If you're among the 60% of business and technology leaders who are looking to exploit the benefits of today’s accessible and affordable Machine Learning solutions, chances are you've been checking out the options provided by Google Cloud ML. If you now feel ready to take the next step and create a pilot production system, we have gathered 6 ways that can help you make the most of your first machine learning project.
Though you no longer need scarce expertise and deep pockets to use ML, ML projects can still be tricky. We believe most companies starting on their ML journey will see a higher ROI and see it sooner if they work with a partner to help guide them to the right outcome.
1. Skills transfer and coaching for your in-house team
There’s a learning curve to getting the most out of this new generation of ML tools and being able to apply them to deliver business benefits. Our approach to projects is to work with your in-house data scientists — who are the experts in your data — to help them understand what’s possible and to assist them in choosing and implementing the right tools and techniques. The aim is to help you complete your current ML project more quickly and put you in a better position to tackle future projects.
The impact of our mentoring and coaching is summed up by David Taylor, technical lead at Play Sports Network. “Our data scientists are extremely capable but they’re a very small team,” he says. “The training and mentoring Ancoris has provided around tools and best practices has been just as useful as the technical solutions we’ve developed. It’s saved us from making a few mistakes and taking longer to figure out how to use what’s available in GCP.”
2. Expert advice and best practice
At every stage of your ML journey, from proof-of-concept projects and pilots through to creating production systems and identifying new ways to apply ML to your business, we can help you understand your options and make the best choices. If you’re wondering which tools and technologies to use, which model is best for your use case, how you can balance accuracy with speed, or how to get your data in good shape for your ML application, our expert data scientists can help you dig into the issues and provide you with advice.
Our extensive experience with other Google products, especially Google BigQuery and the Google Cloud Platform, means we can also help you get great performance from your production systems while minimising costs, and advise you on the benefits of augmenting Google’s solutions with third-party tools from Google’s partner ecosystem.
3. Long-term customer success services
Our aim is always to ensure our customers achieve success over the long-term. A key step in any of our projects once they’ve gone live is to hand you over to our Customer Success Team, who will help you carry on moving forwards with your ML projects. Their goal is to make sure your solution doesn’t just work on a technical level when first implemented but that it stays “healthy” — in the face of emerging security threats, for example — and that you continue to “move forward” in applying technology to business goals and getting the maximum value out of your investment so far. And to add to the expertise of our own team, our status as a a Google Premier partner means we can draw on our close relationships with the product experts at Google itself if we run into something we've not seen before.
4. Product roadmap visibility
ML is a fast moving field, with new tools and options emerging all the time. Take Google's Cloud AutoML, a no-code solution which enables teams with limited ML expertise to create and train high-quality models through an easy-to-use graphical interface. Even three years ago, some of the problems Cloud AutoML can handle would have been very difficult to solve without a lot of technology and deep traditional data science expertise. We'll keep you up to date with the Google Cloud ML product roadmap, making sure you’re aware of new features and solutions that are on the horizon, spending time with you to help you explore what they mean for your own ML strategy.
5. Managed services
We don’t just help you build applications using Google tools but can also handle the daily operations of your purpose-built solutions and platforms with a Managed Service. A Managed Service from Ancoris is like gaining the capability of a specialised development, operations and support team — with certified professionals, knowledge of the Google stack and agile methodologies. The dedicated Managed Service built around your application can include user training and change management to maximise adoption, as well as ongoing updates, repairs and enhancements to the solution.
6. Business Transformation through ML
Ancoris is at heart a services and solutions company, with referenceable case studies on project and application build work that we’ve done with our clients. We’re an application developer on Google Cloud, with extensive experience in business transformation projects. We can help you re-imagine your processes to improve business performance and customer engagement by integrating ML into your business operations.
Working with our data analytics and AI team
Our Data, Analytics and AI practice brings together a highly committed team of experienced data scientists, mathematicians and engineers. We operate at the edge of modern data warehousing, machine learning and AI, regularly participating in Google Cloud alpha programs to trial new products and features and to future-proof our client solutions.
We have support from an in-house, award winning application development practice to deliver embedded analytics incorporating beautifully designed UIs.
If you'd like to find out more about how we can help you build your own modern data and analytics platform, why not take a look at some of our customer stories or browse our resources. Needless to say, please get in touch with our team if you'd like more practical support and guidance.
