management
We can help you to…
…Scale your data science programme
Increase the effective capacity of your existing data science team by investing in a platform that delivers rapid automation across the ML life cycle, liberating the data scientists and engineers to concentrate on core, high value activities.
…Provide actionable insights into your data
Transparent models and Robotica Machine Learning’s behaviour-driven, automated analysis pipeline explains the how and why of your predictive models
Shorten the pre-live life cycle from months or years to days with Robotica Machine Learning automation.
…Empower more people
Bring the benefits of data science and predictive modelling out of the lab and equip more key stakeholders with the strengths to apply automated machine learning to their domains and enable them to deliver actions on their insights.
…Harden your data science processes
Create robust, repeatable pipelines, operating 24/7 with no distractions or human error.
…Learn the latest algorithms and what works for your business objectives and data
The automated machine learning platform, integrated by Robotica Machine Learning, understands and can apply hundreds more predictive modelling algorithms than most of the world’s top data science teams can imagine. Further, the transparency and insights into modelling and scoring, accompanied by detailed explanations and customer-facing data scientists from DataRobot, supports expert and novice team members to constantly improve upon their abilities, delivering extra value to your data science programme.
…Protect your IP
Mitigate the consequence of brain drain by permitting the DataRobot platform to crunch through modelling approaches and deliver insights and predictive models. Secure your understand and your intellectual property in Gherkin-based automated “living documentation” from Robotica Machine Learning.
…Invest in a platform
Resource spent on automation continues to pay back each time it is used. And robots never sleep - run your automation pipeline around the clock to maximise the value on tap.
…Stay on top of your objectives
Over time, data will drift out of sync with the models and remodelling will be necessary. Depending on the problem domain, drift may take months before new models are beneficial. Or the game may move on in days. Either way, integrated, automated machine learning gives you the platform and the power to remodel and redeploy as often as you require, maintaining your competitive advantage.
Automated machine learning is a trusted means to expand your data science programme, work through your backlog and give you greater confidence in the project success and modelling accuracy. Speak to Robotica Machine Learning to set up a proof of concept.
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