The second edition of the Machine Learning Developers Summit (MLDS 2020) has already started to create a significant excitement and buzz in the industry. The event, which is to be held on 22-23 Jan in Bengaluru and on 30-31 Jan in Hyderabad, is set to be one of the biggest gatherings of developers in India.
With over 1,500 attendees expected to attend the event, MLDS will see innovators from leading tech companies gathering together to discuss the latest software architecture of ML systems, producing and deploying the newest ML frameworks, addressing the challenges faced and more.
In this article, we are listing down ten such discussions and talks that you can look forward to attending at MLDS 2020. These talks are a combination of keynotes, panel discussions, tech talks and knowledge talks:
1. Ecosystem Intelligence, The Next Frontier In AI
When: 31 Jan, 11:30 am, Hyderabad
By: Shailesh Kumar, Chief Data Scientist, CoE AI/ML at Jio Faculty for Advanced Management Programme in Business Analytics at ISB
Like any other technology, AI has been growing in a bottom-up manner. Shailesh Kumar says that we have mastered the art of building accurate AI models, deploying them at scale, and continuously improving them as more data comes. We have done that for a variety of AI API’s now. He says that humans are now ready to build the next-generation products that can utilize these AI API’s like an ecosystem and deliver products that will be vastly different from what we think of products today. In this talk, Shailesh Kumar will explore what such products look like and how our AI and product thinking have to evolve to build such products.
2. Humans Not Machines Will Build The Future
When: 22 Jan, 12.10 pm, Bangalore
By: Vivek Kumar, Managing Director at Springboard India
Vivek Kumar says that since we live in the age of machine learning algorithms setting to help humankind with Siri and Alexa, made a quantum leap. In such a world, do you need humanness? This talk will touch upon humanness in the age of AI-ML and how it impacts careers in these future technologies. Springboard is a company built on humanness, and this talk will throw light on how to become a better AI engineer while also helping the community of aspirants.
3. Deep Learning With TensorFlow
When: 23 Jan, 12.10 pm, Bangalore
By: Mohan Kumar Silaparasetty, Co-Founder, CEO at Trendwise Analytics
4. Building An AI-First Organization
When: 22 Jan, 2.40 pm, Bangalore | 30 Jan, 10 am, Hyderabad
By: Sundara Ramalingam N Head of Deep Learning practice at NVIDIA India
This talk will focus on how to create a successful recipe for building AI into the existing traditional workflows of an organization. Machine learning, deep learning and data sciences have evolved extremely fast in the recent past, and practitioners find it challenging to keep in pace with the technology. The talk will cover the latest advancements in AI technology from both infrastructure and software perspective and recommends the best practices learnt from multiple domains for setting up a successful AI practice.
5. Deep Learning Using Convolution Neural Network For Classification Of Images
When: 30 Jan, 11:30 am, Hyderabad
By: Mohammad Shaheer Zaman Senior software engineer at AMD
Machine learning has found its application in various practical domains. In this presentation, Mohammed will look at how deep learning can be used to classify images. Specifically, he will look at convolution neural networks which are a subset of deep learning models to solve a classic problem of computer vision which is to differentiate between two sets of images. Finally, he will compare the performance of machines as opposed to those of humans in recognizing images.
6. Anthropomorphism in Conversational User Interfaces
When: 23 Jan, 4:10 pm, Bangalore
By: Mathangi Sri Head of Data Science at PhonePe
Machines may have “artificial” intelligence, but they want you to believe that they are humans. Can they behave like humans? Does it make sense, or is it better to go all out and declare the truth to the users?. If we want them to take human characteristics, what can be done?. What is the state-of-the-art in anthropomorphic systems, and how can machine learning and NLP help? In this talk, Mathangi attempts to break into the surface of this problem.
7. Advances in Deep Recommender Systems & Their Impact on Top Lines
When: 22 Jan, 4:50 pm, Bangalore
By: Bhavik Gandhi Director, Data Sciences and Analytics at Shaadi.com
8. Are We Ready For AI DevOps?
When: 23 Jan, 1:50 pm, Bangalore
By: Sunil Kumar Vuppala, Director – Data Science at Ericsson Global AI Accelerator
This talk discusses the basic W & H questions (what, why, where, when and how) of AI DevOps and the challenges in adoption of deploying the ML/DL models. This covers a couple of reference frameworks in this journey such as MLFlow, SageMaker and Azure ML service. Sunil also includes a couple of use cases from the Telecom industry to illustrate the need for faster diagnosis and edge deployment. He highlights various trade-offs one needs to handle in their ML life cycle, including the PLASTER framework. The key takeaways for the audience will include essential factors to be considered while designing and deploying the DL based solutions, take steps towards AI DevOps and corresponding research areas to work on.
9. Boosting Memory-Based Collaborative Filtering Using Content-Metadata
When: 23 Jan, 4.50 pm, Bangalore
By: Anish Agarwal Director – Data & Analytics at RBS India
Recommendation systems are widely used in conjunction with many popular personalized services, which enables people to find not only content items they are currently interested in, but also those in which they might become involved in future. Many recommendation systems employ the memory-based collaborative filtering (CF) method, which has been generally accepted as one of the consensus approaches. Despite the usefulness of the CF method for a strong recommendation, several limitations remain, such as sparsity and cold-start problems that degrade the performance of CF systems in practice. In this talk, Anish will talk about how to overcome these limitations, a suitable content-metadata-based approach that effectively uses content-metadata.
10. AI in Manufacturing Intelligence
When: 31 Jan, 09:15 am
By: Venugopal Jarugumalli Principal Solutions Architect at ZF Group
Artificial intelligence technology is now making its way into manufacturing, and the machine-learning technology and pattern-recognition software at its core could hold the key to transforming factories of the near future. AI will perform manufacturing, quality control, shorten design time, and reduce materials waste, improve production reuse, perform predictive maintenance, and more.