Today, Machine Learning (ML) is enabling companies solve high-impact business challenges and bring in innovative features to products and services, which means data scientists, data analysts, and engineers trained on ML are becoming as integral to a company as cutting-edge technology and tools.
As the scope and usage of ML becomes ever encompassing, it is becoming the most in-demand skill. With data scientists, data engineers, and data analysts continuing to lead core ML initiatives, apply ML and analytics approaches to solve critical business problems, and turn raw data into insights, IT engineers too are expected to play a contributory role in the company’s AI/ ML projects.
Given this context, it is interesting to see that technology providers, like Google, are designing products and solutions to enable everyone – from data scientists, ML engineers, and developers to leverage ML and for a wide range of use cases. For instance, the Google Cloud ML Engine enables developers and data scientists to build and bring superior ML models to production. Cloud ML Engine offers training and prediction services, which can be used together or individually to solve problems ranging from identifying clouds in satellite images, ensuring food safety, and responding to customer emails at supersonic speed.
In fact, the company has also been one of the strongest proponents of enabling ‘everyone to be able to use ML for their own needs,’ and has channelised efforts to design products and features that align with this philosophy. So, when it was observed that data analysts haven’t been able to leverage ML well enough to better understand the data they are generating, Google devised a solution to address this challenge through BigQuery ML – a capability inside BigQuery, Google’s highly scalable, cost-effective, and fully managed cloud data warehouse for analytics, with built-in ML.
Today, technology providers are not only keeping the needs of the end-users, customers, and tech community at the core of their product design and development, but also coming forward to share their expertise with the larger tech community and master the necessary skills.
TechSparks’18 presents one such opportunity where you can learn an expert from Google.
At the masterclass on NextGen of Google Cloud: Data Processing & ML with SQL, you will learn
- How data processing has changed from an on-premise, batch-focused Hadoop-based analytical system, to a streaming-oriented analytics pipeline hosted in a cloud environment utilising Apache Beam.
- How you can apply machine learning techniques with SQL language extensions even without having a strong data science background.
This is a unique opportunity that data analysts, engineers and AI/ML enthusiasts should not miss, because you get to learn from a seasoned engineer who has mentored multiple startups and enterprises on their data processing and ML journey.
The workshop is being led by Rishi Singhal, a Customer Engineer for Google Cloud Platform (GCP). Rishi works with various startups and enterprises to solve the problems and challenges they are currently facing w.r.t data processing and generating insights from their data. He also helps them in visualising their NEXT journey with the power of Google Cloud Platform. Rishi has more than 14 years of IT experience with expertise in application development and data processing, and has done startup consulting in Transport, Healthcare & Stock Trading.
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